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Abstract AimEcological niche models (ENMs) are used widely in ecology, evolution, global change biology, but model uncertainty remains an underappreciated issue. Generally, either a single model from one algorithm or an ensemble of single models from different algorithms is used to provide a prediction. In addition to variability among algorithms, recent studies have shown the need to consider variability within a single algorithm, for example optimizing model complexity by tuning model settings. We present an ensembleENMusing a single‐algorithm approach, while adjusting model settings to maximize performance. LocationMadagascar. MethodsWe usedmaxent, bioclimatic variables and occurrence records of four species of Malagasy tenrecs (Family Tenrecidae). We calibrated and evaluated preliminary models using a jackknife approach, tuning two model settings to estimate optimal model complexity. We chose a suite of top‐performing preliminary models and then generated a consensus prediction. Furthermore, we calculated the variability among predictions of the co‐optimal models to indicate variation in geography (i.e. uncertainty). We then did the same after projecting the predictions to climatic estimates for the Last Glacial Maximum and the year 2070. ResultsThe default settings were never identified as optimal for any of the four species. The model settings considered as the co‐optimal solutions essentially led to the same evaluation statistics; however, they showed high variation in their geographic predictions for three of the four species. Additionally, variation among such models was greater when transferred across time. Main conclusionsThis approach likely can provide better predictions for a single algorithm as well as quantifications of within‐algorithm uncertainty, qualities that are highly useful in interpreting reconstructed suitable areas or forecasts of potential range shifts under future climate change. Finally, this within‐algorithm uncertainty can be integrated into a larger framework that considers variability due to other factors (e.g. related to input data, alternate algorithms or various Global circulation models). 

Abstract Biotic interactions can influence the ranges and abundances of species, but no clear guidelines exist for integrating them into correlative models of niches and distributions. Niche/distributional models characterize environmental/habitat suitability or species presence using predictor variables unaffected by (= unlinked to) the population of the focal species. Such variables (termed ‘scenopoetic’) typically have been considered to include only abiotic factors. In contrast, population–demographic approaches model the abundance of the focal species by including linked predictor variables, which frequently are biotic interactors. Nevertheless, a focal species might hold no, or negligible, population‐level effects on its biotic interactors. Hence, contrary to current theory, such interactors would represent unlinked variables valid and potentially very useful for niche/distributional models. Consideration of population‐level effects indicates that facilitators and affecting amensals (species that negatively affect another species but are not affected by it) constitute unlinked variables, but commensals and affected amensals do not. For competitors, mutualists, predators/prey, consumers/resources, and parasites/hosts, additional information is necessary. Specifically, available ecological/natural history information for the particular species involved (e.g. regarding specificity) and theory regarding ecological networks can allow identification of interactors that are likely to be unlinked or nearly so. Including an unlinked biotic interactor as a predictor variable in a niche/distributional model should improve predictions when the effects of the biotic interactor vary across the study region, or in another place or time period. Other relevant interactors must be taken into account by post‐processing a niche/distributional model, or via population–demographic models that require abundance data over time. This framework should improve current correlative models and highlights areas requiring progress. 

Abstract Quaternary climatic oscillations affected species distributions worldwide, creating cycles of connectivity and isolation that impacted population demography and promoted lineage divergence. These effects have been well studied in temperate regions. Taxa inhabiting mesic montane habitats in tropical ecosystems show high levels of endemism and diversification in the distinct mountain ranges they inhabit; such a pattern has commonly been ascribed to past climatic oscillations, but few phylogeographic studies have tested this hypothesis. Here, we combine ecological niche models of species distributions with molecular data to study phylogeographic patterns in two rodents endemic to the highlands of Costa Rica and western Panama (Reithrodontomys creper and Nephelomys devius). In so doing, we apply a novel approach that incorporates a basic ecological principle: the expected positive relationship between environmental suitability and population abundance. Specifically, we use niche models to predict potential patterns of population connectivity and stability of different suitability levels during climatic extremes of the last glacial–interglacial cycle; we then test these predictions with population genetic analyses of a mitochondrial and a nuclear marker. The detailed predictions arising from the different levels of suitability were moderately to highly congruent with the molecular data depending on the species. Overall, results suggest that in these tropical montane ecosystems, cycles of population connectivity and isolation followed a pattern opposite to that typically described for temperate or lowland tropical ecosystems: namely, higher connectivity during the colder glacials, with isolation in montane refugia during the interglacials, including today. Nevertheless, the individualistic patterns for each species indicate a potentially wide gamut of phylogeographic histories reflecting particularities of their niches. Taken together, this study illustrates how phylogeographic inferences may benefit from niche model outputs that provide more detailed predictions of connectivity and finer characterizations of potential refugia through time.