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Selection: Elith:J [10 articles] 

Publications by author Elith:J.

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Ecography, Vol. 40, No. 8. (1 August 2017), pp. 913-929,


Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also ...


Maxent is not a presence-absence method: a comment on Thibaud et al

Methods in Ecology and Evolution, Vol. 5, No. 11. (November 2014), pp. 1192-1197,


[Summary] [::1] Thibaud et al. (Methods in Ecology and Evolution 2014) present a framework for simulating species and evaluating the relative effects of factors affecting the predictions from species distribution models (SDMs). They demonstrate their approach by generating presence–absence data sets for different simulated species and analysing them using four modelling methods: three presence–absence methods and Maxent, which is a presence-background modelling tool. One of their results is striking: that their use of Maxent performs well in estimating occupancy probabilities and even ...


Bias correction in species distribution models: pooling survey and collection data for multiple species

Methods in Ecology and Evolution, Vol. 6, No. 4. (1 April 2015), pp. 424-438,


[::] Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence–absence or count data collected in systematic, planned surveys are more reliable but typically less abundant. [::] We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence–absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting ...


Point process models for presence-only analysis

Methods in Ecology and Evolution, Vol. 6, No. 4. (1 April 2015), pp. 366-379,


[::] Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. [::] In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. [::] Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature ...


Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

Ecological Applications, Vol. 19, No. 1. (January 2009), pp. 181-197,


Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with ...


Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Ecography, Vol. 36, No. 1. (1 January 2013), pp. 27-46,


Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or ...

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Is my species distribution model fit for purpose? Matching data and models to applications

Global Ecology and Biogeography, Vol. 24, No. 3. (February 2015), pp. 276-292,


Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the ...

Visual summary


A statistical explanation of MaxEnt for ecologists

Diversity and Distributions, Vol. 17, No. 1. (1 January 2011), pp. 43-57,


MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack ...


Species distribution models: ecological explanation and prediction across space and time

Annual Review of Ecology, Evolution, and Systematics, Vol. 40, No. 1. (2009), pp. 677-697,


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 ...


Novel methods improve prediction of species' distributions from occurrence data

Ecography, Vol. 29, No. 2. (1 April 2006), pp. 129-151,


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 ...

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