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Phillips SJ, Anderson RP, Schapire RE.. Maximum entropy modeling of species geographic distribution. Ecol Model 19: 231-259

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Abstract

The 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 presence/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 10 random 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.

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... Calibration, evaluation, and selection of ENMs were performed using the kuenm package [62]. Using the maxent algorithm, this package executes the calibration and constructs the models [63] through the dismo package in R [64]. Models were calibrated with 'kuenm_cal' function combining regularization multipliers one by one up to four, 10,000 background points and establishing the "basic" option for the argument of 'features' to be taken into account (seekuenm's help in R). ...
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Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local‐scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating preexisting range information into MSOMs, yielding a “biogeographic multi‐species occupancy model” (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey and entire avian communities in forests and pastures of Colombia's West Andes. Compared with traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species‐specific inference even for never‐observed species. Incorporating preexisting range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false absences are modeled in an occupancy framework. Incorporating pre‐existing range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false‐absences are modeled in an occupancy framework.
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Habitat suitability models based on particular environmental variables are increasingly being used to predict occurrence of species for wildlife management issues. A variety of techniques and statistical methods are used in species distribution modelling. In this case we use MaxEnt and data on the distribution of snow leopard in Nepal based on a large set of occurrence data collected from a much wider range of areas (9 districts) than in the previous studies. We used camera traps, scat collections and monitoring of fresh pugmarks and scrapes. All our data based on scats were consistently genotyped to avoid misidentification of the species that produced them. All fresh pugmarks and scrapes were verified whether they originate from snow leopard by using movement pattern of snow leopard from camera trap data. Altitude and annual mean temperature are important common factors contributing to snow leopard habitat suitability within the area studied, indicated by both the percentage contribution of environmental variables and jackknife test from MaxEnt model. Some other uncommon factors also seem to play a role as they were important in at least one of the analyses. These were: distance from roads and precipitation of the driest month; however, their importance has to be considered with caution. To conclude: the habitat suitability models indicate that the main danger for snow leopard survival may be climate change and human expansion. Both these phenomena will push the lower limit of its distribution upwards to higher elevations, which will entail two negative effects.
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Rampant deforestation, industrialization, modernization, population explosion, and overexploitation of natural resources have increased the intensity of climate change. Global climate change is evidenced by a continuous rise in temperature, which in the last century, rose to 0.6°C. Nevertheless, this temperature rise is also evidenced in the Himalayas, more so than the global average (Shrestha and Aryal, Reg Environ Chang 11:65–77, 2011). Climate change has far-reaching impacts on all forms of life and ecosystems, including mountain systems. There are several ways to interpret these climatic changes on plant invasions and their impacts on ecosystems. The impacts of climate change on global mountain ecosystems have become a common phenomenon, with frequent occurrences of glacier melting and land degradation (Hamid et al. Front Plant Sci 11:421, 2020). These effects are common to vegetation, with high mountains being more vulnerable (Spence and Mahaney (1989) Plant succession on glacial deposits of Mount Kenya, East Africa. In: Mahaney WC (ed) Quaternary and environmental research on East African mountains, Balkema, pp 279–290; Theurillat and Guisan Climatic Change 50(1):77–109, 2001; Beniston (ed) (2002) Mountain environments in changing climates. Routledge). The global temperature has experienced a rise of 0.6°C in the last century, with evidence of a rise of 2°C in annual minimum temperatures in the European Alps (Beniston (1997) Variations of snow depth and duration in the Swiss Alps over the last 50 years: links to changes in large-scale climatic forcings. In Climatic change at high elevation sites (pp. 49–68). Springer, Dordrecht). This global temperature rise in mountains drives alpine vegetation to higher elevations (Salick et al., 2009; Root et al., 2003; Sattar et al., 2021). The Himalayas is experiencing a temperature rise higher than the global average, with evidence of similar changes in the future with its days and nights becoming warmer, and cold days and nights less frequent. But the climate change impact assessment on Himalaya-specific studies is not well known (Immerzeel et al. Science 328(5984):1382–1385, 2010; Chaudhary and Bawa, Biol Lett 7(5):767–770, 2011). No specific emphasis is given to distinguishing the Western Himalayas with a greater sensitivity to temperature fluctuations (Baidya et al. J Hydrol Meteorol 5(1):38–51, 2008; Dimri and Dash, Climatic Change 111(3):775–800, 2012) from the Eastern Himalayas to changes in rainfall regime (Pandey and Bardsley, Appl Geogr 64:74–86, 2015) despite significant socio-economic and environmental consequences (Chaudhary and Bawa, Biol Lett 7(5):767–770, 2011); Huettmann and Regmi, 2020).Invasive species increase the vulnerability of native species by weakening their biotic resistance to climate stressors. Consequently, the range of native species reduces and the species composition of a natural ecosystem alters (Burgiel and Muir (2010) Invasive species, climate change and ecosystem-based adaptation: addressing multiple drivers of global change global invasive species program, GISP United Nations Avenue P.O. Box 633–00621, Nairobi, Kenya, pp 56; Diez et al., 2012; Taylor and Kumar, J Environ Manag 114:414–422, 2013); Panda et al., 2018). Invasive species modify the ecosystem properties that have traditionally been thought to be uncommon in natural systems (Gordon, Ecol Appl 8(4):975-989, 1998). The invasive species alter the ecosystem processes (Vitousek, Oikos 57:7–13, 1990); Raizada et al. Proc Natl Acad Sci India Sect B – Biological Sciences, 78 nPART:4288–4298, 2008), community structure (Hejda et al. J Ecol 97:393−403, 2009a, Hejda et al. Glob Ecol Biogeogr 18(3), 372–382, 2009b); Vila et al. Proc R Soc B Biol Sci 276(1674):3887–3893, 2009), and native species habitats (Cox (2004) Alien species and evolution: the evolutionary ecology of exotic plants, animals, microbes, and interacting native species. Island Press) and disrupt the ecological integrity and energy flow of native ecosystems (Pimentel et al. Agric Ecosyst Environ 84(1):1–20, 2001). Invasive species occupy the space created by glacier melting and human-mediated disturbances such as deforestation, tourism development, urbanization, overgrazing, and frequent movement by local people for fuelwood collection and the establishment of hydropower projects in mountain areas. But the magnitude and severity of climate change effects on plant invasions are unique for individual species (Pearson et al. Ecol Model 154:289−300, 2002), where the plasticity of a species to adapt to envermental challenges determines its success. Assessing these invasion risks are crucial for biosecurity, sustainable biodiversity, ecosystem restoration, and conservation prioritization (Thuiller et al. Ecography 27:165–172, 2004; Rhodes et al. Conserv Biol 20(2):449-459, 2006; Vaclavik and Meentemeyer, Ecol Model 220:3248–3258, 2009); Kriticos and Leriche, Ecography 33:115–127, 2010). In recent years, systematic studies on the impacts of plant invasions have been picked up, but most of the studies focus on regional and continental scales. Mountain-specific studies are limited, where data availability is a major constraint (IPCC, 2007). The Himalayas offers ample scope to study such climate change impacts on plant invasions and this chapter highlights the probability of plant invasions to the Indian Himalayan ecosystem.KeywordsGeneralized linear model Impact and Mitigation Intermediate disturbance hypothesis Invasion risks Maxent Native species Range size Species distributionThermophillic species
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Data, data collection, selection, and methodology play key roles in ecological analysis. Data collected by appropriate methodology, and with the proper organization are essential for good interpretation. Based on the scientific problem to answer, data collection procedures are designed for sampling. And an appropriate sampling method solves major parts of the research and the results are considered logical. Therefore, quality data determines the accuracy of the research and ecological interpretations, in particular. However, it is not always possible to preplan data collection; and suitable data for analysis need preprocessing. Preprocessing looks into the quality aspects of the data and helps in preparing a reasonable dataset for analysis. It involves the careful removal of correlated variables, replacement or removal of missing values, transformations, and feature selection. Data types and sources are also crucial for quality research, where a scientific approach to feature optimization matters a lot. This chapter discusses all these issues related to proper data selection and modeling in ecological analysisKeywords Data filtering Decision tree Multicollinearity Principal component analysis Species distribution modelling
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Climate change is affecting biodiversity by altering the geographical distribution range of species, and this effect is amplified in climate-sensitive areas. Studying the geographic distribution of flagship species in response to climate change is important for the long-term conservation of species and the maintenance of regional biodiversity. Therefore, we collected field survey records from 2016 to 2020 and conducted field surveys of black-necked cranes in the Shaluli Mountains (SLLMs) in May–June and August–October 2021; 103 breeding records were acquired totally, and the geographical distribution range under the current and four future climate scenarios was modeled with the MaxEnt model to predict the impact of climate change on its distribution and habitat quality. The results showed that 152 black-necked cranes were surveyed in seven counties of SLLMs in total; the estimated number of black-necked cranes in the entire SLLMs was about 200. The currently suitable habitat area is 27,122 km2, mainly distributed in gentle meadows and wetland habitats along the lake where the Annual Mean Temperature is −1 °C and the Mean Diurnal Range (16 °C) and Precipitation Seasonality (105) are comparatively large. Furthermore, the breeding range would expand to varying degrees under future climate scenarios and showed a migration trend toward the northwest and higher elevation. Besides, as time goes by, the habitat for black-necked cranes in SLLMs would become more homogeneous and more suitable. The conservation effectiveness of the existing reserve network would keep stable with climate change, although there are large conservation gaps between protected areas, and these gaps will gradually expand over time. Overall, this study provides a preliminary understanding of the population and distribution and predicts the future distribution of black-necked cranes in the SLLMs. It also demonstrates the importance of SLLMs for protecting the central population of black-necked cranes and maintaining regional biodiversity. Therefore, we recommend long-term monitoring and conservation of the black-necked crane population and wetland resources in the region.
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Many museums and academic institutions maintain first-rate collections of biological materials, ranging from preserved whole organisms to DNA libraries and cell lines. These biological collections make innumerable contributions to science and society in areas as divergent as homeland secu- rity, public health and safety, monitoring of environmental change, and traditional taxonomy and systematics. Moreover, these collections save governments and taxpayers many millions of dollars each year by effectively guiding government spending, preventing catastrophic events in public health and safety, eliminating redundancy, and securing natural and agricultural resources. However, these contributions are widely underappre- ciated by the public and by policymakers, resulting in insufficient financial support for maintenance and improvement of biological collections.
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This paper demonstrates the use of a bioclimatic model mapped over geographical regions as a tool for spatially refined risk assessment for the establishment of non-indigenous plants with invasive behaviour. Drawing on the relationship between plant distribution and climate, the approach uses gridded spatial interpolated monthly means of temperature and precipitation linked with accurate maps of general native distribution ranges to predict the long-term potential of a plant species to invade a certain region. The ascertained potential for establishment is illustrated by the example of garlic mustard (Alliaria petiolata[M. Bieb.] Cavara & Grande) in North America. The first step is to calculate and visualize the number of populated grid cells along climatic gradients in frequency diagrams for the general native distribution range. Interpretations of the response curves recorded are used for assessing apparent climatic range boundaries. Modelling was gradually optimized based on the results of experience-based interpretations and by examining omission and over-representation errors. The obtained climatic model of the range of A. petiolata shows considerable congruencies with its mapped, native Eurasian range. Degrees of climatic similarity between North America and the native range of A. petiolata were calculated with the help of GIS methodology and were used to assess the regionally different likelihood of establishment in North America of the invasive species under consideration.
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The development of quantitative models of species’ distributions has largely ignored the potential for intraspecific variation in species’ niche requirements. Application of such models may nevertheless provide a rich, untapped opportunity to address the basic issue of niche conservatism vs. evolution. We illustrate this potential using genetic algorithms coupled with geographical information systems, which provide a powerful and novel approach to characterizing species’ ecological niches and geographical distributions. Our example consists of several species of Mexican birds with recognized subspecies, and associated climatic and vegetation data. Our basic protocol is to develop an ecological niche model for each subspecies, and use this model to predict distributions of other subspecies. In some cases, the ecological niche model inferred for one subspecies provides an excellent descriptor of other subspecies’ ranges, whereas in other cases the prediction is rather poor. We suggest that the latter may reveal the potential existence of evolved, intraspecific niche differentiation. We discuss alternative, non-evolutionary explanations, and point out potential implications of our results for predictive models of species’ invasions.
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Natural history museums have been linked since their creation with universities. These museums have important role as support in research and knowledge on biodiversity, and teaching at higher levels. The current advances in computer applications are opening the vision of new opportunities to the natural history museums, differing from the traditional methods of work. The use of this new tools will expand the future of these institutions. The program of development of the Museum of Biology of the Universidad Central de Venezuela, can be used as a model for developing countries in the XXI century. The program is directed toward the formation of interdisciplinary teams, in order to apply the advances in computer sciences in order that natural history museums can play a preponderant role in the advance of science.
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This study investigates, on a continent-wide scale, which environmental factors associate with the distributional boundaries of wintering North American avifauna. Distribution and abundance maps of 148 land birds wintering in the contiguous United States and southern Canada are compared with maps of six environmental factors: average minimum January temperature, mean length of frost-free period, potential vegetation, mean annual precipitation, average general humidity, and elevation. The species maps were generated from 10 years of National Audubon Society's Christmas Bird Count data. The comparisons reveal that average minimum January temperature, mean length of frost-free period, and potential vegetation frequently associate with the northern range limits of wintering species (60.2%, 50.4%, 63.7%, respectively). Only two factors, potential vegetation and mean annual precipitation, are coincident with a large proportion of species' eastern range boundaries (62.8% and 39.7%, respectively). The frequency of association between the environmental factors and the western boundaries of the species' ranges is lower than in the other two directions, and more factors exhibit a correspondence. These include potential vegetation (46.0%), mean annual precipitation (36.0%) and elevation (38.0%). Associations with species' southern boundaries are ignored, because 89.2% of the species have range limits abutting the edge of the study area. Only 0.6% of the possible associations between the species' ranges and environmental factors are expected to occur by chance. To enhance the understanding of the correspondence between species' range limits and environmental factors, four mutually exclusive feeding guilds are examined. These include Raptors and Shrikes, Bark Gleaners, Foliage Gleaners, and Seed Eaters. Predictably, both temperature factors, minimum January temperature and mean length of frost-free period, associate with northern range limits of many species in all four guilds (Raptors and Shrikes, 54.5% and 36.4%; Bark Gleaners, 46.1 and 46.1%; Foliage Gleaners, 86.4% and 68.2%; and Seed Eaters, 60.0% and 56.0%, respectively). Isopleths of mean lenght of frost-free period also coincide with the western boundaries of Foilage Gleaners (55.6%). Potential vegetation corresponds with the northern distributional boundaries of a large proportion of species in all four guilds (Raptors and Shrikes, 54.5%; Bark Gleaners, 76.9%; Foliage Gleaners, 68.2%; Seed Eaters, 68.0%), while along the eastern and western boundaries, vegetation is coincident with members of the two guilds most strongly linked with vegetation: Bark Gleaners (East 88.9% and West 66.7%) and Seed Eaters (East 62.5% and West 42.9%). The western range edges of the Bark and Foliage Gleaners associate with elevation (44.4% and 44.4%).
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Natural-history collections in museums contain data critical to decisions in biodiversity conservation. Collectively, these specimen-based data describe the distributions of known taxa in time and space. As the most comprehensive, reliable source of knowledge for most described species, these records are potentially available to answer a wide range of conservation and research questions. Nevertheless, these data have shortcomings, notably geographic gaps, resulting mainly from the ad hoc nature of collecting effort. This problem has been frequently cited but rarely addressed in a systematic manner. We have developed a methodology to evaluate museum collection data, in particular the reliability of distributional data for narrow-range taxa. We included only those taxa for which there were an appropriate number of records, expert verification of identifications, and acceptable locality accuracy. First, we compared the available data for the taxon of interest to the “background data,” comprised of records for those organisms likely to be captured by the same methods or by the same collectors as the taxon of interest. The “adequacy”of background sampling effort was assessed through calculation of statistics describing the separation, density, and clustering of points, and through generation of a sampling density contour surface. Geographical information systems (GIS) technology was then used to model predicted distributions of species based on abiotic (e.g., climatic and geological) data. The robustness of these predicted distributions can be tested iteratively or by bootstrapping. Together, these methods provide an objective means to assess the likelihood of the distributions obtained from museum collection records representing true distributions. Potentially, they could be used to evaluate any point data to be collated in species maps, biodiversity assessment, or similar applications requiring distributional information.
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Applications of Hutchison's n-dimensional niche concept are often focused on the role of interspecific competition in shaping species distributions patterns. In this paper, I discuss a variety of factors, in addition to competition, that influence the observed relationship between species distribution and the availability of suitable habitat. In particular, I show that Hutchison's niche concept can be modified to incorporate the influences of niche width, habitat availability and dispersal, as well as interspecific competition per se. I introduce a simulation model called NICHE that embodies many of Hutchison's original niche concepts and use this model to predict patterns of species distribution. The model may help to clarify how dispersal, niche size and competition interact, and under what conditions species might be common in unsuitable habitat or absent from suitable habitat, ways predicted by theory. However, most tests of niche theory are hampered by inadequate consideration of what does and what does not constitute suitable habitat. More conclusive evidence for these predictions will require rigorous determination of habitat suitability under field conditions. I suggest that to do this, ecologists must measure habitat specific demography and quantify how demographic parameters vary in response to temporal and spatial variation in measurable niche dimensions.
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With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone.
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. Generalized additive models (GAMs) are a non-parametric extension of generalized linear models (GLMs). They are introduced here as an exploratory tool in the analysis of species distributions with respect to climate. An important result is that the long-debated question of whether a response curve, in one dimension, is actually symmetric and bell-shaped or not, can be tested using GAMs. GAMs and GLMs are discussed and are illustrated by three examples using binary data. A grey-scale plot of one of the fits is constructed to indicate which areas on a map seem climatically suitable for that species. This is useful for species introductions. Further applications are mentioned.
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The monthly entropy fluxes associated with direct, diffuse and reflected solar radiation in Lake Mendota are calculated from the corresponding energy data of the lake. Also, the monthly entropy fluxes associated with infrared radiation, evaporation and sensible heat are estimated. The net entropy flow into the lake becomes negative; that is, the lake absorbs ‘negative entropy’ from its surroundings. In this respect, the lake can be regarded as a ‘superorganism’, which has ordered structures and functions in it similar to a biological organism. The change of entropy content is computed from the change of heat-storage and the mean temperature of the lake. From the net entropy flow and the change of entropy content, the entropy production is calculated. Entropy production is large in summer and small in winter; it oscillates with a period of 1 year. The monthly entropy production (Sprod) is a linear function of the monthly solar energy absorbed by the lake (Esolar):Sprod=a+bEsolar, a=0.006 MJ m−3 month−1 K−1 and b=2.29 × 10−4 m−1 K−1. The values (a, b) are holistic indices which characterize the lake from an entropy viewpoint. Other indices of similar nature are also introduced. Comparison of these quantities in Lake Mendota (eutrophic) with those in the northern basin of Lake Biwa (oligo-mesotrophic) shows that processes of eutrophication or succession are accompanied by the increase of magnitudes of these indices that are related to entropy production. The increase of entropy production will be an entropy principle in some stage of ecological succession in lakes, and also in other ecological systems. A hypothesis is presented: the whole span of ecological succession consists of three phases: developmental stage (early stage) in which entropy production increases with time, stationary stage (intermediate stage) in which entropy production is kept constant, and senescent stage (later stage) in which entropy production decreases with time. These trends will be applied to Lotka-Odum's power and also Hirata-Ulanowicz's ascendency.
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de de J JJ JJaneir aneir aneir aneir aneiro o o o o, Br Br Br Br Brazil. azil. azil. azil. azil. This study was conduced in a second growth woodland close to Itatiaia National Park (22º30'S e 44º30'W), Rio de Janeiro state, Brazil, and aimed to describe the understory bird community living in this area. We sampled three different vegetation types, reforestation, wood and orchard, through 19 field trips between 1984 and 1999. Birds were sampled by mist-netting and bird-banding, using from 15 to 31 12 x 2 36 mm mesh mist- nets, and we also collected information on morphological (total, wing, bill, tail and tarsus length), and biological data (sex, age, reproduction and molt). After 5,621.79 net hours, we got 553 captures and 71 recaptures (12.8%); 417 individuals were banded and 65 hummingbirds were not, due to the absence of specific rings. The studied community was represented by 77 species and 18 families, showing a diversity index of H' = -1.59 and curve of new species tending to stabilization. The most well-represented families were Emberizidae (n = 21; 27.27%), and Tyrannidae (n = 15; 19.48%); the species with highest number of capture were Turdus leucomelas Vieillot, 1818 (n = 40; 9.59%) and Turdus rufiventris Vieillot, 1818 (n = 36; 8.63%). Six of the sampled species (7.8%) are endemic to Atlantic forest. In the rainy season we sampled 68 species, and in the dry season, 42; and the captures were correlated with rainfall (rS = -0.68; p = 0.05). The breeding season occurred from October to March, and also was correlated with the beginning of the rainy season (rS = -0.70; p = 0.05).
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AimA hierarchical framework is presented for modelling the spatial distribution of terrestrial vertebrate animals.LocationThe location of the study is the montane ash forests of the Central Highlands of Victoria, south-eastern Australia.Methods The framework is illustrated using as a case study the distribution of Leadbeater’s Possum [Gymnobelideus leadbeateri McCoy, 1867, (Marsupialia: Petauridae)], a small arboreal marsupial. The framework is based upon quantifying the environmental response of a species in terms of a five-level environmental hierarchy defined by scales (global-, meso-, topo-, micro- and nano-scales) that represent natural breaks in the distribution and availability of the primary environmental resources. Animal response is examined in terms of a species’ distribution as observed in four biological units (the species in toto, meta-population/population, group/colony, and individual organism). We define the spatial occurrence and abundance of the target species in each of these units as its ‘distributional behaviour’.ResultsPredictions of the potential spatial distribution of Leadbeater’s Possum are presented at meso-, topo-, micro- and nano-scales. These spatial predictions utilize Geographical Information System (GIS)-based spatial models of long term mean monthly climate and terrain-modified surface radiation, together with vegetation cover and individual tree attributes from air-photo interpretation and field survey.Main conclusionsIdeally, species’ responses at each level in the environmental hierarchy should be empirically derived using statistical models based on field observation of a species’ distribution and abundance. Spatial modelling of species’ responses becomes problematic at finer scales because of the lack of suitable environmental data. The key characteristics of the modelling framework are generic, but the influence of additional scales and processes will be important in other ecosystems and species.
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Excerpt This concluding survey1 of the problems considered in the Symposium naturally falls into three sections. In the first brief section certain of the areas in which there is considerable difference in outlook are discussed with a view to ascertaining the nature of the differences in the points of view of workers in different parts of the field; no aspect of the Symposium has been more important than the reduction of areas of dispute. In the second section a rather detailed analysis of one particular problem is given, partly because the question, namely, the nature of the ecological niche and the validity of the principle of niche specificity has raised and continues to raise difficulties, and partly because discussion of this problem gives an opportunity to refer to new work of potential importance not otherwise considered in the Symposium. The third section deals with possible directions for future research. The Demographic
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Bell System Technical Journal, also pp. 623-656 (October)
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The geographical limits of Nothofagus cunninghamii are highly correlated with climate and appear to be more or less in equilibrium with the climate of the present century in all but one of the areas of its present range. It is suggested that suitable climates for the species occur in the highlands of northeastern Victoria and southern New South Wales, beyond its present range, and it is possible that it occurred within the predicted area prior to the last ice age. It is suggested that populations of N. cunninghamii along the northeastern edge of its present range in the Central Highlands of Victoria may be migrating northeast along a narrow corridor of apparently suitable climate to re-occupy the postulated former range. The rate of migration would be expected to be extremely slow because of the poor dispersal ability of the species and the adverse impact of recurrent fires.
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1. Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo-absences. 2. In this study we tested a new approach to generating pseudo-absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum. This method of generating pseudo-absences was compared with two others: (i) use of a GLM with pseudo-absences generated totally at random, and (ii) use of an ENFA only. 3. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above-mentioned methods with the two grains generated a distinct HS map. 4. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. 5. Results showed that (i) GLM models using ENFA-weighted pseudo-absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. 6. Synthesis and applications. We suggest that the use of ENFA-weighted pseudo-absence is a possible way to enhance the quality of GLM-based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.
Article
Data on localities from which insectivorous bats were collected or sighted in Israel were compiled into a Geographical Information System (GIS) in order to analyse patterns of species distribution. By intersecting precipitation and temperature data with spatially-referenced data on species observations stored in the GIS we determined the ‘climatic envelope’ of each species and constructed predictive maps which show the potential distribution of each species. Using cluster analysis, the bats were classified into three main biogeographic groups according to their distribution, namely desert, Mediterranean, and widespread in Israel. The potential distribution maps of all the species indicate that there are areas which have suitable bat habitats but from which bats have never been collected or observed. In the Mediterranean region of Israel, this is attributed to a large reduction in population size due to fumigation of caves, cave visitation and secondary poisoning.