Factors Affecting Species Distribution Predictions: A Simulation Modeling Experiment

Abstract

Geospatial species sample data (e.g., records with location information from natural history museums or annual surveys) are rarely collected optimally, yet are increasingly used for decisions concerning our biological heritage. Using computer simulations, we examined factors that could affect the performance of autologistic regression (ALR) models that predict species occurrence based on environmental variables and spatially correlated presence/absence data. We used a factorial experiment design to examine the effects of survey design, spatial contiguity, and species detection probability and applied the results of ten replications of each factorial combination to an ALR model. We used additional simulations to assess the effects of sample size and environmental data error on model performance. Predicted distribution maps were compared to simulated distribution maps, considered “truth,” and evaluated using several metrics: omission and commission error counts, residual sums of squares (RSS), and areas under receiver operating characteristic curves (AUC). Generally, model performance was better using random and stratified survey designs than when using other designs. Adaptive survey designs were an exception to this generalization under the omission error performance criterion. Surveys using rectangular quadrats, designed to emulate roadside surveys, resulted in models with better performance than those using square quadrats (using AUC, RSS, and omission error metrics) and were most similar in performance to a systematic quadrat design. Larger detection probabilities, larger sample sizes, contiguous distributions, and fewer environmental data errors generally improved model performance. Results suggest that spatially biased sample data, e.g., data collected along roads, could result in model performance near that of systematic quadrat designs even in the presence of potentially confounding factors such as contiguity of distributions, detection probability, sample size, and environmental data error.

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

“…False absences result from insufficient sampling effort given the species' abundance or detectability. They can hinder good model performance, although Reese et al (2005) showed that randomly distributed false absences, due to randomly biased sampling effort, do not. True absences can be further subdivided into two classes: "true environmental" absences, when the species is not present due to environmental constraints on its physiology; and contingent absences, when the environment is appropriate but other factors such as limitations to dispersal, elimination by anthropogenic or stochastic events, or biotic interactions prevent species occurrence .…”

Regional data to support biodiversity assessments: terrestrial vertebrate and butterfly data from the Southwest

“…Consequently, sampling biases can confound the development of tenable species distribution models. Although algorithms for predicting species distributions that are robust to such sampling biases are being investigated (Peterson 2001;Hirzel and others 2002;Reese 2003), use of this database to construct inferential models must explicitly consider effects associated with potentially biased sampling effort.…”

Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research

“…Sampling error can also occur when observers differ in their ability to detect, identify and quantify species or events [32], leading to variation in data accuracy [10,34,35]. Sample size, coupled with species' ecological and detection differences can alter the performance of distribution models [36]. Consequently, inadequate evaluation of these biases associated with citizen science data will lead to false results that fail to accurately describe the species distribution.…”