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Selection: Lahoz-Monfort:JJ [3 articles] 

Publications by author Lahoz-Monfort:JJ.

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 ...


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 ...

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