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Species distribution modeling (SDM) plays a crucial role in investigating habitat suitability and addressing various ecological issues. While likelihood analysis is commonly used to draw ecological conclusions, its statistical performance is not robust when confronted with slight deviations due to model misspecification in SDM. We proposed a new robust estimation method based on a novel divergence for the Poisson point process model. The method is characterized by weighting the log-likelihood equation to mitigate the impact of heterogeneous observations in the presence-only data, which can result from model misspecification. We demonstrated that our proposed method improved the predictive performance of maximum likelihood estimation in both simulation studies and the analysis of vascular plant data in Japan. • We introduced a robust method to address model misspecification in species distribution using thinned Poisson processes. • Simulation studies revealed bias in conventional estimates for mixed data, while our method provided appropriate estimates. • Our method improved prediction performance of the likelihood approach in analyzing presence-only data of vascular plants. [ABSTRACT FROM AUTHOR]

Climate-only species distribution models are commonly used to evaluate a species' vulnerability to future climate change. However, these models may overestimate a species' vulnerability when occurrence data comes from only a subset of the species' historical range due to anthropogenic range contractions. This study investigated the impacts of using climate-only species distribution models with anthropogenically truncated occurrence data on future predictions, using the little-studied leopard cat (Prionailurus bengalensis) of mainland Tropical Asia as a case study. Like most other species in mainland Tropical Asia, the leopard cat's current distribution represents a non-random subset of its historical range due to the disproportionate loss of lowland forests in the region. We found that climate-only species distribution models trained on current occurrence data significantly overestimated the leopard cat's vulnerability to future climate change, predicting considerable range contractions by 2081–2100 due to rising summer temperatures. A similar range contraction was observed using a virtual species whose occurrence was solely predicated on extant forest cover and not climate. This suggested the leopard cat's apparent vulnerability to future climate change was driven by a spurious relationship with summer temperatures, as modern occurrence data primarily came from refugial forest cover in the region's cooler uplands rather than the warmer, human-dominated lowlands. This bias was not apparent, however, from our current predictions, which had high predictive accuracy and were spatially consistent with current knowledge of the species' distribution; an observation highlighting the inadequacy of predictive accuracy alone as a metric for selecting models in studies where transferability to novel environments is critical. We next considered two potential strategies for detecting and mitigating the effects of anthropogenically truncated occurrence data: 1) the use of hybrid models that incorporate both climatic and non-climatic variables, and 2) the use of climate-only models with historical datasets. Despite using different data, both proposed strategies produced spatially similar predictions of present and future leopard cat occurrence with little-to-no range contractions predicted. We thus recommend all species distribution modeling studies interested in assessing a species' vulnerability to climate change compare future predictions derived from climate-only and hybrid models as well as compare predictions from current and historical datasets, as differences in predictions can highlight the presence of potential biases and other limitations. Finally, we discuss our findings as they apply to both our focal species and to the overall field of species distribution modeling. [ABSTRACT FROM AUTHOR]

Ensemble habitat selection modeling is becoming a popular approach among ecologists to answer different questions. Since we are still in the early stages of development and application of ensemble modeling, there remain many questions regarding performance and parameterization. One important gap, which this paper addresses, is how the number of background points used to train models influences the performance of the ensemble model. We used an empirical presence-only dataset and three different selections of background points to train scale-optimized habitat selection models using six modeling algorithms (GLM, GAM, MARS, ANN, Random Forest, and MaxEnt). We tested four ensemble models using different combinations of the component models: (a) equal numbers of background points and presences, (b) background points equaled ten times the number of presences, (c) 10,000 background points, and (d) optimized background points for each component model. Among regression-based approaches, MARS performed best when built with 10,000 background points. Among machine learning models, RF performed the best when built with equal presences and background points. Among the four ensemble models, AUC indicated that the best performing model was the ensemble with each component model including the optimized number of background points, while TSS increased as the number of background points models increased. We found that an ensemble of models, each trained with an optimal number of background points, outperformed ensembles of models trained with the same number of background points, although differences in performance were slight. When using a single modeling method, RF with equal number of presences and background points can perform better than an ensemble model, but the performance fluctuates when the number of background points is not properly selected. On the other hand, ensemble modeling provides consistently high accuracy regardless of background point sampling approach. Further, optimizing the number of background points for each component model within an ensemble model can provide the best model improvement. We suggest evaluating more models across multiple species to investigate how background point selection might affect ensemble models in different scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1300sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the MaxEnt model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results. [Copyright &y& Elsevier]