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2 edition of Spatially explicit distribution models for predicting species occurrences. found in the catalog.

Spatially explicit distribution models for predicting species occurrences.

Pilar Hernandez

Spatially explicit distribution models for predicting species occurrences.

by Pilar Hernandez

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  • 17 Currently reading

Published .
Written in English


About the Edition

Species distribution modeling is an essential tool for conservation planning. These models utilize the species-environment relationship to formulate a spatial depiction of its distribution pattern. Often these models are developed aspatially. That is they do not consider the spatial context of the species occurrence. Thereby, ignoring spatial components that contribute to the species distribution pattern such as species endogenous processes and the species dependence on its spatially structured physical environment. Species distribution modeling methods have been developed that explicitly account for these spatial processes. Spatially explicit modeling methods are reviewed and the importance of carefully considering interactions between the ecological, data and statistical components of the model is highlighted. A comparative evaluation of five spatially explicit methods and an aspatial method was performed to investigate their relative abilities to accurately predict three songbird occurrences. Results were mixed and dependent on characteristics of the species ecology and model data.

The Physical Object
Pagination98 leaves.
Number of Pages98
ID Numbers
Open LibraryOL19215489M
ISBN 100494023228

Search Tips. Phrase Searching You can use double quotes to search for a series of words in a particular order. For example, "World war II" (with quotes) will give more precise results than World war II (without quotes). Wildcard Searching If you want to search for multiple variations of a word, you can substitute a special symbol (called a "wildcard") for one or more letters.   Aim: Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so-called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit.

AbstractSpecies distribution models are a fundamental tool in ecology, conservation biology, and biogeography and typically identify potential species distributions using static phenomenological models. We demonstrate the importance of complementing these popular models with spatially explicit, dynamic mechanistic models that link potential and realized distributions. model, results in a spatially explicit “wall-to-wall” prediction of species distribution or habitat suitability (Fig. ). Maps of environmental pre-dictors, or their surrogates, must be available in order for predictive mapping to be implemented (Franklin, ). The purpose of this book is to describe the process of species dis-.

The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon. Ecological Modelling., v, p - , Cite.   This model provides a spatially explicit description of species range size and aspects of range structure. 3. Occupancy data from Drosophilidae species inhabiting a decaying fruit mesocosm were used to test the SSO model. Predictions from the spatially implicit and explicit models were largely equally accurate.


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Spatially explicit distribution models for predicting species occurrences by Pilar Hernandez Download PDF EPUB FB2

Species distribution models can be made spatially explicit in numerous ways (see Dormann,for a review of various methods). Spatial aspects are included in the SDM given the spatial autocorrelation of species distributions (Besag, ; Record et al., ).Cited by: 5.

Earliest found examples of modelling strategies using correlations between distributions of species and climate seems to be those of Johnston (), predicting the invasive spread of a cactus species in Australia, and Hittinka () assessing the climatic determinants of the distribution of several European species (quoted in Pearson & Dawson Cited by:   Predicting continental-scale patterns of bird species richness with spatially explicit models.

The stochastic placement of species occurrences in the two models is a Monte Carlo method for estimating the statistical expectation of species richness (range overlap) in each map cell, with and without range cohesion.

(range cohesion plus Cited by:   The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon several studies have produced spatially explicit models to simulate the dynamics of including data generated using the models and the deforestation in In that year were deforestation occurrences for Cited by:   Integrated wolf distribution models accounting for the distribution of mortality events can be interpreted as a spatially-explicit prediction of inherent problems associated with the long-standing conflict linked to the species, as also found in other regions where the wolf returned naturally, was reintroduced, or was planned to be reintroduced Cited by: 5.

Species distribution models (SDMs; also commonly referred to as ecological niche models, ENMs, amongst other names; see Appendix S1) are currently the main tools used to derive spatially explicit predictions of environmental suitability for species (Guisan & Thuiller ; Elith & Leathwick ; Franklin ; Peterson et al.

They. Calibration Methodology for an Individual-based, Spatially Explicit Simulation Model: Case Study of White-tailed Deer in the Florida Everglades / Christine S. Hartless, Ronald F. Labisky and Kenneth M. Portier ; Pt. Predicting Species Presence and Abundance ; Introduction to Part 4: Predicting Species Presence and Abundance /.

Draft 5 93 models to predict species patterns in areas for which little information is of the species 94 available is an extremely interesting line of research (Acevedo et al. 95 Models can be transferred to different spatial scenarios, temporal periods and/or spatial 96 resolution.

Spatial transferability is a means to assess the degree to which a. The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology.

Many areas of modeling have shifted. We provide a global, spatially explicit characterization of 47 terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme. Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting.

So-called species distribution models (SDMs; Guisan and ZimmermannGuisan et al. ), which relate species occurrences or abundances to spatially explicit ecological vari - ables, allow predicting the occurrence probability of a species at a given location across the landscape.

The quality of. By Brendan Wintle (This article was first published in the March issue of Decision Point, The Monthly Magazine of the Environmental Decisions Group) Species distribution models (SDMs) combine observations of species occurrence or abundance with information about environmental variables to gain ecological insights and to predict species' distributions across landscapes.

Introduction. Species distribution models (SDMs) for geographical range prediction (Segurado & Araújo ; Guisan & Thuiller ; Elith et al. ) assume that species' occurrence is determined by an immediate response of individuals to environmental variation (equilibrium of species' distribution in relation to climate, sensuAraújo & Pearson ).

Here, we employ a fundamentally different approach that uses spatially explicit Monte Carlo models of the placement of cohesive geographical ranges in an environmentally heterogeneous landscape.

These models predict species richness of endemic South American birds ( species) measured at a continental scale. Uriarte, R.

Condit, C.D. Canham, S.P. Hubbell, A spatially explicit model of sapling growth in a tropical forest: does the identity of neighbors matter, Journal of Ecology, 92\t () Google Scholar; bib J.A. Veech, A probabilistic model for analysing species co-occurrence, Global Ecology and Biogeography, 22 () Predicting continental-scale patterns of bird species richness with spatially explicit models Carsten Rahbek1, *, Nicholas J.

Gotelli2, Robert K. Colwell3, Gary L. Entsminger4, Thiago Fernando L. Rangel5 and Gary R. Graves6 1Center of Macroecology, Institute of Biology, University of Copenhagen, Universitetspar Copenhagen O, Denmark. 5 urn:lsid::pub:8D1BC1DDBCBD5BA NeoBiota NB Pensoft Publishers /neobiota Research Article Chordata Rodentia Vertebrata Biological Invasions Conservation Biology Data analysis & Modelling Species Inventories Cenozoic Europe The potential current distribution of the coypu (Myocastor coypus) in Europe and.

Predicting Species Occurrences addresses those concerns, highlighting for managers and researchers the strengths and weaknesses of current approaches, as well as the magnitude of the research required to improve or test predictions of currently used models.

The book is an outgrowth of an international symposium held in October that brought. Bryan S. Stevens, Courtney J. Conway, Mapping habitat suitability at range‐wide scales: Spatially‐explicit distribution models to inform conservation and research for marsh birds, Conservation Science and Practice, /csp, 2, 4, ().

Predicting Species Occurrences: Issues of Accuracy and Scale [Peter H. Raven, J. Michael Scott, Patricia Heglund and Michael L. Morrison]. Predictions about where different species are, where they are not, and how they move across a landscape or resp.

One way to improve our knowledge of the status of rare species is to use species distribution models (SDMs) to prioritize areas for field surveys. SDMs predict a species' distribution across space based on georeferenced occurrence records and environmental predictors (Guisan & Zimmermann ).Existing algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches.

Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas.