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Journal ArticleDOI

Predictive habitat distribution models in ecology

05 Dec 2000-Ecological Modelling (Elsevier)-Vol. 135, Iss: 2, pp 147-186
TL;DR: A review of predictive habitat distribution modeling is presented, which shows that 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.
About: This article is published in Ecological Modelling.The article was published on 2000-12-05. It has received 6748 citations till now. The article focuses on the topics: Model selection & Environmental niche modelling.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.

13,120 citations


Cites methods from "Predictive habitat distribution mod..."

  • ...Sometimes both presence and absence occurrence data are available 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 Zimmerman, 2000; Scott et al., 2002)....

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  • ...…both presence and absence occurrence data are available 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, seeCorsi et al., 2000; Elith, 2002; Guisan and Zimmerman, 2000; Scott et al., 2002)....

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Journal ArticleDOI
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

7,589 citations


Cites background or methods from "Predictive habitat distribution mod..."

  • ...There is now a plethora of methods for modelling species’ distributions that vary in how they model the distribution of the response, select relevant predictor variables, define fitted functions for each variable, weight variable contributions, allow for interactions, and predict geographic patterns of occurrence ( Guisan and Zimmerman 2000, Burgman et al. 2005, Wintle and Bardos in press)....

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  • ...…model the distribution of the response, select relevant predictor variables, define fitted functions for each variable, weight variable contributions, allow for interactions, and predict geographic patterns of occurrence (Guisan and Zimmerman 2000, Burgman et al. 2005, Wintle and Bardos in press)....

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Journal ArticleDOI
25 Apr 2013-Nature
TL;DR: These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.
Abstract: Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes. For some patients, dengue is a life-threatening illness. There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread. The contemporary worldwide distribution of the risk of dengue virus infection and its public health burden are poorly known. Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanization. Using cartographic approaches, we estimate there to be 390 million (95% credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of disease severity). This infection total is more than three times the dengue burden estimate of the World Health Organization. Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help to guide improvements in disease control strategies using vaccine, drug and vector control methods, and in their economic evaluation.

7,238 citations

Journal ArticleDOI
TL;DR: An overview of recent advances in species distribution models, and new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales are suggested.
Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

5,620 citations


Cites background or methods from "Predictive habitat distribution mod..."

  • ...Environmental predictors can exert direct or indirect effects on species, arranged along a gradient from proximal to distal predictors (Austin 2002), and are optimally chosen to reflect the three main types of influences on the species (modified from Guisan & Zimmermann 2000; Huston 2002; Fig....

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  • ...Species distribution models are empirical models relating field observations to environmental predictor variables, based on statistically or theoretically derived response surfaces (Guisan & Zimmermann 2000)....

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  • ...A striking characteristic of SDMs is their reliance on the niche concept (Guisan & Zimmermann 2000)....

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  • ...For more details on the different steps of SDM building, we refer readers to Guisan & Zimmermann (2000)....

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  • ...The procedure of SDM building ideally follows six steps (modified from Guisan & Zimmermann 2000; see Table 2): (i) conceptualization, (ii) data preparation, (iii) model fitting, (iv) model evaluation, (v) spatial predictions, and (vi) assessment of model applicability....

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Journal ArticleDOI
TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
Abstract: Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

5,076 citations


Cites background from "Predictive habitat distribution mod..."

  • ...Reviews of SDM literature include those of Guisan & Zimmermann (2000), Stauffer (2002), Guisan & Thuiller (2005), Richards et al. (2007), and Schröder (2008)....

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References
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Book
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

52,705 citations

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01 Jan 1993
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Abstract: This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.

37,183 citations

Journal ArticleDOI
Jacob Cohen1
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Abstract: CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and

34,965 citations


"Predictive habitat distribution mod..." refers background in this paper

  • ...…measures have been proposed such as the proportion of area correctly classified, the percent commission and omission errors (and the resulting percent confusion error), k (Cohen, 1960; Monserud and Lee- mans, 1992; Fitzgerald and Lees, 1994b), t (Ma and Redmond, 1995), or Foody’s (1992) k....

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Book
01 Jan 1983
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Abstract: The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).

23,215 citations