Random Dispersal in Theoretical Populations在随机种群扩散

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自制GRE模考题1

1. Geologists suggest that as the most productive oil reservoirs begin to dry up, the expensive cost and high risk of drilling in the marginal area become less _____ and more acceptable.A. onerousB. efficaciousC. auspiciousD. benignE. natural2. Evidence suggest that populations of migratory birds in both the New and Old Worldshave(i)_____, a (ii)_____ that carries the potential for significant ecological damage far beyond the shrinking numbers of the birds themselves.A. scattered D. dispersalB. stabilized E. declineC. plummeted F. displacement3. Even though company’s CEO professes to be (i)_____ and urges the deal forward, its investors are unlikely to be so (ii)_____ about its prospect. Many were uncomfortable at the way the corporation was forced to spend much of last year expansion, and this new venture is potentially even more risky.A. apprehensive D. indifferentB. apathetic E. sanguineC. confident F. anxious4. Among the most popular Currier&levs lithographic prints in nineteenth-century America were bird’s-eye views of great cities such as New York, Chicago, and San Francisco. These were edited into books, and the cities’ (i)_____ meant that new views were (ii)_____ for successive editions.A. tremendous productivity D. hard to obtainB. extraneous growth E. rendered superfluousC. unparalleled activity F. regularly required5. Dirac may have (i)_____ the company of other theoretical physicals, but he often (ii)_____, rarely (iii)_____ group discussions and almost never collaborating with others in his own work.A. appreciated D. kept them at a distance G. cutting offB. analyzed E. welcomed their company H. contributing toC. spurned F. pondered their insights I. shrinking from6. Environmental models—mathematical representations designed to stimulate natural systems—are regularly used by litigants in legal disputes over environmental issues. Unfortunately, the(i)_____ scientific model is (ii)_____ in environmental tort litigation. Because of the adversarial nature of litigation, models are often used by one side to (iii)_____ empirical evidence presented by the other. And because modeling is a particularly technical field, the task of asserting a given model’s relevance and reliability may exceed the abilities of judge and juries.A. evidentiary value of D. avoided G. adduceB. uncertainty inherent in E. predictive H. obfuscateC. increasing reliance on F. exacerbated I. replicateFor years, the leading theory for what caused the Younger Dryas (a dramatic reversal, about 12,900 years ago, in a global warming trend) was a release of water from Glacial Lake Agassiz. The theory posited that this meltwater flooded into the North Atlantic, lowering the salinity and intensity of surface waters enough to prevent them from sinking. Ocean currents were changed in such a way that northward transport of heat in the ocean diminished, and the North Atlantic regions plunged back into near-glacial conditions. However, evidence has emerged that the Younger Dryas began long before freshwater flooded the North Atlantic. Additionally, the temperature changes induced by a shutdown in the North Atlantic heat conveyor system are too small to explain the Younger Dryas.7. The author of the passage implies which of the following about the release of water from glacial Lake Agassiz?A. The notion that the release occurred has been challenged by more recent findings.B. The release probably occurred much earlier than scientists have generally assumed.C. The release would not have been sufficient to cause any temperature change in the North Atlantic.D. The timing of the release is such that it probably did not trigger the onset of the Younger Dryas.E. The release was probably unrelated to the global warming trend that was taking place.8. The passage is primarily concerned withA. presenting evidence that undermines an explanationB. explaining the nature of a climatological phenomenonC. questioning the timing of a particular eventD. discussing a new explanation for a phenomenonE. suggesting revisions to a popular theoryIn 1995 the Galileo spacecraft captured data about Jupiters atmosphere—namely, the absence of most of the predicted atmospheric water—that challenged prevailing theories about Jupiters structure. The unexpectedness of this finding fits a larger pattern in which theories about planetary composition and dynamics have failed to predict the realities discovered through space exploration. Instead of normal planets whose composition could be predicted by theory, the planets populating our solar system are unique individuals whose chemical and tectonic identities were created through numerous contingent events. One implication of this is that although the universe undoubtedly holds other planetary systems, the duplication of the sequence that produced our solar system and the development of life on Earth is highly unlikely.Recently planetary scientists have suggested that the external preconditions for the development of Earth’s biosphere probably included four paramount contingenc ies. First, a climate conducive to life on Earth depends upon the extraordinarily narrow orbital parameters that define a continuously habitable zone where water can exist in a liquid state. If Earths orbit were only 5 percent smaller than it is, temperatures during the early stages of Earths history would have been high enough to vaporize the oceans. If the Earth-Sun distance were as little as 1 percent larger, runaway glaciation on Earth about 2 billion years ago would have caused the oceans to freeze and remain frozen to this day. Second, Jupiter’s enormous mass prevents most Sun-bound comets from penetrating the inner solar system. It has been estimated thatwithout this shield, Earth would have experienced bombardment by comet-sized impactors a thousand times more frequently than has actually been recorded during geological time. Even if Earth‘s surface were not actually sterilized by this bombardment, it is unlikely that any butthe most primitive life-forms could have survived. This suggests that only planetary systems containing both terrestrial planets like Earth and gas giants like Jupiter might be capable of sustaining complex life-forms.Third, the gravitational shield of the giant outer planets, while highly efficient, must occasionally fail to protect Earth. Paradoxically, while the temperatures required for liquid water exist only in the inner solar system, the key building blocks of life, including water itself, occur primarily beyond the asteroid belt. Thus the evolution of life has depended on a frequency of cometary impacts sufficient to convey water, as well as carbon and nitrogen, from these distant regions of the solar system to Earth while stopping short of an impact magnitude that would destroy the atmosphere and oceans.Finally, Earth’s unique and massive satellite, the Moon, plays a crucial role in stabilizing the obliquity of Earth’s rotational axis, this obliquity creates the terrestrial seasonality so important to the evolution and diversity of life. Mars, in contrast, has a wildly oscillating tilt and chaotic seasonality, while Venus, rotating slowly backward, has virtually no seasonality at all.9 The passage is primarily concerned withA. enumerating conditions that may have been necessary for a particular developmentB. outlining the conditions under which scientists may be able to predict certain eventsC. explaining how a particular finding affected scientists understanding of a phenomenonD. suggesting reasons why a particular outcome was more likely to occur than other possible outcomesE. assessing the relative significance of factors that contributed to a particular occurrence10. It can be inferred from the passage that the planetary scientists would be most likely to agree with which of the following statements concerning the development of complex life forms on Earth?A. It might have occurred earlier in Earths history if cometary impacts had been less frequent than they were.B. It could have occurred if Earths orbit were 1 percent larger than it is but not if Earths orbit were 5 percent smallerC. It probably follows a pattern common on other terrestrial planets that occupy planetary systems containing gas giants.D. Its dependence on the effect that Jupiters gravitational shield has on Earth was difficult to recognize prior to 1995.E. It has been contingent on conditions elsewhere in Earths solar system as well as on conditions on Earth itself.11. The author of the passage most likely mentions Mars oscillating tilt primarily in order toA. provide evidence for a proposition about the potential effects of cometary impactsB. emphasize the absence from our solar system of normal planetsC. contrast the rotational axis of Mars with that of VenusD. characterize the role of other planets in the solar system in earths developmentE. emphasize the importance of the Moon to the development of life on Earth12. The passage suggests each of the following about water on Earth EXCEPT:A. It was conveyed to Earth by comets.B. It appeared on Earth earlier than did carbon and nitrogen.C. Its existence in a liquid state is contingent on Earths orbital parameters.D. Much of it came from a part of the solar system where water cannot exist in a liquid state.E. It is unlikely that there would be much of it available to support life if the gravitational shield of the outer planets did not limit the frequency with which comets strike Earth.13. Williams finds the appearance of Whitman’s Leaves of Grass in 1855 nearly _____ given the immense disparity between Whitman’s earlier published works, which Williams finds dismal, and the consummate mastery of Leaves.A. oracularB. propheticC. inevitableD. inexplicableE. inauspiciousF. incomprehensible14. Carr insists that the so-called information society might be more accurately described as the interruption society: it _____ attention, the scarcest of all resources, and stuffs the mind trivia.A. guardsB. protectsC. divertsD. destroysE. annihilatesF. transcends15. Of all the singer’s works, this album is the most dependent on the musical conventions of her day; it was both the least _____ of her albums and the most commercially successful.A. personalB. well-knownC. experimentalD. innovativeE. acclaimedF. recognizable16. The environmental advocacy group pushed for a single, overarching wetlands management plan that would _____ the existing efforts of various entities, resulting in a focused blueprint for saving the area’s wetlands.A. combineB. meldC. undermineD. spearheadE. supportF. subvertCuts that need to be held closed in order to heal properly have generally been held closed with stitches. However, pressure to reduce medical costs is mounting. Consequently, it is likely that a newly developed adhesive will become the routine method of holding most types of cuts closed. The new adhesive holds most types of cuts closed as well as stitches do,and the cost of applying it is comparable to that of closing cuts with stitches. But whereas stitches must generally be removed by medical personnel after the cut has healed, the adhesive simply wears off. Thus, for any cut that the adhesive can hold closed as well as stitches can, it is more economical to use the adhesive.17.In the argument given, the two highlighted portions play which of the following roles?A. The first is a claim that the argument disputes; the second provides evidence against that disputed claim.B. The first is a claim that is used as supporting evidence for the main conclusion of the argument; the second is that main conclusion."C. The first is a claim that is used as supporting evidence for the main conclusion of the argument; the second is a conclusion that is drawn in order to support that main conclusion.D. The first introduces a practice about which the argument makes a prediction, the second is a conclusion based on that prediction."E. The first introduces a practice about which the argument makes a prediction; the second is an assessment that is used to support that prediction.The relevance of the literary personality—a writer’s distinctive attitudes, concerns, and artistic choices—to the analysis of a literary work is being scrutinized by various schools of contemporary criticism. Deconstructionists view the literary personality, like the writer’s biographical personality, as irrelevant. The proper focus of literary analysis, they argue, is a work’s intertextuality( interrelationship with other texts), subtexts (unspoken, concealed, or repressed discourses), and metatexts (self-referential aspects), not a perception of a writer’s verbal and aesthetic “fingerprints.” New historicists also devalue the literary personality, since, in their emphasis on a work’s historical contexts, they credit a writer with only those insights and ideas that were generally available when the writer lived. However, to readers interested in literary detective work--say scholars of classical( Greek and Roman) literature who wish to reconstruct damaged texts or deduce a work’s authorship—the literary personality sometimes provides vital clues.1. The passage is primarily concerned withA. discussing attitudes toward a particular focus for literary analysisB. describing the limitations of two contemporary approaches to literary analysisC. pointing out the similarities among seemingly contrasting approaches to literary analysisD. defending the resurgence of a particular focus for literary analysisE. defining a set of related terms employed in literary criticism2. It can be inferred from the passage that on the issue of how to analyze a literary work, the new historicists would most likely agree with the deconstructionists thatA. The writer’s insights and ideas should be understood in terms of the writer’s historical context.B. The writer’s literary personality has little or no relevance.C. The critic should primarily focus on intertextuality, subtexts and metatexts.3. In the context in which it appears, “credit writer with” most nearly meansA. trust a writer withB. applaud a writer forC. believe a writer createdD. presume a writer hadE. accept a writer for。

自制GRE模考题

自制GRE模考题1. Geologists suggest that as the most productive oil reservoirs begin to dry up, the expensive cost and high risk of drilling in the marginal area become less _____ and more acceptable.A. onerousB. efficaciousC. auspiciousD. benignE. natural2. Evidence suggest that populations of migratory birds in both the New and Old Worldshave(i)_____, a (ii)_____ that carries the potential for significant ecological damage far beyond the shrinking numbers of the birds themselves.A. scattered D. dispersalB. stabilized E. declineC. plummeted F. displacement3. Even though company’s CEO professes to be (i)_____ and urges the deal forward, its investors are unlikely to be so (ii)_____ about its prospect. Many were uncomfortable at the way the corporation was forcedto spend much of last year expansion, and this new venture is potentially even more risky.A. apprehensive D. indifferentB. apathetic E. sanguineC. confident F. anxious4. Among the most popular Currier&levs lithographic prints in nineteenth-century America were bird’s-eye views of great cities such as New York, Chicago, and San Francisco. These were edited into books, and the cities’(i)_____ meant that new views were (ii)_____ for successive editions.A. tremendous productivity D. hard to obtainB. extraneous growth E. rendered superfluousC. unparalleled activity F. regularly required5. Dirac may have (i)_____ the company of other theoretical physicals, but he often (ii)_____, rarely (iii)_____ group discussions and almost never collaborating with others in his own work.A. appreciated D. kept them at a distance G. cutting offB. analyzed E. welcomed their company H. contributing toC. spurned F. pondered their insights I. shrinking from6. Environmental models—mathematical representations designed to stimulate natural systems —are regularly used by litigants in legal disputes over environmental issues. Unfortunately, the(i)_____ scientific model is (ii)_____ in environmental tort litigation. Because of the adversarial nature of litigation, models are often used by one side to (iii)_____ empirical evidence presented by the other. And because modeling is a particularly technical field, the task of asserting a given model’s relevance and reliability may exceed the abilities of judge and juries.A. evidentiary value of D. avoided G. adduceB. uncertainty inherent in E. predictive H. obfuscateC. increasing reliance on F. exacerbated I. replicateFor years, the leading theory for what caused the Younger Dryas (a dramatic reversal, about12,900 years ago, in a global warming trend) was a release of water from Glacial Lake Agassiz. The theory posited that this meltwater flooded into the North Atlantic, lowering the salinity and intensity of surface waters enough to prevent them from sinking. Ocean currents were changed in such a way that northwardtransport of heat in the ocean diminished, and the North Atlantic regions plunged back into near-glacial conditions. However, evidence has emerged that the Younger Dryas began long before freshwater flooded the North Atlantic. Additionally, the temperature changes induced by a shutdown in the North Atlantic heat conveyor system are too small to explain the Younger Dryas.7. The author of the passage implies which of the following about the release of water from glacial Lake Agassiz?A. The notion that the release occurred has been challenged by more recent findings.B. The release probably occurred much earlier than scientists have generally assumed.C. The release would not have been sufficient to cause any temperature change in the North Atlantic.D. The timing of the release is such that it probably did not trigger the onset of the Younger Dryas.E. The release was probably unrelated to the global warming trend that was taking place.8. The passage is primarily concerned withA. presenting evidence that undermines an explanationB. explaining the nature of a climatological phenomenonC. questioning the timing of a particular eventD. discussing a new explanation for a phenomenonE. suggesting revisions to a popular theoryIn 1995 the Galileo spacecraft captured data about Jupiters atmosphere—namely, the absenceof most of the predicted atmospheric water—that challenged prevailing theories about Jupiters structure. The unexpectedness of this finding fits a larger pattern in which theories about planetary composition and dynamics have failed to predict the realities discovered through space exploration. Instead of normal planets whose composition could be predicted by theory, the planets populating our solar system are unique individuals whose chemical and tectonic identities were created through numerous contingent events. One implication of this is that although the universe undoubtedly holds other planetary systems, the duplication of the sequence that produced our solar system and the development of life on Earth is highlyunlikely.Recently planetary scientists have suggested that the external preconditions for the development of Earth’s biosphere probably included four paramount contingenc ies. First, a climate conducive to life on Earth depends upon the extraordinarily narrow orbital parameters that define a continuously habitable zone where water can exist in a liquid state. If Earths orbit were only 5 percent smaller than it is, temperatures during the early stages of Earths history would have been high enough to vaporize the oceans. If the Earth-Sun distance were as little as 1 percent larger, runaway glaciation on Earth about 2 billion years ago would have caused the oceans to freeze and remain frozen to this day. Second, Jupiter’s enormous mass prevents most Sun-bound comets from penetrating the inner solar system. It has been estimated that without this shield, Earth would have experienced bombardment by comet-sized impactors a thousand times more frequently than has actually been recorded during geological time. Even if Earth‘s surface were not actually sterilized by this bombardment, it is unlikelythat any but the most primitive life-forms could have survived. This suggests that only planetary systems containing both terrestrial planets like Earth and gas giants like Jupiter might be capable of sustaining complex life-forms.Third, the gravitational shield of the giant outer planets, while highly efficient, must occasionally fail to protect Earth. Paradoxically, while the temperatures required for liquid water exist only in the inner solar system, the key building blocks of life, including water itself, occur primarily beyond the asteroid belt. Thus the evolution of life has depended on a frequency of cometary impacts sufficient to convey water, as well as carbon and nitrogen, from these distant regions of the solar system to Earth while stopping short of an impact magnitude that would destroy the atmosphere and oceans.Finally, Earth’s unique and massive satellite, the Moon, plays a crucial role in stabilizing the obliquity of Earth’s rotational axis, this obliquity creates the terrestrial seasonality so important to the evolution and diversity of life. Mars, in contrast, has a wildly oscillating tilt andchaotic seasonality, while Venus, rotating slowly backward, has virtually no seasonality at all.9 The passage is primarily concerned withA. enumerating conditions that may have been necessary for a particular developmentB. outlining the conditions under which scientists may be able to predict certain eventsC. explaining how a particular finding affected scientists understanding of a phenomenonD. suggesting reasons why a particular outcome was more likely to occur than other possible outcomesE. assessing the relative significance of factors that contributed to a particular occurrence10. It can be inferred from the passage that the planetary scientists would be most likely to agree with which of the following statements concerning the development of complex life forms on Earth?A. It might have occurred earlier in Earths history if cometary impacts had been less frequent than they were.B. It could have occurred if Earths orbit were 1 percent larger than it is but not if Earths orbit were5 percent smallerC. It probably follows a pattern common on other terrestrial planets that occupy planetary systems containing gas giants.D. Its dependence on the effect that Jupiters gravitational shield has on Earth was difficult to recognize prior to 1995.E. It has been contingent on conditions elsewhere in Earths solar system as well as on conditions on Earth itself.11. The author of the passage most likely mentions Mars oscillating tilt primarily in order toA. provide evidence for a proposition about the potential effects of cometary impactsB. emphasize the absence from our solar system of normal planetsC. contrast the rotational axis of Mars with that of VenusD. characterize the role of other planets in the solar system in earths developmentE. emphasize the importance of the Moon to the development of life on Earth12. The passage suggests each of the following about water on Earth EXCEPT:A. It was conveyed to Earth by comets.B. It appeared on Earth earlier than did carbon and nitrogen.C. Its existence in a liquid state is contingent on Earths orbital parameters.D. Much of it came from a part of the solar system where water cannot exist in a liquid state.E. It is unlikely that there would be much of it available to support life if the gravitational shield of the outer planets did not limit the frequency with which comets strike Earth.13. Williams finds the appearance of Whitman’s Leaves of Grass in 1855 nearly _____ given the immense disparity between Whitman’s earlier published works, which Williams finds dismal, and the consummate mastery of Leaves.A. oracularB. propheticC. inevitableD. inexplicableE. inauspiciousF. incomprehensible14. Carr insists that the so-called information society might be more accurately described as the interruption society: it _____ attention, the scarcest of all resources, and stuffs the mind trivia.A. guardsB. protectsC. divertsD. destroysE. annihilatesF. transcends15. Of all the singer’s works, this album is the most dependent on the musical conventions of her day; it was both the least _____ of her albums and the most commercially successful.A. personalB. well-knownC. experimentalD. innovativeE. acclaimedF. recognizable16. The environmental advocacy group pushed for a single, overarching wetlands management plan that would _____ the existing efforts of various entities, resulting in a focused blueprint for saving the area’s wetlands.A. combineB. meldC. undermineD. spearheadE. supportF. subvertCuts that need to be held closed in order to heal properly have generally been held closed with stitches. However, pressure to reduce medical costs is mounting. Consequently, it is likely that a newly developed adhesive will become the routine method of holding most types of cuts closed.The new adhesive holds most types of cuts closed as well as stitches do,and the cost of applying it is comparable to that of closing cuts with stitches. But whereas stitches must generally be removed by medical personnel after the cut has healed, theadhesive simply wears off. Thus, for any cut that the adhesive can hold closed as well as stitches can, it is more economical to use the adhesive.17.In the argument given, the two highlighted portions play which of the following roles?A. The first is a claim that the argument disputes; the second provides evidence against that disputed claim.B. The first is a claim that is used as supporting evidence for the main conclusion of the argument; the second is that main conclusion."C. The first is a claim that is used as supporting evidence for the main conclusion of the argument; the second is a conclusion that is drawn in order to support that main conclusion.D. The first introduces a practice about which the argument makes a prediction, the second is a conclusion based on that prediction."E. The first introduces a practice about which the argument makes a prediction; the second is an assessment that is used to support that prediction. The relevance of the literary personality—a writer’s distinctive attitudes, concerns, and artistic choices—tothe analysis of a literary work is being scrutinized by various schools of contemporary criticism. Deconstructionists view the literary personality, like the writer’s biographical personality, as irrelevant. The proper focus of literary analysis, they argue, is a work’s intertextuality( interrelationship with other texts), subtexts (unspoken, concealed, or repressed discourses), and metatexts (self-referential aspects), not a perception of a writer’s verbal and aesthetic “fingerprints.”New historicists also devalue the literary personality, since, in their emphasis on a work’s historical contexts, they credit a writer with only those insights and ideas that were generally available when the writer lived. However, to readers interested in literary detective work--say scholars of classical( Greek and Roman) literature who wish to reconstruct damaged texts or deduce a work’s authorship—the literary personality sometimes provides vital clues.1. The passage is primarily concerned withA. discussing attitudes toward a particular focus for literary analysisB. describing the limitations of two contemporaryapproaches to literary analysisC. pointing out the similarities among seemingly contrasting approaches to literary analysisD. defending the resurgence of a particular focus for literary analysisE. defining a set of related terms employed in literary criticism2. It can be inferred from the passage that on the issue of how to analyze a literary work, the new historicists would most likely agree with the deconstructionists thatA. The writer’s insights and ideas should be understood in terms of the writer’s historical context.B. The writer’s literary personality has little or no relevance.C. The critic should primarily focus on intertextuality, subtexts and metatexts.3. In the context in which it appears, “credit writer with”most nearly meansA. trust a writer withB. applaud a writer forC. believe a writer createdD. presume a writer hadE. accept a writer for。

2.1-Introduction-to-randomized-algorithm

2
Generate Random Number?
Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin(痴心妄想).
John von Neumann
Generate Random Numbers
8
Lagged Fibonacci Generators
Similar to Fibonacci Sequence Increasingly popular Xn+1 = (Xn-l + Xn-k) mod m (l>k>0) l seeds are needed m usually a power of 2
We really don’t have truly random random number generators.
To generate psuedo-random sequence, let s∈X be a seed. This seed defines a foralli
0
yi g(xi ) foralli 0
Where, f : X→X and g: X→Y, X is a sufficiently large set and Y is the domain of pseudorandom values to be generated. yi is the pseudorandom sequence
Other generators have longer maximum periods. Bad choices of m result in very bad sequences

Maximum entropy modeling of species geographic distributions

Ecological Modelling 190(2006)231–259Maximum entropy modeling of species geographic distributionsSteven J.Phillips a ,∗,Robert P.Anderson b ,c ,Robert E.Schapire daAT&T Labs-Research,180Park Avenue,Florham Park,NJ 07932,USAbDepartment of Biology,City College of the City University of New York,J-526Marshak Science Building,Convent Avenue at 138th Street,New York,NY 10031,USAcDivision of Vertebrate Zoology (Mammalogy),American Museum of Natural History,Central Park West at 79th Street,New York,NY 10024,USAd Computer Science Department,Princeton University,35Olden Street,Princeton,NJ 08544,USAReceived 23February 2004;received in revised form 11March 2005;accepted 28March 2005Available online 14July 2005AbstractThe availability of detailed environmental data,together with inexpensive and powerful computers,has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions.For some species,detailed pres-ence/absence occurrence data are available,allowing the use of a variety of standard statistical techniques.However,absence data are not available for most species.In this paper,we introduce the use of the maximum entropy method (Maxent)for modeling species geographic distributions with presence-only data.Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation,and it has a number of aspects that make it well-suited for species distribution modeling.In order to investigate the efﬁcacy of the method,here we perform a continental-scale case study using two Neotropical mammals:a lowland species of sloth,Bradypus variegatus ,and a small montane murid rodent,Microryzomys minutus .We compared Maxent predictions with those of a commonly used presence-only modeling method,the Genetic Algorithm for Rule-Set Prediction (GARP).We made predictions on 10random subsets of the occurrence records for both species,and then used the remaining localities for testing.Both algorithms provided reasonable estimates of the species’range,far superior to the shaded outline maps available in ﬁeld guides.All models were signiﬁcantly better than random in both binomial tests of omission and receiver operating characteristic (ROC)analyses.The area under the ROC curve (AUC)was almost always higher for Maxent,indicating better discrimination of suitable versus unsuitable areas for the species.The Maxent modeling approach can be used in its present form for many applications with presence-only datasets,and merits further research and development.©2005Elsevier B.V .All rights reserved.Keywords:Maximum entropy;Distribution;Modeling;Niche;RangeCorresponding author.Tel.:+19733608704;fax:+19733608871.E-mail addresses:phillips@(S.J.Phillips),anderson@ (R.P.Anderson),schapire@ (R.E.Schapire).1.IntroductionPredictive modeling of species geographic distribu-tions based on the environmental conditions of sites of known occurrence constitutes an important tech-0304-3800/$–see front matter ©2005Elsevier B.V .All rights reserved.doi:10.1016/j.ecolmodel.2005.03.026232S.J.Phillips et al./Ecological Modelling190(2006)231–259nique in analytical biology,with applications in con-servation and reserve planning,ecology,evolution, epidemiology,invasive-species management and other ﬁelds(Corsi et al.,1999;Peterson and Shaw,2003; Peterson et al.,1999;Scott et al.,2002;Welk et al.,2002;Yom-Tov and Kadmon,1998).Sometimes both presence and absence occurrence data are avail-able for the development of models,in which case general-purpose statistical methods can be used(for an overview of the variety of techniques currently in use, see Corsi et al.,2000;Elith,2002;Guisan and Zim-merman,2000;Scott et al.,2002).However,while vast stores of presence-only data exist(particularly in nat-ural history museums and herbaria),absence data are rarely available,especially for poorly sampled tropical regions where modeling potentially has the most value for conservation(Anderson et al.,2002;Ponder et al., 2001;Sober´o n,1999).In addition,even when absence data are available,they may be of questionable value in many situations(Anderson et al.,2003).Modeling techniques that require only presence data are therefore extremely valuable(Graham et al.,2004).1.1.Niche-based models from presence-only dataWe are interested in devising a model of a species’environmental requirements from a set of occurrence localities,together with a set of environmental vari-ables that describe some of the factors that likely inﬂuence the suitability of the environment for the species(Brown and Lomolino,1998;Root,1988). Each occurrence locality is simply a latitude–longitude pair denoting a site where the species has been ob-served;such georeferenced occurrence records often derive from specimens in natural history museums and herbaria(Ponder et al.,2001;Stockwell and Peterson, 2002a).The environmental variables in GIS format all pertain to the same geographic area,the study area, which has been partitioned into a grid of pixels.The task of a modeling method is to predict environmen-tal suitability for the species as a function of the given environmental variables.A niche-based model represents an approximation of a species’ecological niche in the examined envi-ronmental dimensions.A species’fundamental niche consists of the set of all conditions that allow for its long-term survival,whereas its realized niche is that subset of the fundamental niche that it actually occupies (Hutchinson,1957).The species’realized niche may be smaller than its fundamental niche,due to human inﬂuence,biotic interactions(e.g.,inter-speciﬁc com-petition,predation),or geographic barriers that have hindered dispersal and colonization;such factors may prevent the species from inhabiting(or even encoun-tering)conditions encompassing its full ecological po-tential(Pulliam,2000;Anderson and Mart´ınez-Meyer, 2004).We assume here that occurrence localities are drawn from source habitat,rather than sink habitat, which may contain a given species without having the conditions necessary to maintain the population with-out immigration;this assumption is less realistic with highly vagile taxa(Pulliam,2000).By deﬁnition,then, environmental conditions at the occurrence localities constitute samples from the realized niche.A niche-based model thus represents an approximation of the species’realized niche,in the study area and environ-mental dimensions being considered.If the realized niche and fundamental niche do not fully coincide,we cannot hope for any modeling al-gorithm to characterize the species’full fundamental niche:the necessary information is simply not present in the occurrence localities.This problem is likely ex-acerbated when occurrence records are drawn from too small a geographic area.In a larger study region,how-ever,spatial variation exists in community composi-tion(and,hence,in the resulting biotic interactions) as well as in the environmental conditions available to the species.Therefore,given sufﬁcient sampling effort, modeling in a study region with a larger geographic extent is likely to increase the fraction of the funda-mental niche represented by the sample of occurrence localities(Peterson and Holt,2003),and is preferable. In practice,however,the departure between the fun-damental niche(a theoretical construct)and realized niche(which can be observed)of a species will remain unknown.Although a niche-based model describes suitabil-ity in ecological space,it is typically projected into geographic space,yielding a geographic area of pre-dicted presence for the species.Areas that satisfy the conditions of a species’fundamental niche represent its potential distribution,whereas the geographic ar-eas it actually inhabits constitute its realized distribu-tion.As mentioned above,the realized niche may be smaller than the fundamental niche(with respect to the environmental variables being modeled),in whichS.J.Phillips et al./Ecological Modelling190(2006)231–259233case the predicted distribution will be smaller than the full potential distribution.However,to the extent that the model accurately portrays the species’fundamen-tal niche,the projection of the model into geographic space will represent the species’potential distribution.Whether or not a model captures a species’full niche requirements,areas of predicted presence will typically be larger than the species’realized distribution.Due to many possible factors(such as geographic barriers to dispersal,biotic interactions,and human modiﬁca-tion of the environment),few species occupy all areas that satisfy their niche requirements.If required by the application at hand,the species’realized distribution can often be estimated from the modeled distribution through a series of steps that remove areas that the species is known or inferred not to inhabit.For ex-ample,suitable areas that have not been colonized due to contingent historical factors(e.g.,geographic barri-ers)can be excluded(Peterson et al.,1999;Anderson, 2003).Similarly,suitable areas not inhabited due to bi-otic interactions(e.g.,competition with closely related morphologically similar species)can be identiﬁed and removed from the prediction(Anderson et al.,2002). Finally,when a species’present-day distribution is de-sired,such as for conservation purposes,a current land-cover classiﬁcation derived from remotely sensed data can be used to exclude highly altered habitats(e.g.,re-moving deforested areas from the predicted distribution of an obligate-forest species;Anderson and Mart´ınez-Meyer,2004).There are implicit ecological assumptions in the set of environmental variables used for modeling, so selection of that set requires great care.Temporal correspondence should exist between occurrence localities and environmental variables;for example, a current land-cover classiﬁcation should not be used with occurrence localities that derive from museum records collected over many decades(Anderson and Mart´ınez-Meyer,2004).Secondly,the variables should affect the species’distribution at the relevant scale, determined by the geographic extent and grain of the modeling task(Pearson et al.,2004).For example, using the terminology of Mackey and Lindenmayer (2001),climatic variables such as temperature and pre-cipitation are appropriate at global and meso-scales; topographic variables(e.g.,elevation and aspect)likely affect species distributions at meso-and topo-scales; and land-cover variables like percent canopy cover inﬂuence species distributions at the micro-scale.The choice of variables to use for modeling also affects the degree to which the model generalizes to regions outside the study area or to different environmental conditions(e.g.,other time periods).This is important for applications such as invasive-species management (e.g.,Peterson and Robins,2003)and predicting the impact of climate change(e.g.,Thomas et al.,2004). Bioclimatic and soil-type variables measure availabil-ity of the fundamental primary resources of light,heat, water and mineral nutrients(Mackey and Linden-mayer,2001).Their impact,as measured in one study area or time frame,should generalize to other situa-tions.On the other hand,variables representing latitude or elevation will not generalize well;although they are correlated with variables that have biophysical impact on the species,those correlations vary over space and time.A number of other serious potential pitfalls may af-fect the accuracy of presence-only modeling;some of these also apply to presence–absence modeling.First, occurrence localities may be biased.For example,they are often highly correlated with the nearby presence of roads,rivers or other access conduits(Reddy and D´a valos,2003).The location of occurrence localities may also exhibit spatial auto-correlation(e.g.,if a re-searcher collects specimens from several nearby local-ities in a restricted area).Similarly,sampling intensity and sampling methods often vary widely across the study area(Anderson,2003).In addition,errors may exist in the occurrence localities,be it due to transcrip-tion errors,lack of sufﬁcient geographic detail(espe-cially in older records),or species misidentiﬁcation. Frequently,the number of occurrence localities may be too low to estimate the parameters of the model re-liably(Stockwell and Peterson,2002b).Similarly,the set of available environmental variables may not be sufﬁcient to describe all the parameters of the species’fundamental niche that are relevant to its distribution at the grain of the modeling task.Finally,errors may be present in the variables,perhaps due to errors in data manipulation,or due to inaccuracies in the climatic models used to generate climatic variables,or inter-polation of lower-resolution data.In sum,determining and possibly mitigating the effects of these factors rep-resent worthy topics of research for all presence-only modeling techniques.With these caveats,we proceed to introduce a modeling approach that may prove use-234S.J.Phillips et al./Ecological Modelling190(2006)231–259ful whenever the above concerns are adequately ad-dressed.1.2.MaxentMaxent is a general-purpose method for making predictions or inferences from incomplete information. Its origins lie in statistical mechanics(Jaynes,1957), and it remains an active area of research with an Annual Conference,Maximum Entropy and Bayesian Meth-ods,that explores applications in diverse areas such as astronomy,portfolio optimization,image recon-struction,statistical physics and signal processing.We introduce it here as a general approach for presence-only modeling of species distributions,suitable for all existing applications involving presence-only datasets. The idea of Maxent is to estimate a target probability distribution byﬁnding the probability distribution of maximum entropy(i.e.,that is most spread out,or closest to uniform),subject to a set of constraints that represent our incomplete information about the target distribution.The information available about the target distribution often presents itself as a set of real-valued variables,called“features”,and the constraints are that the expected value of each feature should match its em-pirical average(average value for a set of sample points taken from the target distribution).When Maxent is ap-plied to presence-only species distribution modeling, the pixels of the study area make up the space on which the Maxent probability distribution is deﬁned,pixels with known species occurrence records constitute the sample points,and the features are climatic variables, elevation,soil category,vegetation type or other environmental variables,and functions thereof.Maxent offers many advantages,and a few draw-backs;a comparison with other modeling methods will be made in Section2.1.4after the Maxent approach is described in detail.The advantages include the fol-lowing:(1)It requires only presence data,together with environmental information for the whole study area.(2)It can utilize both continuous and categorical data,and can incorporate interactions between different variables.(3)Efﬁcient deterministic algorithms have been developed that are guaranteed to converge to the optimal(maximum entropy)probability distribution.(4)The Maxent probability distribution has a concise mathematical deﬁnition,and is therefore amenable to analysis.For example,as with generalized linear and generalized additive models(GLM and GAM),in the absence of interactions between variables,additivity of the model makes it possible to interpret how each environmental variable relates to suitability(Dud´ık et al.,2004;Phillips et al.,2004).(5)Over-ﬁtting can be avoided by using 1-regularization(Section2.1.2).(6) Because dependence of the Maxent probability distri-bution on the distribution of occurrence localities is ex-plicit,there is the potential(in future work)to address the issue of sampling bias formally,as in Zadrozny (2004).(7)The output is continuous,allowingﬁne dis-tinctions to be made between the modeled suitability of different areas.If binary predictions are desired,this allows greatﬂexibility in the choice of threshold.If the application is conservation planning,theﬁne distinc-tions in predicted relative environmental suitability can be valuable to reserve planning algorithms.(8)Maxent could also be applied to species presence/absence data by using a conditional model(as in Berger et al.,1996), as opposed to the unconditional model used here.(9) Maxent is a generative approach,rather than discrim-inative,which can be an inherent advantage when the amount of training data is limited(see Section2.1.4).(10)Maximum entropy modeling is an active area of re-search in statistics and machine learning,and progress in theﬁeld as a whole can be readily applied here.(11) As a general-purpose andﬂexible statistical method, we expect that it can be used for all the applications outlined in Section1above,and at all scales.Some drawbacks of the method are:(1)It is not as mature a statistical method as GLM or GAM,so there are fewer guidelines for its use in general,and fewer methods for estimating the amount of error in a predic-tion.Our use of an“unconditional”model(cf.advan-tage8)is rare in machine learning.(2)The amount of regularization(see Section2.1.2)requires further study (e.g.,see Phillips et al.,2004),as does its effectiveness in avoiding over-ﬁtting compared with other variable-selection methods(for alternatives,see Guisan et al., 2002).(3)It uses an exponential model for probabil-ities,which is not inherently bounded above and can give very large predicted values for environmental con-ditions outside the range present in the study area.Extra care is therefore needed when extrapolating to another study area or to future or past climatic conditions(for example,feature values outside the range of values in the study area should be“clamped”,or reset to the ap-propriate upper or lower bound).(4)Special-purposeS.J.Phillips et al./Ecological Modelling190(2006)231–259235software is required,as Maxent is not available in stan-dard statistical packages.1.3.Existing approaches for presence-only modelingMany methods have been used for presence-only modeling of species distributions,and we only attempt here to give a broad overview of existing methods. Some methods use only presences to derive a model. BIOCLIM(Busby,1986;Nix,1986)predicts suitable conditions in a“bioclimatic envelope”,consisting of a rectilinear region in environmental space represent-ing the range(or some percentage thereof)of observed presence values in each environmental dimension.Sim-ilarly,DOMAIN(Carpenter et al.,1993)uses a similar-ity metric,where a predicted suitability index is given by computing the minimum distance in environmental space to any presence record.Other techniques use presence and background data.General-purpose statistical methods such as generalized linear models(GLMs)and generalized additive models(GAMs)are commonly used for modeling with presence–absence datasets.Recently, they have been applied to presence-only situations by taking a random sample of pixels from the study area, known as“background pixels”or“pseudo-absences”, and using them in place of absences during model-ing(Ferrier and Watson,1996;Ferrier et al.,2002).A sample of the background pixels can be chosen purely at random(sometimes excluding sites with presence records,Graham et al.,2004),or from sites where sampling is known to have occurred or from a model of such sites(Zaniewski et al.,2002;Engler et al.,2004).Similarly,a Bayesian approach(Aspinall, 1992)proposed modeling presence versus a random sample.The Genetic Algorithm for Rule-Set Predic-tion(Stockwell and Noble,1992;Stockwell and Peters, 1999)uses an artiﬁcial-intelligence framework called genetic algorithms.It produces a set of positive and negative rules that together give a binary prediction; rules are favored in the algorithm according to their signiﬁcance(compared with random prediction)based on a sample of background pixels and presence pixels. Environmental-Niche Factor Analysis(ENFA,Hirzel et al.,2002)uses presence localities together with environmental data for the entire study area,without requiring a sample of the background to be treated like absences.It is similar to principal components analysis, involving a linear transformation of the environmental space into orthogonal“marginality”and“specializa-tion”factors.Environmental suitability is then modeled as a Manhattan distance in the transformed space.As aﬁrst step in the evaluation of Maxent,we chose to compare it with GARP,as the latter has recently seen extensive use in presence-only studies(Anderson, 2003;Joseph and Stockwell,2002;Peterson and Kluza, 2003;Peterson and Robins,2003;Peterson and Shaw, 2003and references therein).While further stud-ies are needed comparing Maxent with other widely used methods that have been applied to presence-only datasets,such studies are beyond the scope of this pa-per.2.Methods2.1.Maxent details2.1.1.The principleWhen approximating an unknown probability dis-tribution,the question arises,what is the best approx-imation?E.T.Jaynes gave a general answer to this question:the best approach is to ensure that the ap-proximation satisﬁes any constraints on the unknown distribution that we are aware of,and that subject to those constraints,the distribution should have max-imum entropy(Jaynes,1957).This is known as the maximum-entropy principle.For our purposes,the un-known probability distribution,which we denoteπ,is over aﬁnite set X,(which we will later interpret as the set of pixels in the study area).We refer to the individ-ual elements of X as points.The distributionπassigns a non-negative probabilityπ(x)to each point x,and these probabilities sum to1.Our approximation ofπis also a probability distribution,and we denote itˆπ.The entropy ofˆπis deﬁned asH(ˆπ)=−x∈Xˆπ(x)lnˆπ(x)where ln is the natural logarithm.The entropy is non-negative and is at most the natural log of the number of elements in X.Entropy is a fundamental concept in information theory:in the paper that originated that ﬁeld,Shannon(1948)described entropy as“a measure236S.J.Phillips et al./Ecological Modelling 190(2006)231–259of how much ‘choice’is involved in the selection of an event”.Thus a distribution with higher entropy involves more choices (i.e.,it is less constrained).Therefore,the maximum entropy principle can be interpreted as saying that no unfounded constraints should be placed on ˆπ,or alternatively,The fact that a certain probability distribution maxi-mizes entropy subject to certain constraints represent-ing our incomplete information,is the fundamental property which justiﬁes use of that distribution for inference;it agrees with everything that is known,but carefully avoids assuming anything that is not known (Jaynes,1990).2.1.2.A machine learning perspectiveThe maximum entropy principle has seen recent interest in the machine learning community,with a major contribution being the development of efﬁ-cient algorithms for ﬁnding the Maxent distribution (see Berger et al.,1996for an accessible introduction and Ratnaparkhi,1998for a variety of applications and a favorable comparison with decision trees).The ap-proach consists of formalizing the constraints on the unknown probability distribution πin the following way.We assume that we have a set of known real-valued functions f 1,...,f n on X ,known as “features”(which for our application will be environmental vari-ables or functions thereof).We assume further that the information we know about πis characterized by the expectations (averages)of the features under π.Here,each feature f j assigns a real value f j (x )to each point x in X .The expectation of the feature f j under πis deﬁned asx ∈X π(x )f j (x )and denoted by π[f j ].In general,for any probability distribution p and function f ,we use the notation p [f ]to denote the expectation of f under p .The feature expectations π[f j ]can be approximated using a set of sample points x 1,...,x m drawn inde-pendently from X (with replacement)according to the probability distribution π.The empirical average of f j is 1m mi =1f j (x i ),which we can write as ˜π[f j ](where ˜πis the uniform distribution on the sample points),and use as an estimate of π[f j ].By the maximum entropy principle,therefore,we seek the probability distribu-tion ˆπof maximum entropy subject to the constraint that each feature f j has the same mean under ˆπas ob-served empirically,i.e.ˆπ[f j ]=˜π[f j ],for each feature f j(1)It turns out that the mathematical theory of convexduality can be used (Della Pietra et al.,1997)to showthat this characterization uniquely determines ˆπ,and that ˆπhas an alternative characterization,which can be described as follows.Consider all probability distribu-tions of the form q λ(x )=e λ·f (x )Z λ(2)where λis a vector of n real-valued coefﬁcients or fea-ture weights,f denotes the vector of all n features,and Z λis a normalizing constant that ensures that q λsums to 1.Such distributions are known as Gibbs distribu-tions.Convex duality shows that the Maxent probabil-ity distribution ˆπis exactly equal to the Gibbs prob-ability distribution q λthat maximizes the likelihood (i.e.,probability)of the m sample points.Equivalently,it minimizes the negative log likelihood of the sample points ˜π[−ln(q λ)](3)which can also be written ln Z λ−1m m i =1λ·f (x i )and termed the “log loss”.As described so far,Maxent can be prone to over-ﬁtting the training data.The problem derives from the fact that the empirical feature means will typically not equal the true means;they will only approximate them.Therefore the means under ˆπshould only be restricted to be close to their empirical values.One way this can be done is to relax the constraint in (1)above (Dud´ık et al.,2004),replacing it with|ˆπ[f j ]−˜π[f j ]|≤βj ,for each feature f j (4)for some constants βj .This also changes the dual char-acterization,resulting in a form of 1-regularization:the Maxent distribution can now be shown to be the Gibbs distribution that minimizes˜π[−ln(q λ)]+jβj |λj |(5)where the ﬁrst term is the log loss (as in (3)above),while the second term penalizes the use of large values for the weights λj .Regularization forces Max-ent to focus on the most important features,and 1-S.J.Phillips et al./Ecological Modelling190(2006)231–259237regularization tends to produce models with few non-zeroλj values(Williams,1995).Such models are less likely to overﬁt,because they have fewer parameters; as a general rule,the simplest explanation of a phe-nomenon is usually best(the principle of parsimony, Occam’s Razor).Note that 1regularization has also been applied to GLM/GAMs,and is called the“lasso”in that context(Guisan et al.,2002and references therein).This maximum likelihood formulation suggests a natural approach forﬁnding the Maxent probability distribution:start from the uniform probability distri-bution,for whichλ=(0,...,0),then repeatedly make adjustments to one or more of the weightsλj in such a way that the regularized log loss decreases.Regular-ized log loss can be shown to be a convex function of the weights,so no local minima exist,and several convex optimization methods exist for adjusting the weights in a way that guarantees convergence to the global min-imum(see Section2.2for the algorithm used in this study).The above presentation describes an“uncondi-tional”maximum entropy model.“Conditional”mod-els are much more common in the machine learning literature.The task of a conditional Maxent model is to approximate a joint probability distribution p(x,y) of the inputs x and output label y.Both presence and absence data would be required to train a conditional model of a species’distribution,which is why we use unconditional models.2.1.3.Application to species distribution modelingAustin(2002)examines three components needed for statistical modeling of species distributions:an eco-logical model concerning the ecological theory being used,a data model concerning collection of the data, and a statistical model concerning the statistical the-ory.Maxent is a statistical model,and to apply it to model species distributions successfully,we must con-sider how it relates to the two other modeling com-ponents(the data model and ecological model).Using the notation of Section2.1.2,we deﬁne the set X to be the set of pixels in the study area,and interpret the recorded presence localities for the species as sample points x1,...,x m taken from an unknown probability distributionπ.The data model consists of the method by which the presence localities were collected.One idealized sampling strategy is to pick a random pixel,and record1if the species is present there,and0other-wise.If we denote the response variable as y,then under this sampling strategy,πis the probability distribution p(x|y=1).By applying Bayes’rule,we get thatπis proportional to probability of occurrence,p(y=1|x), although with presence-only data we cannot determine the constant of proportionality.However,most presence-only datasets derive from surveys where the data model is much less well-deﬁned that the idealized model presented above.The various sampling biases described in Section1seriously violate this data model.In practice,then,π(andˆπ)can be more conservatively interpreted as a relative index of envi-ronmental suitability,where higher values represent a prediction of better conditions for the species(similar to the relaxed interpretation of GLMs with presence-only data in Ferrier et al.(2002)).The critical step in formulating the ecological model is deﬁning a suitable set of features.Indeed,the con-straints imposed by the features represent our ecologi-cal assumptions,as we are asserting that they represent all the environmental factors that constrain the geo-graphical distribution of the species.We considerﬁve feature types,described in Dud´ık et al.(2004).We did not use the fourth in our present study,as it may require more data than were available for our study species.1.A continuous variable f is itself a“linear feature”.It imposes the constraint onˆπthat the mean of the environmental variable,ˆπ[f],should be close to its observed value,i.e.,its mean on the sample locali-ties.2.The square of a continuous variable f is a“quadraticfeature”.When used with the corresponding linear feature,it imposes the constraint onˆπthat the vari-ance of the environmental variable should be close to its observed value,since the variance is equal to ˆπ[f2]−ˆπ[f]2.It models the species’tolerance for variation from its optimal conditions.3.The product of two continuous environmental vari-ables f and g is a“product feature”.Together with the linear features for f and g,it imposes the constraint that the covariance of those two variables should be close to its observed value,since the covariance isˆπ[fg]−ˆπ[f]ˆπ[g].Product features therefore in-corporate interactions between predictor variables.4.For a continuous environmental variable f,a“thresh-old feature”is equal to1when f is above a given。

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Potential Problems With the Random-Zero SphygmomanometerNicholas J. BirkettAbstract :The random-zero sphygmomanometer is frequently used in epidemiologic and clinical research to increase the reliability and validity of blood pressure readings. However, recent reports have suggested that there is a correlation between the zero values of a random-zero sphygmomanometer and the zero-corrected blood pressure readings obtained. The design of the random-zero sphygmomanometer would suggest that the zero values and zero-corrected blood pressures should be uncorrelated. Hence, residual correlation might be of importance in determining the utility of this device. We have explored this relation in the Middlesex County Blood Pressure Survey, which collected data on 2725 randomly selected adults. Each person had three blood pressure readings taken with a random-zero sphygmomanometer operated by trained interviewers.There was a very weak but statistically significant correlation between the zero values and zero-corrected systolic blood pressure (r=-.O34, regression slope =-.10), but there was no statistically significant relation with zero-corrected diastolic blood pressure (r=.0003, slope=.0006). Both the conelations and regression slopes were higher for subjects over age 65 years. These data fail to confirm the observed correlations found by Kronmal et al. This discrepancy might be explained by differences in measurement technique, which could introduce a blood pressure-dependent skewing of the range of zero values. If confirmed, this effect would have no effect on the validity of the final blood pressure readings and hence would not need to be considered in decisions about the use of the random-zero sphygmomanometer.Key Words :blood pressure determination blood pressure monitors • reproducibility of resultsObtaining accurate readings of blood pressure is important for studies of cardiovascular disease,both for epidemiologic and etiologic research and clinical trials. Research in the 1960s and 1970s established that the standard mercury sphygmomanometer can be subject to serious problems of bias and end-digit preference.1-3 Several approaches have been explored to overcome these problems, including standardized observer training,4-5 semiautomatic or automatic measuring of blood pressure,57 and the use of modified sphygmomanometers such as the LondonSchool of Hygiene sphygmomanometer1 and the random-zero sphygmomanometer.8'9 Of these approaches,the random-zero sphygmomanometer seems to have acquired the most popularity, although recently considerable interest has centered around improved automatic sphygmomanometers and 24-hour blood pressure monitoring.The original studies of the random-zero sphygmomanometer demonstrated that it reduced expectation bias and end-digit preference. The standard mercury sphygmomanometer is designed so that at equilibrium the mercury column lies at 0 mm Hg. The random-zero device overfills the column so that the equilibrium point is around 40 mm Hg. Before inflation, a wheel is spun to randomize the position of a cam. This cam acts as a stop point for a second, flexible mercury reservoir. During the process of cuff inflation, the reservoir is connected to the mercury column with the result that as the mercury column rises a portion of the mercury is pushed into the reservoir. When one is ready to measure the blood pressure, a valve is turned to isolate the column from the reservoir. By altering the amount of mercury in the column, the zero point of the sphygmomanometer is also varied in such a way that the observer is unaware of the actual zero reading until after the cuff has been completely deflated. The blood pressure is measured in the usual way with the equilibrium level of the deflated column (the zero reading) being subtracted from the observed systolic blood pressure (SBP) and diastolic blood pressure (DBP) to produce the actual blood pressure levels. The machine is designed so that the zero level varies from 0 to approximately 20 mm Hg. To ensure that the machine functions correctly, it is necessary to inflate the cuff to a higher level than usual and wait for between 5 and 10 seconds before deflation is begun so that the mercury reservoir has been properly filled. Failure to comply with these instructions will tend to reduce the range of zero levels but should not bias the blood pressure readings. This issue will be discussed again below.Comparison of blood pressures obtained using the random-zero sphygmomanometer with simultaneously obtained readings from a normal mercury sphygmomanometer reveals that the random-zero readings tend to be slightly lower (around 2 mm Hg for SBP and 4 mm Hg for DBP).1011 These studies also found a slight increase in the variance of readings taken using the random-zero sphygmomanometer. On the other hand, random-zero readings demonstrate less end-digit preference and are less susceptible to expectation bias.Recently, Kronmal et al12-13 have published data suggesting a further problem with the random-zero sphygmomanometer: they found highly significant correlations between the zero values and both the SBP and DBP after subtracting the zero values from the observed blood pressure values (regression slopes=-.87 and -.22 for SBP and DBP). These correlations persisted when the zero values were correlated with independently obtained blood pressure values from standard mercury sphygmomanometers (slopes=-.71 and -.17). They suggest that the actual blood pressure may be affecting the zero level.Concern over the implications of these observed correlations has led us to reanalyze data collected from more than 2700 randomly selected subjects in a community blood pressure survey. We presenthere our empirical findings and a hypothesis to explain the results.MethodsThe Middlesex County Blood Pressure Survey was conducted from 1982 to 1983 in Middlesex County, Ontario, Canada. The methods of this survey have been published elsewhere.14 Briefly, before the start of any field work, the protocol was reviewed and approved by the Ethics Review Committee of the University of Western Ontario. All subjects were contacted using approved consent procedures, and all study methods were in accordance with institutional guidelines and were approved by this committee. A three-stage stratified probability sample of 1500 households containing 3067 people was selected. Personal interviews were conducted in the respondent's home. The interviews included a 15-minute questionnaire and three blood pressure measurements. All readings were obtained with the random-zero sphygmomanometer (Hawksley and Sons Ltd). Readings followed a procedure similar to that recommended by the Canadian Hypertension Society with fifth-phase diastolic pressure being recorded." The cuff was selected from three different sizes (small, adult, and large) based on arm circumference, and all readings were obtained with subjects in a sitting position. After randomization of the zero point, the cuff was inflated to at least 200 mm Hg for at least 5 seconds. The cuff was then deflated at a rate of 2 mm Hg/s with the first and fifth KorotkofF phases being recorded to the nearest even digit. After complete deflation, the zero point was read to the nearest digit. Observed blood pressures were converted to actual pressures by subtracting the zero value from the observed readings. All observers were trained using the Rose technique.4 In total, nine interviewers conducted the interviews, obtaining a minimum of 393 readings each with a mean of 908 readings per interviewer. All interviewers had their own sphygmomanometer, making it impossible to separate interviewer from machine effects. Pearson correlation coefficients were calculated to relate the observed zero values to the zero-corrected blood pressures. Linear regression was used to obtain the slope of this relation. Comparability of the slopes across the three measurements was performed using ANOV A techniques.ResultsInterviews were obtained from 2735 people (response rate, 89.2%) with three blood pressure readings obtained in 2725 subjects and two readings in 1 additional subject. The mean blood pressures at the three readings were 124.1/76.9, 122.9/76.7, and 122.2/76.5 mm Hg, respectively. The mean subject age was 42 years; 47% of the sample was male.Separate correlation and regression analyses were obtained on the readings from the first, second, and third measurements. A standard test for equality of correlation coefficients15 showed that there were no significant differences among the correlations for SBP (*2=3.4, P=.18) or DBP (^1=3.5, P=.17). ANOV A testing also revealed no difference in the regression slopes from the three sets ofmeasurements (P>.2). Therefore, all measurements were combined in the remaining analyses. Tables 1 and 2 present the correlations and regression slopes relating the observed zero values to the zerocorrected SBP and DBP for each interviewer separately and for the pooled data set. For SBP, the correlations and slopes were significant for only three of the nine interviewers. The pooled estimate of the correlation was only -.034, with a regression slope of -.10. These both achieved statistical significance because of the large sample size but were markedly smaller than the corresponding values from Kronmal et al (correlation =-.20, slope=-.71).13 For DBP, values for only one interviewer were significant, and the pooled estimates of correlation (.00032) and slope (.0006) were effectively zero and again much lower than found by Kronmal et al (correlation =-.09, slope=-.17).13The sample studied by Kronmal et al13 was composed only of people over age 65 years. To explore the hypothesis that the correlations might be age specific, we repeated the overall analyses with the population divided into two age groups (under 65 and 65 and over). The results are shown in Table 3. The equality of the regression slopes within age groups was tested using a parallel line analysis.17 For SBP, the slopes were significantly different (F1]?171=7.62, P=.0058). Similarly, for DBP, the hypothesis of equality of the slopes was rejected with a borderline P value (F1 8 m = 4.04, P=.Q45). However, even for people over age 65, the correlation between zero values and SBP was small (-.089).The Figure shows a histogram of the zero values obtained from the 8176 readings. If the devices had functioned as designed, one would expect to have 5% of the zero readings at each value between 0 and 20 mm Hg. It is apparent from the Figure that this was not achieved, with an excess of readings between 10 and 20 mm Hg when compared with the range from 0 to 9 mm Hg. In addition, there was a "tail" of zero values above 20 mm Hg. A somewhat similar pattern was reported by Kronmal et al,13 although the "tail" appears to be more prominent in that report. The Figure reveals no clear digit preference, although the frequency of even digit values (62.1%) is larger than would have been expected (50%), possibly reflecting some confusion by the interviewers over the instructions to record the blood pressure readings using only even digits but to record the zero value to the nearest digit (Z=21.8, P<.00001).DiscussionIn a large community blood pressure study, comparison of the zero-corrected blood pressure with the random-zero values failed to confirm the reported correlation between zero levels and blood pressure. There was a small but statistically significant correlation between the zero values and the zero-corrected SBP (r=-.O34).After insightful comments from a reviewer of this manuscript, a hypothesis can be presented to explain the discrepant observations. There is a noticeable time delay from when the maximal cuffpressure is reached to when the flexible reservoir of the random-zero sphygmomanometer is completely filled. The time required is related to both the randomly determined capacity of the reservoir and the pressure gradient pushing mercury into the reservoir. Examination of a random-zero sphygmomanometer revealed that maximal bladder filling takes less than 2 seconds at a cuff pressure of 200 mm Hg or higher. The time increases as the maximal cuff pressure is lowered until, at a cuff pressure of around 160 mm Hg, maximal bladder filling is never achieved. Because low zero values (0 to 5 mm Hg) require maximal reservoir filling, low cuff inflation pressure would tend to truncate the distribution of zero values. There would also be a tendency to obtain zeros greater than 20 mm Hg.From published reports, it appears that Kronmal etal12-13 determined the maximum point of cuff inflation based on the blood pressure level at which the radial pulse was obliterated to palpation. There is no clear statement about how long the maximum pressure was maintained before closing the reservoir value, but given the relative low pressure obtained, it is reasonable to assume that the reservoir would be incompletely filled. Under these assumptions, one would expect that subjects with higher SBP would tend to have more complete reservoir filling and thus lower mean zero values. This process would introduce a negative correlation between the true blood pressure and the zero values.This hypothesis is supported by several observations. First, the Middlesex survey used a fixed maximal inflation pressure (200 mm Hg) and maintained this pressure for at least 5 seconds. This would produce complete reservoir filling for all subjects, and hence there should be no correlation, as was found. Second, the correlation found by Kronmal etal12-13 remained even when independently obtained blood pressures collected with a standard mercury sphygmomanometer were substituted. Finally, the hypothesis is also compatible with the apparently larger proportion of zero values over 20 mm Hg in the study of Kronmal et al when compared with the Middlesex survey.If this explanation of the observed correlations is substantiated, are there significant implications regarding the use of the random-zero sphygmomanometer? In our opinion, the answer would be no. Once the valve has been closed to isolate the reservoir from the mercury column, the random-zero sphygmomanometer functions as a regular mercury sphygmomanometer. The effect of inadequate reservoir filling would be to alter the distribution of zero values, which would not have any effect on the true blood pressure readings. This was found by Kronmal et al12-13; blood pressure readings obtained with the random-zero sphygmomanometer and the standard sphygmomanometer were essentially the same once allowance was made for the reported bias of 2mm Hg associated with random-zero measurements.Other explanations for these discrepant results could be considered, but it is unclear which of theseother explanations could be seriously entertained. A number of sphygmomanometers were used in each study, making instrument failure an unlikely explanation for the differences. Kronmal et al12-13 studied a sample of people aged 65 and older, whereas the Middlesex study included adults of all ages. Hence, one might hypothesize an age-related effect. Subgroup analysis of the Middlesex County Survey by age indicates that the regression slopes between the zero readings and the zero-corrected blood pressure did increase with age, although the regression slopes found for people over age 65 are still much lower than reported by Kronmal et al. It has been suggested that increased arterial rigidity in the elderly adversely affects the validity of indirect blood pressure readings.18 One might hypothesize that differences in arterial responsiveness to cuff pressure at different zero levels in the elderly might introduce a spurious correlation, but there is no direct evidence to support this hypothesis.One further observation might be of relevance: The mean blood pressure found in the study of Kronmal et al (132.3/68.6 mmHg)12-13 is considerably lower than would have been expected based on other studies o blood pressure in the elderly. When the observations from the Middlesex study are restricted to people over age 65 and weighted according to the sampling fractions used by \ Kronmal et al as reported in Fried et al,19 the mean blood pressure becomes 143.6/77.9 mm Hg. This marked difference in mean blood pressure is difficult to explain given the reported sample selection strategies of the two surveys. It is possible that the factor leading to the lower mean blood pressures might also be responsible for the higher zero blood pressure correlations found by Kronmal et al,12-13 although it is difficult tom identify a selection or other bias that would operate in this fashion.Decisions about the value of the random-zero sphygmomanometer in epidemiologic and clinical research will depend on factors other than a potential zerocorrected blood pressure correlation. The consistent observation that random-zero blood pressure values are approximately 2 mm Hg lower than those obtained with a standard mercury sphygmomanometer is a matter for concern. This would argue that consistency of use within a study is of considerable importance. Further work is needed to establish the role of the random-zero sphygmomanometer in clinical research. References1. Rose GA, Holland WW, Crowley EA. A sphygmomanometer for epidemiologists. Lancet.2. Labarthe DR, Hawkins CN, Reminigton RD. Evaluation of performance of selected devices for measuring blood pressure. Am J Cardiol.3. Oldham PD, Pickering Sir G, Roberts JAF, Sowry GSC. The nature of essential hypertension. Lancet.4. Rose GA. Standardization of observers in blood-pressure measurement. Lancet.5. Curb JD, Labarthe DR, Cooper SP, Cutter GR, Hawkins CM. Training and certification of bloodpressure observers. Hypertension.6. O'Brien E, Petrie J, Littler W, de Swiet M, Padfield PL, O'Malley K, Jamieson M, Altman D, Bland M, Atkins N. The British Hypertension Society protocol for the evaluation of automated and semiautomated blood pressure measuring devices with special reference to ambulatory systems. J Hypertens.7. Mancia G, Di Rienzo M, Parati G. Ambulatory blood pressure monitoring use in hypertension research and clinical practice. Hypertension.8. Wright BM, Dore CF. A random-zero sphygmomanometer. Lancet.9. Evans JG, Prior LAM. Experience with the random-zero sphygmomanometer. BrJPrev Soc Med.10. Parker D, Liu K, Dyer AR, Giumetti D, Liao Y, Stamler J. A comparison of the random-zero and standard mercury sphygmomanometers. Hypertension.11. O'Brien E, Fainsia M, Atkins N, O'Malry K. Inaccuracy of the Hawksley-random zero sphygmomanometer. Lancet.12. Kronmal RA, Rutan GH, Borhani NO, Manolio TA, Furberg CD. Potential problems with random-zero sphygmomanometer. Lancet.13. Kronmal RA, Rutan GH, Manolio TA, Borhani NO. Properties of the random zero sphygmomanometer. Hypertension.14. Birkett NJ, Dormer AP, Maynard M. Prevalence and control of hypertension in an Ontario County. Can Med Assoc J.15. Logan AG. Report of the Canadian Hypertension Society's consensus conference on the management of mild hypertension. Can Med Assoc J.16. Snedecor GW, Cochran WG. Statistical Methods. 6th ed. Ames, Iowa: Iowa State University Press.17. Kleinbaum DG, Kupper LL. Applied Regression Analysis and Other MultivariabU Methods. 1st ed. Mass: Duxbury Press.18. Finnegan TP, Spence JD, Wong DG, Wells GA. Blood pressure measurement in the elderly: correlation of arterial stiffness with difference between inter-arterial and cuff pressures. J Hypertens. .19. Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio TA, Mittelmark MB, Newman A, O'Leary DH, Psaty B, Rautaharju P, Tracy RP, Weiler PG, for the Cardiovascular Health Study Research Group. The Cardiovascular Health Study: design and rationale. Ann Epidemiol.。

《普通生态学》专英词汇

《普通生态学》专英词汇Ecology生态学Interaction相互作用Individual个体Population种群Community群落Ecosystem生态系统Assemblage集合Mixture混合体Biosphere生物圈Scale尺度Field approach野外研究法Experimental approach实验研究法Theoretical approach理论研究法Environment环境Macro environment大环境Micro environment小环境Marco climate大气候Micro climate小气候Biome生物群系Ecological factor生态因子Habitat生境Density dependent factor密度制约因子Density independent factor非密度制约因子Coevolution协同进化Liebig’s “law of minimum”利比希最小因子定律Law of limiting factors限制因子定律Limiting factor限制因子Law of tolerance耐受性定律Ecological amplitude生态幅Ecological valence生态价Eury-广，steno-狭Eurythermal广温性stenothermal 狭温性Euryhydric 广水性stenohydric 狭水性Euryhaline 广盐性stenohaline 狭盐性Euryphagic 广食性stenophagic 狭食性Euryphotic 广光性stenophot ic 狭光性Euryecious 广栖性stenoecious 狭栖性Euryedapic 广土性stenoedapic 狭土性Homeostasis 内稳性Epilimnion 上湖层Thermocline 斜温层、温梯层Hypolimnion 下湖层Photosynthetically active radiation 光合有效辐射Etiolation phenomenon 黄化现象Photosynthetic capacity 光合任用Daily rhythm 昼夜节律Photoperiodism 或photoperiodicity 光周期现象Long day plant 长日照植物Short day plant 短日照植物Day intermediate plant 中日照植物Day neutral plant 日中性植物Long day animal 长日照动物Short day animal 短日照动物Diaqause 滞育Homeotherm 常温动物Poikilothem 变温动物Ectotherm 外温动物Endotherm 内温动物The thermoneutral zone 热中性区Temperature coefficient 温度系数Freeze injury 冻害Chilling injury 冷害Developmental threshold temperature 发育阈温度Biological zero 生物学零度Sum of heat 总积温Sum of effective temperature 有效积温Phtsiological time 生理时间Vernalization 春化Acclimation 驯化Acclimatization 气候驯化Bergmann`s rule 贝格曼规律Allen`s rule 阿伦规律Countercurrent heat cxchange 逆流热交换Nonshivering thermogenesis 非颤抖性产热Brown adipose tissue 褐色脂肪组织Heterothermy 异温性Daily torpor 日麻痹Hibernation 冬眠Estivation 夏眠Heterotherm 异温动物Adaptive hypothermia 适应性低体温Polar nature 极性性质High heat capacity 高热容量Precipitation 降雨量Atmosphere humidity 大气湿度Relative humidity 相对湿度Transpiration 蒸腾Field capacity 田间持水量Hygrophyte 湿生植物Mesad 中生植物Siccocolous 旱生植物Apuatic plant 水生植物Water balance 水平衡Hypertonic 高渗性的Hypotonic 低渗性的Isotonic 等渗的Water loss 失水Countercurrent exchange 逆流交换Urea 尿素Uricacid 尿酸Humidity 湿度Snow cover 雪被Energy metabolism 能量代谢Hypoxia adaptation 高海拔低氧的适应2、3-diphosphoglycreate，DPG 2、3-二磷酸甘油酸Pco2 分压Green-house effect 温室效应Texture 土壤质地Soil structure 土壤结构Soil moisture 土壤水分Fossorial mammal 地下兽Soil temperature 土壤Soil acidity 土壤酸度Humus 腐殖质Psammophyte 沙生植物Crown fire 林冠火Surface fire 地面火Population 种群Unilary organism 单体生物Modular organism 构件生物Ramets 无性系分株Species 物种Size 大小Evolutionary individual进化个体Internal distribution pattern 内分布型Dispersion 分布Random 随机的Uniform 均匀的Clumped 成群的Architecture 建筑学结构Natality 出生率Mortality 死亡率Demography 种群统计学Age pyramid 年龄锥体Stage structure 时期结构Size classes个体大小群Sex ratio 性比Life table 生命表Cohort 同生群Cohort analysis 同生群分析Agespecific survival rate 特定年龄存活率Life expectancy 生命期望Killing power致死力Net reproductive rate净生产率Key factors关键因子k-factor analysis K-因子分析killing factor致死因子survivorship curve存活曲线generation time世代时间innate rate of increase内禀增长率density-independent growth与密度无关的种群增长per-capita rate of population growth每员增长率density-dependent growth 与密度有关的种群增长carrying capacity环境容纳量logistic equation逻辑斯谛方程minimum viable population最小可存活种群ecological invasion生态入侵nutrient recovery hypothesis营养物恢复学说Wyune-Edwards 行为调节学说(温-爱德华学说) Christian内分泌调节学说(克里斯琴学说) Chitty奇蒂学说Metapopulation集合种群Local population局域种群Patch斑块Local scale局域尺度Metapopulation scale集合种群尺度Geographical scale地理尺度Local breeding population局域繁殖种群Turnover周转Genotype基因型Phenotype表现型Diploid二倍体Homologous同源Gene基因Allele等位基因Locus座位Homozygous纯合的Herterozygous杂合的Codominant共显性的Dominant显性的Recessive隐性Polygenic多基因的Gene pool基因库Genotypic frequency基因型频率Gene frequency基因频率Hardy-Weinberg Law哈代-魏伯格定律Variation变异Gelelectrophoresis凝胶电泳Allozyme别构酶Polymorphism多态现象Geopraphic variation地理变异Cline渐变群Subspecies地理亚种Natural selection自然选择Fitness适合度Selective coefficient选择系数Genetic drift遗传漂变Fixation固定Evolutionary forces进化动力Founder effect建立者效应Founder population建立者种群Stabilizing selection稳定选择Directional selection定向选择Disruptive selection分裂选择Gamete selection配子选择Kin selection亲属选择Group selection群体选择Sexual selection性选择Speciation物种形成Gene flow基因流Geographical theory of speciation地理物种形成学说Reproductive isolating mechanism繁殖隔离机制Polyploidy多倍体Adaptive radiation适应辐射Life history生活史Body size身体大小Growth rate生长率Reproduction繁殖Longevity寿命Bionomic strategy生态对策Life history strategy生活史对策Darwinian demons达尔文魔鬼Trade-off权衡Energy allocation能量分配Semelparity单次繁殖Parental care亲体关怀Current reproduction当前繁殖Future reproduction未来的繁殖Reproductive value生殖价Bet—hedging两面下注Diapause滞育Dormancy休眠Seed bank种子库Crytobiosis潜生现象Torper蛰伏Hibernation冬眠Aestivation夏眠Migration迁徙Dispersal扩散Morphological form形态学形状Generation世代Metamorphosis变态Optimization in habitat utilization生境利用最优化Mechanistic level 机械水平Mutation-accumulation突变积累Antagonistic pleiotropy拮抗性多效Competition竞争Cannibalism自相残杀Predation捕食Parasition寄生Mutualism互利共生Parasitoidism拟寄生Commensualism偏利共生Amensualism偏害共生Intraspecific relationship种内关系Intraspecific competition 种内竞争Territoriality领域性Law of constant final yield最后产量法则Self-thinning自疏Ecology of ***性别生态学Parental investment亲代投入Hermaphrodite雌雄同体Self-compatibility自我兼容Cleistogamous闭花受精Recombine重组Red Queen effect红皇后效应Fisher’s *** ratio theory Fisher氏性比理论Rare typeadvantage稀少型有利Equal investment相等投入Local resource competition局域资源竞争Local mate competition局域交配竞争Sexual selection性选择Intra***ual selection性内选择。

《孟德尔随机化研究指南》中英文版

《孟德尔随机化研究指南》中英文版全文共3篇示例，供读者参考篇1Randomized research is a vital component of scientific studies, allowing researchers to investigate causal relationships between variables and make accurate inferences about the effects of interventions. One of the most renowned guides for conducting randomized research is the "Mendel Randomization Research Guide," which provides detailed instructions and best practices for designing and implementing randomized controlled trials.The Mendel Randomization Research Guide offers comprehensive guidance on all aspects of randomized research, from study design and sample selection to data analysis and interpretation of results. It emphasizes the importance of randomization in reducing bias and confounding effects, thus ensuring the validity and reliability of study findings. With clear and practical recommendations, researchers can feel confident in the quality and rigor of their randomized research studies.The guide highlights the key principles of randomization, such as the use of random assignment to treatment groups, blinding of participants and researchers, and intent-to-treat analysis. It also discusses strategies for achieving balance in sample characteristics and minimizing the risk of selection bias. By following these principles and guidelines, researchers can maximize the internal validity of their studies and draw accurate conclusions about the causal effects of interventions.In addition to the technical aspects of randomized research, the Mendel Randomization Research Guide also addresses ethical considerations and practical challenges that researchers may face. It emphasizes the importance of obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring the safety and well-being of study subjects. The guide also discusses strategies for overcoming common obstacles in randomized research, such as recruitment and retention issues, data collection problems, and statistical challenges.Overall, the Mendel Randomization Research Guide is a valuable resource for researchers looking to improve the quality and validity of their randomized research studies. By following its recommendations and best practices, researchers can conductstudies that produce reliable and actionable findings, advancing scientific knowledge and contributing to evidence-based decision making in various fields.篇2Mendel Randomization Study GuideIntroductionMendel Randomization Study Guide is a comprehensive and informative resource for researchers and students interested in the field of Mendel randomization. This guide provides anin-depth overview of the principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies.The guide is divided into several sections, each covering a different aspect of Mendel randomization. The first section provides a brief introduction to the history and background of Mendel randomization, tracing its origins to the work of Gregor Mendel, the father of modern genetics. It also discusses the theoretical foundations of Mendel randomization and its potential applications in causal inference.The second section of the guide focuses on the methods and techniques used in Mendel randomization studies. This includesa detailed explanation of how Mendel randomization works, as well as guidelines on how to select instrumental variables and control for potential confounders. It also discusses the strengths and limitations of Mendel randomization, and provides practical tips on how to deal with common challenges in Mendel randomization studies.The third section of the guide is dedicated to practical considerations in Mendel randomization studies. This includes advice on how to design a Mendel randomization study, collect and analyze data, and interpret the results. It also provides recommendations on how to report Mendel randomization studies and publish research findings in scientific journals.In addition, the guide includes a glossary of key terms and concepts related to Mendel randomization, as well as a list of recommended readings for further study. It also includes case studies and examples of Mendel randomization studies in practice, to illustrate the principles and techniques discussed in the guide.ConclusionIn conclusion, the Mendel Randomization Study Guide is a valuable resource for researchers and students interested in Mendel randomization. It provides a comprehensive overview ofthe principles and methods of Mendel randomization, as well as practical advice on how to design and conduct Mendel randomization studies. Whether you are new to Mendel randomization or looking to deepen your understanding of the field, this guide is an essential reference for anyone interested in causal inference and genetic epidemiology.篇3"Guide to Mendelian Randomization Studies" English VersionIntroductionMendelian randomization (MR) is a method that uses genetic variants to investigate the causal relationship between an exposure and an outcome. It is a powerful tool that can help researchers to better understand the underlying mechanisms of complex traits and diseases. The "Guide to Mendelian Randomization Studies" provides a comprehensive overview of MR studies and offers practical guidance on how to design and carry out these studies effectively.Chapter 1: Introduction to Mendelian RandomizationThis chapter provides an overview of the principles of Mendelian randomization, including the assumptions andlimitations of the method. It explains how genetic variants can be used as instrumental variables to estimate the causal effect of an exposure on an outcome, and outlines the key steps involved in conducting an MR study.Chapter 2: Choosing Genetic InstrumentsIn this chapter, the guide discusses the criteria for selecting appropriate genetic instruments for Mendelian randomization. It covers issues such as the relevance of the genetic variant to the exposure of interest, the strength of the instrument, and the potential for pleiotropy. The chapter also provides practical tips on how to search for suitable genetic variants in public databases.Chapter 3: Data Sources and ValidationThis chapter highlights the importance of using high-quality data sources for Mendelian randomization studies. It discusses the different types of data that can be used, such asgenome-wide association studies and biobanks, and offers advice on how to validate genetic instruments and ensure the reliability of the data.Chapter 4: Statistical MethodsIn this chapter, the guide explains the various statistical methods that can be used to analyze Mendelian randomization data. It covers techniques such as inverse variance weighting, MR-Egger regression, and bi-directional Mendelian randomization, and provides guidance on how to choose the most appropriate method for a given study.Chapter 5: Interpretation and ReportingThe final chapter of the guide focuses on the interpretation and reporting of Mendelian randomization results. It discusses how to assess the strength of causal inference, consider potential biases, and communicate findings effectively in research papers and presentations.ConclusionThe "Guide to Mendelian Randomization Studies" is a valuable resource for researchers who are interested in using genetic data to investigate causal relationships in epidemiological studies. By following the guidance provided in the guide, researchers can enhance the rigor and validity of their Mendelian randomization studies and contribute to a better understanding of the determinants of complex traits and diseases.。

[转载]一个师兄论述的群落生态学中性理论，真令我汗颜

[转载]⼀个师兄论述的群落⽣态学中性理论，真令我汗颜原⽂地址：⼀个师兄论述的群落⽣态学中性理论，真令我汗颜作者：⼀般群落⽣态学中性理论引⾃李奇的博客从物理学到⽣态学，科学探索的⼀个令⼈敬畏的⽬标就是确定在⾃然中起作⽤的⼒量，以及这些⼒量如何组织我们的世界[1]。

群落⽣态学是⽣态学研究的核⼼。

对⽣物群落，尤其是植物群落各种模式形成机制的解释，是⽣态学家⾄今仍需⾯对的挑战。

其主要内容包括物种间相对多度的分布，多样性的形成、维持及进化机制和群落组建与演替过程等⼀系列问题[1-8,11-12]。

⽣态位理论和中性理论都可能解释群落的装配、动态和结构[1-6，12]。

但是⼆者的相对重要性仍然是群落⽣态学⼀个悬⽽未决的重要问题[7,11]。

⽣态位理论主要是基于权衡（如特定环境中的竞争能⼒）来解释物种多度和分布。

但是当更⼴泛地探索为什么群落中经常有许多稀有种，⽽只有⼀些丰富种时，仅仅基于确定物种特性的⽣态位理论显然是不够的[1]。

由于⽣态位理论不能给群落内物种相对多度分布、种-⾯积关系等群落学模式提供令⼈满意的解释，中性理论应运⽽⽣[1]。

中性理论是过去⼗年中⽣态学核⼼理论的重⼤突破之⼀。

中性理论考虑同域的竞争相似资源的营养相似物种。

完全抛弃了如竞争优势等物种特性，甚⾄认为不同物种是功能等值的。

模型假设群落中所有种有关这些过程的特性都是等同的，物种随机绝灭。

通过对复杂现象的简化，成功地描述了⼤量的群落物种多度模式[1-8]。

Hubbell（2005）认为功能等值并不要求物种的特性等同，只是这些物种特性的差异不会导致关键的种群统计学参数差异[14]。

中性理论预测物种特性与多度⽆关、群落组成与环境条件⽆关。

物种的稀有与丰富不是因为该物种和竞争者的特性，⽽只是因为竞争等同物种密度的随机漂移[2，12]。

中性理论也有很多缺陷，不能预测何种物种丰富或稀有是其中之⼀。

当前的⽣态位学家试图把⽣态位概念分为：物种⽣活在⼀个特定环境中的需求和物种对其⽣活环境的影响两部分。

生态学总结

P2191. Environment limits the geographic distribution of species. 环境限制了物种在地理上的分布。

2. On small scales, individuals within populations are distributed in patterns that may be random,regular, or clumped. 在小尺度上，群体内个体的分布模式，可能是随机的，固定的，或聚集的。

3. Population density declines with increasing organism size. 种群密度随有机体大小的增加的下降。

4. Abundance 丰度：研究体系中被研究元素的相对含量。

5. Niche 生态位：物种在环境中所处的地位以及食物、行为等细节。

6. Fundamental niche 基础生态位：一个物种在无别的竞争物种存在时所占有的生态位。

7. Population 种群：在一定时间内占据一定空间的同种生物的所有个体。

8. Realized niche 实际生态位：有别的物种竞争存在时的生态位。

P2421. Dispersal can increase or decrease local population densities. 扩散可以增加或减少当地的种群密度。

2. Ongoing dispersal can join numerous subpopulations to form a metapopulation. 许多亚群可以加入正在扩散的种群形成一个集合种群。

3. A survivorship curve summarizes the pattern of survival in a population. 存活曲线总结了种群的生存模式。

4. The age distribution of a population reflects its history of survival, reproduction, and potential forfuture growth. 种群的年龄分布反映了其在历史上的生存，繁殖，和未来的增长潜力。

SADISA包（版本1.2）：物种丰度分布与独立物种假设说明书

Package‘SADISA’October12,2022Type PackageTitle Species Abundance Distributions with Independent-SpeciesAssumptionVersion1.2Author Rampal S.Etienne&Bart HaegemanMaintainer Rampal S.Etienne<******************>Description Computes the probability of a set of species abundances of a single or multiple sam-ples of individuals with one or more guilds under a mainland-island model.One must spec-ify the mainland(metacommunity)model and the island(local)community model.It as-sumes that speciesﬂuctuate independently.The package also contains functions to simulate un-der this model.See Haegeman,B.&R.S.Etienne(2017).A general sampling formula for com-munity structure data.Methods in Ecology&Evolution8:1506-1519<doi:10.1111/2041-210X.12807>.License GPL-3LazyData FALSERoxygenNote6.1.1Encoding UTF-8Depends R(>=3.5)Imports pracma,DDD(>=4.1)Suggests testthat,knitr,rmarkdown,VignetteBuilder knitrNeedsCompilation noRepository CRANDate/Publication2019-10-2312:10:02UTCR topics documented:convert_fa2sf (2)datasets (2)ﬁtresults (3)12datasets integral_peak (4)SADISA_loglik (5)SADISA_ML (6)SADISA_sim (8)SADISA_test (9)Index11 convert_fa2sf Converts different formats to represent multiple sample dataDescriptionConverts the full abundance matrix into species frequencies If S is the number of species and M is the number of samples,then fa is the full abundance matrix of dimension S by M.The for example fa=[010;321;010]leads to sf=[0102;3211];Usageconvert_fa2sf(fa)Argumentsfa the full abundance matrix with species in rows and samples in columnsValuethe sample frequency matrixReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.datasets Data sets of various tropical forest communitiesDescriptionVarious tree commnunity abundance data sets to test and illustrate the Independent Species ap-proach.•dset1.abunvec contains a list of6samples of tree abundances from6tropical forest plots(BCI, Korup,Pasoh,Sinharaja,Yasuni,Lambir).•dset2.abunvec contains a list of11lists with one of11samples from BCI combined with samples from Cocoli and Sherman.ﬁtresults3•dset3.abunvec contains a list of6lists with2samples,each from one dispersal guild,for6tropical forest communities(BCI,Korup,Pasoh,Sinharaja,Yasuni,Lambir).•dset4a.abunvec contains a list of6samples from6censuses of BCI(1982,1985,1990,1995,200,2005)with dbh>1cm.•dset4b.abunvec contains a list of6samples from6censuses of BCI(1982,1985,1990,1995,200,2005)with dbh>10cm.Usagedata(datasets)FormatA list of5data sets.See description for information on each of these data sets.Author(s)Rampal S.Etienne&Bart HaegemanSourceCondit et al.(2002).Beta-diversity in tropical forest trees.Science295:666-669.See also11.Janzen,T.,B.Haegeman&R.S.Etienne(2015).A sampling formula for ecological communitieswith multiple dispersal syndromes.Journal of Theoretical Biology387,258-261.fitresults Maximum likelihood estimates and corresponding likelihood valuesfor variousﬁts to various tropical forest communitiesDescriptionMaximum likelihood estimates and corresponding likelihood values for variousﬁts to various trop-ical forest communities,to test and illustrate the Independent Species approach.•ﬁt1a.llikopt contains maximum likelihood values ofﬁt of pm-dl model to dset1.abunvec•ﬁt1a.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dl model to dset1.abunvec •ﬁt1b.llikopt contains maximum likelihood values ofﬁt of pmc-dl model to dset1.abunvec•ﬁt1b.parsopt contains maximum likelihood parameter estimates ofﬁt of pmc-dl model todset1.abunvec•ﬁt2.llikopt contains maximum likelihood values ofﬁt of rf-dl model to dset1.abunvec•ﬁt2.parsopt contains maximum likelihood parameter estimates ofﬁt of rf-dl model to dset1.abunvec •ﬁt3.llikopt contains maximum likelihood values ofﬁt of dd-dl model to dset1.abunvec•ﬁt3.parsopt contains maximum likelihood parameter estimates ofﬁt of dd-dl model to dset1.abunvec •ﬁt4.llikopt contains maximum likelihood values ofﬁt of pm-dl model to dset2.abunvec(mul-tiple samples)4integral_peak •ﬁt4.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dl model to dset1.abunvec(multiple samples)•ﬁt5.llikopt contains maximum likelihood values ofﬁt of pm-dl model to dset3.abunvec(mul-tiple guilds)•ﬁt5.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dl model to dset3.abunvec (multiple guilds)•ﬁt6.llikopt contains maximum likelihood values ofﬁt of pr-dl model to dset1.abunvec•ﬁt6.parsopt contains maximum likelihood parameter estimates ofﬁt of pr-dl model to dset1.abunvec •ﬁt7.llikopt contains maximum likelihood values ofﬁt of pm-dd model to dset1.abunvec•ﬁt7.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dd model to dset1.abunvec •ﬁt8a.llikopt contains maximum likelihood values ofﬁt of pm-dd model to dset4a.abunvec•ﬁt8a.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dd model todset4a.abunvec•ﬁt8b.llikopt contains maximum likelihood values ofﬁt of pm-dd model to dset4b.abunvec•ﬁt8b.parsopt contains maximum likelihood parameter estimates ofﬁt of pm-dd model todset4b.abunvecUsagedata(fitresults)FormatA list of20lists,each containing either likelihood values or the corresponding parameter estimates.See description.Author(s)Rampal S.Etienne&Bart HaegemanSourceCondit et al.(2002).Beta-diversity in tropical forest trees.Science295:666-669.integral_peak Computes integral of a very peaked functionDescription#computes the logarithm of the integral of exp(logfun)from0to Inf under the following assump-tions:Usageintegral_peak(logfun,xx=seq(-100,10,2),xcutoff=2,ycutoff=40,ymaxthreshold=1e-12)SADISA_loglik5Argumentslogfun the logarithm of the function to integratexx the initial set of points on which to evaluate the functionxcutoff when the maximum has been found among the xx,this parameter sets the width of the interval toﬁnd the maximum inycutoff set the threshold below which(on a log scale)the function is deemed negligible,i.e.that it does not contribute to the integral)ymaxthreshold sets the deviation allowed inﬁnding the maximum among the xxValuethe result of the integrationReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.SADISA_loglik Computes loglikelihood for requested modelDescriptionComputes loglikelihood for requested model using independent-species approachUsageSADISA_loglik(abund,pars,model,mult="single")Argumentsabund abundance vector or a list of abundance vectors.When a list is provided and mult=’mg’(the default),it is assumed that the different vectors apply to dif-ferent guilds.When mult=’ms’then the different vectors apply to multiplesamples from the same metacommunity.In this case the vectors should haveequal lengths and may contain zeros because there may be species that occur inmultiple samples and species that do not occur in some of the samples.Whenmult=’both’,abund should be a list of lists,each list representing multiple guildswithin a samplepars a vector of model parameters or a list of vectors of model parameters.Whena list is provided and mult=’mg’(the default),it is assumed that the differentvectors apply to different guilds.Otherwise,it is assumed that they apply tomultiple samples.model the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomﬁssion),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’(the default),the loglikelihood for a single sample is com-puted When set to’mg’the loglikelihood for multiple guilds is computed.Whenset to’ms’the loglikelihood for multiple samples from the same metacommu-nity is computed.When set to’both’the loglikelihood for multiple guilds withinmultiple samples is computed.DetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently avail-able model combinations are,for a single sample,c(’pm’,’dl’),c(’pm’,’rf’),c(’dd’,’dl’),c(’pr’,’dl’), c(’pm’,’dd’),and for multiple samples,c(’pm’,’dl’).ValueloglikelihoodReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807Examplesdata(datasets);abund_bci<-datasets$dset1.abunvec[[1]];data(fitresults);data.paropt<-fitresults$fit1a.parsopt[[1]];result<-SADISA_loglik(abund=abund_bci,pars=data.paropt,model=c( pm , dl ));cat( The difference between result and the value in fitresults.RData is: ,result-fitresults$fit1a.llikopt[[1]]);SADISA_ML Performs maximum likelihood parameter estimation for requestedmodelDescriptionComputes maximum loglikelihood and corresponding parameters for the requested model using the independent-species approach.For optimization it uses various auxiliary functions in the DDD package.UsageSADISA_ML(abund,initpars,idpars,labelpars,model=c("pm","dl"),mult="single",tol=c(1e-06,1e-06,1e-06),maxiter=min(1000*round((1.25)^sum(idpars)),1e+05),optimmethod="subplex",num_cycles=1)Argumentsabund abundance vector or a list of abundance vectors.When a list is provided and mult=’mg’(the default),it is assumed that the different vectors apply to dif-ferent guilds.When mult=’ms’then the different vectors apply to multiplesamples.from the same metacommunity.In this case the vectors should haveequal lengths and may contain zeros because there may be species that occur inmultiple samples and species that do not occur in some of the samples.initpars a vector of initial values of the parameters to be optimized andﬁxed.See labelpars for more explanation.idpars a vector stating whether the parameters in initpars should be optimized(1)or remainﬁxed(0).labelpars a vector,a list of vectors or a list of lists of vectors indicating the labels integers (starting at1)of the parameters to be optimized andﬁxed.These integers cor-respond to the position in initpars and idpars.The order of the labels in thevector/list isﬁrst the metacommunity parameters(theta,and phi(for protractedspeciation)or alpha(for density-dependence or abundance-dependent specia-tion)),then the dispersal parameters(I).See the example and the vignette formore explanation.model the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomﬁssion),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’(the default),the loglikelihood for a single sample and single guild is computed.When set to’mg’,the loglikelihood for multiple guildsis computed.When set to’ms’the loglikelihood for multiple samples from thesame metacommunity is computed.tol a vector containing three numbers for the relative tolerance in the parameters,the relative tolerance in the function,and the absolute tolerance in the parameters.maxiter sets the maximum number of iterationsoptimmethod sets the optimization method to be used,either subplex(default)or an alternative implementation of simplex.num_cycles the number of cycles of opimization.If set at Inf,it will do as many cycles as needed to meet the tolerance set for the target function.8SADISA_simDetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently avail-able model combinations are,for a single sample,c(’pm’,’dl’),c(’pm’,’rf’),c(’dd’,’dl’),c(’pr’,’dl’), c(’pm’,’dd’),and for multiple samples,c(’pm’,’dl’).ReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807Examplesutils::data(datasets);utils::data(fitresults);result<-SADISA_ML(abund=datasets$dset1.abunvec[[1]],initpars=fitresults$fit1a.parsopt[[1]],idpars=c(1,1),labelpars=c(1,2),model=c( pm , dl ),tol=c(1E-1,1E-1,1E-1));#Note that tolerances should be set much lower than1E-1to get the best results. SADISA_sim Simulates species abundance dataDescriptionSimulates species abundance data using the independent-species approachUsageSADISA_sim(parsmc,ii,jj,model=c("pm","dl"),mult="single",nsim=1)Argumentsparsmc The model parameters.For the point mutation(pm)model this is theta and I.For the protracted model(pr)this is theta,phi and I.For the density-dependentmodel(dd)-which can also be interpreted as the per-species speciation model,this is theta and alpha.ii The I parameter.When I is a vector,it is assumed that each value describes a sample or a guild depending on whether mult==’ms’or mult==’mg’.Whenmult=’both’,a list of lists must be speciﬁed,with each list element relates to asample and contains a list of values across guilds.jj the sample sizes for each sample and each guild.Must have the same structure as iimodel the chosen combination of metacommunity model and local community model as a vector,e.g.c(’pm’,’dl’)for a model with point mutation in the metacom-munity and dispersal limitation.The choices for the metacommunity modelare:’pm’(point mutation),’rf’(randomﬁssion),’pr’(protracted speciation),’dd’(density-dependence).The choices for the local community model are:’dl’(dispersal limitation),’dd’(density-dependence).mult When set to’single’,the loglikelihood of a single abundance vector will be com-puted When set to’mg’the loglikelihood for multiple guilds is computed.Whenset to’ms’the loglikelihood for multiple samples from the same metacommu-nity is computed.When set to’both’the loglikelihood for multiple guilds withinmultiple samples is computed.nsim Number of simulations to performDetailsNot all combinations of metacommunity model and local community model have been implemented yet.because this requires checking for numerical stability of the integration.The currently available model combinations are c(’pm’,’dl’).Valueabund abundance vector,a list of abundance vectors,or a list of lists of abundance vectors,or a list of lists of lists of abundance vectors Theﬁrst layer of the lists corresponds to different simulations When mult=’mg’,each list contains a list of abundance vectors for different guilds.When mult =’ms’,each list contains a list of abundance vectors for different samples from the same meta-community.In this case the vectors should have equal lengths and may contain zeros because there may be species that occur in multiple samples and species that do not occur in some of the samples.When mult=’both’,each list will be a list of lists of multiple guilds within a sampleReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution8:1506-1519.doi:10.1111/2041-210X.12807SADISA_test Tests SADISA for data sets included in the paper by Haegeman&Eti-enneDescriptionTests SADISA for data sets included in the paper by Haegeman&EtienneUsageSADISA_test(tol=0.001)Argumentstol tolerance of the testReferencesHaegeman,B.&R.S.Etienne(2017).A general sampling formula for community structure data.Methods in Ecology&Evolution.In press.Index∗datasetsdatasets,2fitresults,3∗modelSADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,9∗species-abundance-distributionSADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,9convert_fa2sf,2datasets,2fitresults,3integral_peak,4SADISA_loglik,5SADISA_ML,6SADISA_sim,8SADISA_test,911。

Random Dispersal in Theoretical Populations在随机种群扩散理论-精选文档

Random Dispersal in Theoretical Populations
By: J.G. Skellam
J.G. Skellam
“Traditional
biology course lay far too much emphasis on the direct acquisition of information. Insufficient attention is given to the interpretation of facts or to the drawing of conclusions from observations and experience. The student is given little opportunity to apply scientific principles to new situations.”
Random?
From
the perspective of Skellam the best way to understand the random dispersal amongst populations was by first understanding the principle of random walks. So as a reminder of what a random walk is: A random process consisting of a sequence of discrete steps of fixed length.
Skellam’s Perspective
With
regards to random walks, Skellam proposed the following: Consider a plane using the Euclidean coordinate system. In the immediate neighborhood of the origin let there be a particle that tends to leave the origin to gradually form a circular representation of the previous graph.

时间测度上具有时滞的互惠系统的周期解

时间测度上具有时滞的互惠系统的周期解鲁红英【摘要】互惠相互作用关系是生物种群之间相互作用的基本关系之一,是生态学、生物数学的研究热点.2种群互惠系统是指每一种群的存在对另一种群的增长都会起促进作用的系统.由于时滞对一系统所带来的影响,在自然现象中是屡见不鲜的.因此,生态系统中,为了更真实的反应自然,时滞是一种不应忽略的因素.时标理论的提出,整合和统一了连续与离散的分析.因此时标上的动力系统更为一般,包含微分方程与差分方程作为它的特例.在时间测度上研究了具有时滞的两种群互惠系统,利用重合度理论中的延拓定理讨论此系统周期解的存在性问题,从而使这一类系统的连续时间情形与离散时间情形的周期解存在性问题得到了统一研究.并且所获得的周期解存在性定理,推广了文献[15]的主要结果.%Mutual interaction, which is one of the basic relationships between populations, has long been dominant themes in both ecology and mathematical ecology. Mutualism, an interaction of two-species of organisms that benefits both, is found in many type of communities. Since time delays occur so often in nature, a number of models in ecology can be formulated as systems of differential equations with time delays. So, time delay is a factor that should not be ignored. The theory on time scales unified analysis of continuous process and discrete process. Therefore, the dynamic equations on time scales are more general, including differential equations and difference equations as special cases. In this paper, the existence of periodic solutions for a delayed mutualism system on time scales is considered. By using the continuation theorem of coincidence degree,a set of sufficient conditionswhich ensure the existence of periodic solutions of the system are obtained. So, the study of existence of periodic solutions for the continuous differential equations and discrete difference equations are unified. In addition,The existence theorem of periodic solutions generalized the main results in [15].【期刊名称】《沈阳师范大学学报（自然科学版）》【年(卷),期】2011(029)002【总页数】5页(P165-169)【关键词】时间测度;互惠;时滞;周期解【作者】鲁红英【作者单位】东北财经大学,数学与数量经济学院,辽宁,大连,116025【正文语种】中文【中图分类】O175.1生态系统的动力学行为一直是生态数学的一个重要的研究问题,许多现象可以表达为微分方程或差分方程,近年来有许多作者对此进行了深入的研究,获得了很好的结果[1-18]。

随机利率下分数跳扩散Ornstein-Uhlenbeck期权定价模型

严惠云，曹译尹
（．西安财经学院统计学院，西西安７００；．北电力大学经济管理系，１陕１１０２华河北保定０１０）７００
摘
要
假设股票价格遵循分数布朗运动和复合泊松过程驱动的随机微分方程，期利率服从Ｈｕｌ短ｌ —
第２８卷
第２期
经
济
数
学
Ｖｏ１２８，．Ｎｏ．２
２１年６月０１
ｊｏＵＲＮＡＬＯＦＱＵＡＮＴＩＴＡＴＩＯＮＯＭＩＶＥＥＣＣＳ率下分数跳一散扩Ｏｒｓｉ－ｈｅｂｃｎｔｎＵｌｎｅｋ期权定价模型ｅ
ＡｂｔａｔｓｒｃＵｎｅｈｓｕｔｎｈｔｓｏｋｒｃｒｃｓｓｄｉｅｙｆａｔｎｌｉｕｉｎｐｏｅｓｗｉｈｎｎｈｍｏｅｅｄｒｔｅａｓｍｐｉｓｔａｔｃｓｐｉｅｐｏｅｓｉｒｎｂｒｃｉａｆｓｒｃｓｔｏ — ｏｇｎ — ｏｖｏｄｆｏ－－
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具有较好地“ 尾” 长程依赖特性，且仍然是一厚和而
ｕｄｒｓｏｈｓｉｎｅｅｔｒｔｓｗａｕｌ．Ｕｓｎｈｓｃｌｐｏａｉｓｉｍｅｓｒｆｐｉｅｐｏｅｓａｄｔｅｐｉｃｐｅｏａｒｐｅ — ｎｅｔｃａｔｉｔｒｓａｅｓｂｉｃｔｉｇｐｙｉａｒｂｂｌｔａｕｅｏｒｃｒｃｓｎｈｒｎｉｌｆｆｉｒｍｉｉｃｕ，ｔｅｐｉｉｇｆｒｌｏｒｐａｐｉｎｗａｂａｎｄｗｈｃｅｅａｉｅｈｌｃ — ｃｏｅｄ１ｍｈｒｃｎｏｍｕａｆＥｕｏｅｎｏｔｓｏｔｉｅ，ｉｈｇｎｒｌｓｔｅＢａｋＳｈｌｓｍｏｅ．ｏｚ

永动机英语作文

永动机英语作文In the realm of theoretical physics, the concept of aperpetual motion machine has long captivated the imaginations of inventors and laymen alike. The idea of a device thatcould operate indefinitely without an external energy source is, at its core, a tantalizing proposition. However, the English language, with its rich lexicon and precise terminology, allows us to articulate the nuances of why sucha machine remains a myth.The term "perpetual motion" is derived from the Latin "perpetua," meaning continuous, and "motus," meaning movement. It is a testament to the English language's ability to borrow and adapt words from other languages to describe complex scientific concepts. In English essays and scientific literature, the discussion around perpetual motion oftendelves into the principles of thermodynamics, which are elegantly expressed through English terms such as "energy," "work," and "entropy."The first law of thermodynamics, known as the law of conservation of energy, is succinctly captured in the phrase "energy cannot be created or destroyed, only converted from one form to another." This law, when articulated in English, underscores the fundamental challenge of perpetual motion machines—they defy the conservation of energy by suggestinga system that creates more energy than it consumes, which isa physical impossibility.Moreover, the second law of thermodynamics introduces the concept of entropy, or the measure of disorder in a system.In English, we describe this law as one that dictates the natural progression of energy dispersal, stating that"entropy of an isolated system not in equilibrium will tendto increase over time." This further challenges the notion of a perpetual motion machine, as it implies that no machine can operate without eventually succumbing to increased disorder and energy loss.The English language also provides us with a platform to discuss the historical attempts at creating perpetual motion machines, often referred to as "perpetual motion machines of the first kind" (PM1K), which violate the first law of thermodynamics, and "perpetual motion machines of the second kind" (PM2K), which theoretically comply with the first lawbut violate the second. These classifications are a testament to the precision and clarity that English can offer inscientific discourse.In conclusion, the English language serves as a powerful tool in dissecting the complexities of scientific theories and principles. The concept of perpetual motion, while alluringin its simplicity, is effectively debunked through the structured and logical framework that English provides. It is through this language that we can convey the intricatedetails of why perpetual motion machines remain a fascinating but unattainable goal in the quest for scientific advancement.。

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Contrary to popular belief, Lorem Ipsum is n't simply random text. It has roots in a piece of classical Latin literature from 45 BC.
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Contrary to popular belief, Lorem Ipsum is n't simply random text. It has roots in a piece of classical Latin literature from 45 BC.

生物学导论biology16

Support a biomass of 2 kilograms of fish
Carrying capacity is not an inflexible number
16.5 Categories of limiting factors

Availability of raw materials

Population density 种群密度: the number of organisms of a species per unit area Population pressure 群体压力: increases in the intensity of competition that cause changes in the environment and lead to dispersal

Different ways of approaching reproduction:
Spend large amounts of energy on producing gametes and young: dandelion Produce relatively fewer individual but provide care and protection to young: human

Available raw materials
Food
There
are many reason why the people can’t get food or won’t eat: culture, political, economic, social issue Whether the world can continue to produce enough food

环境生态学---第十三章生活史

种群大小
种内种间斗争选择有利于
寿命导致
r-选择
k-选择
多变,难以预测和不确定
稳定，可预测，较确定
常是灾难性的，无一定规律性，非密度制约的
比较具有规律性，密度制约的
幼体存活率很低，
幼体存活率高
时间上变动大，不稳定，通常低于环境容纳K值，群落不饱和，生态上真空，每年有再移植变动性大，通常不紧张
● 迁入（immigration）——进入的单方向移动。 ● 迁移（migration）——周期性的离开和返回。
（回游、迁徙）
9
③ 动植物扩散的生物学与生态学意义
● 可以使种群内和种群间的个体得以交换，防止长期近亲繁殖而产生不良的后果；
● 可以补充或维持在正常分布区以外的暂时性分布区域的种群数量；
17
繁殖策略
• r – 选择：有利于增大内禀增长率的选择称为r-选择。r-选择的物种称为r-策略者（r-
strategistis）。
• K –选择：有利于竞争能力增加的选择称为 K-选择。K-选择的物种称为K-策略者（K-
strategistis）。
18
r-选择者
r选择者
K-选择者
19
气候死亡率存活
21
生殖价
年龄x的个体的生殖价（RVx）是该个体马上要生产的后代数量加上预期的其在以后的生命过程中要生产的后代数量。个体的生殖价在出生后必然会上升，然后随年龄老化而下降。个体间生殖价的差异提供了一个强有力的生活史对策预报器。
22
生殖价和生殖效率
生殖价随年龄、环境而变化。
天蓝绣球
23/24 23
生物种类、体重、性别、饵料性质、理化因子等影响能量收支。

河北省2024-2025学年高三上学期9月月考英语试题

河北省2024-2025学年高三上学期9月月考英语试题一、听力选择题1．What will the man probably do next?A．Make a cake.B．Take part in a race.C．Stop at the supermarket. 2．What does the man advise the woman to do?A．Take a few risks.B．Watch out for potential dangers.C．Avoid harming the natural system.3．What does the man intend to do?A．Buy a house.B．Expand his house.C．Advertise his house. 4．What are the speakers talking about?A．Drink orders.B．Items on the menu.C．Their favorite fruit. 5．Who is Elle most likely to be?A．Elena’s sister.B．John’s daughter.C．John’s elder sister.听下面一段较长对话，回答以下小题。

6．What do we know about Rob Brown?A．He will graduate next year.B．He takes an interest in cooking.C．He’s dissatisfied with Stacy’s service.7．What problem does Stacy find out?A．Rob clicked the wrong birth date.B．Rob selected the wrong year for his class.C．Rob didn’t know how to register for the course.听下面一段较长对话，回答以下小题。

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It then produces acorns over a period of several hundred years.
Obviously not all the acorns grow to produce more Oak trees:
Some are – eaten by mammels, fail to germinate, or are simply overshadowed by the larger mature trees.
Random?
From the perspective of Skellam the best way to understand the random dispersal amongst populations was by first understanding the principle of random walks.
The bug example continued…
The probability that after 6 months the beetle wanders more than a mile from the starting point is less than 8 in a million (wow, wonder how he figured that out?).
It seems that only 1% of the seedlings are likely to survive the next three years.
It is also safe to assume that the oak population is no more than 9 million.
This law may be written in the form: dN/dt = cN – lN^2
Almost done, really…
“In practice there is rarely sufficient information to construct the contours of population density with accuracy…”
Without external aid a period of time equivalent to 1000000 seasons would be required to raise RMSD.
Soooo, basically as RMSD increases a great deal the particle or in this case wingless beetle comes a great deal nearer the origin than the farthermost position previously reached.
uuuggg
Now, this might seem to resemble the concept of Brownian Motion of a particle in a viscous substance but here in lies the difference:
“The distribution of the position of a particle of the nth generation with be henceforth”
The results of the particle motion can be made applicable to the dispersal of small animals such as worms and snails.
A bug example: If the random mean square dispersion
It turns out the problem that is being referred to is having to do with the Oak tree.
Oaky Doky
The oak does not produce accorns until it is sixty or seventy years old and even then it is not mature.
We then have R/a < 300 sqrt(log 9,000,000) = 1200.
In the original form of the problem as stated by Reid, R is given as 600 miles
Lastly..
It then follows that the rootmean square distance of daughter oaks about their parents is greater than ½ a mile and that agents such as small fuzzy wuzzies (aka mammals and birds) played a major role in the dispersal of this population.
Random Dispersal in Theoretical Populations在随机种群扩散理论
J.G. Skellam
“Traditional biology course lay far too much emphasis on the direct acquisition of information. Insufficient attention is given to the interpretation of facts or to the drawing of conclusions from observations and experience. The student is given little opportunity to apply scientific principles to new situations.”
Soooooo?
So from what we have gathered thus far is that an organism or particle with tend to move away from its origin in a semicircular pattern.
From the previous equations we are then able to calculate its probable whereabouts with regards to random distributing.
Even more uuuggg
Skellam’s polar transformation of this particle positioning of ngenerations turned out to be the following:
Are we getting anywhere with this?
As long as the population is small of shows a natural tendency to decrease, the Malthusian law dN/dt =cN is usually satisfactory.
If the population is not small the Pearl-Verhulst logistic law is more appropriate.
Well that’s about it!
Alas!! Integrating over θ gives us the radical probability density:
a^2 = the mean-square dispersion per generation analogous with the mean-square velocity in Maxwell’s distribution.
Interesting..
“of the population spread out after ngenerations that proportion lying outside a circle of radius R is:”
Awww wook at the fuzzy wuzzies
Buuuuuuut here is a well illustrated spread of the muskrat in central Europe since its introduction in 1905.
If we are prepared to accept a boundary as being representative of a theoretical contour, then we must regard the area enclosed by that boundary as an estimate of pi*r^2
Skellam’s Perspective
With re the following:
Consider a plane using the Euclidean coordinate system.
In the immediate neighborhood of the origin let there be a particle that tends to leave the origin to gradually form a circular representation of the previous graph.
TIMBER!!!
Skellam makes reference to Reid’s Problem:
“We can clearly establish a rigorous conclusion in the form of an equality provided that we can fix appropriate bounds to various parameters.”