Does accounting for imperfect detection improve species distribution models?

Models of species distributions are increasingly being used to address a variety of problems in conservation biology. In many applications, perfect or constant detectability of species, given presence, is assumed. While this problem has been acknowledged and addressed through the development of occupancy models, we still know little regarding whether addressing the potential for imperfect detection improves the predictive performance of species distribution models in nature. Here, we contrast logistic regression models of species occurrence that do not correct for detectability to hierarchical occupancy models that explicitly estimate and adjust for detectability, and maximum entropy models that attempt to circumvent the detectability problem by using data from known presence locations only. We use a large‐scale, long‐term monitoring database across western Montana and northern Idaho to contrast these models for nine landbird species that cover a broad spectrum in detectability. Overall, occupancy models were similar to or better than other approaches in terms of predictive accuracy, as measured by the Area Under the ROC Curve (AUC) and Kappa, with maximum entropy tending to provide the lowest predictive accuracy. Models varied in the types of errors associated with predictions, such that some model approaches may be preferred over others in certain situations. As expected, predictive performance varied across a gradient in species detectability, with logistic regression providing lower relative performance for less detectable species and Maxent providing lower performance for highly detectable species. We conclude by discussing the advantages and limitations to each approach for developing large‐scale species distribution models.     

How do species interactions affect species distribution models?

One of the most promising recent advances in biogeography has been the increased interest and understanding of species distribution models – estimates of the probability that a species is present given environmental data. Unfortunately, such analyses ignore many aspects of ecology, and so are difficult to interpret. In particular, we know that species interactions have a profound influence on distributions, but it is not usually possible to incorporate this knowledge into species distribution models. What is needed is a rigorous understanding of how unmeasured biotic interactions affect the inferences generated by species distribution models. To fill this gap, we develop a general mathematical approach that uses probability theory to determine how unmeasured biotic interactions affect inferences from species distribution models. Using this approach, we reanalyze one of the most important classes of mechanistic models of competition: models of consumer resource dynamics. We determine how measurements of one aspect of the environment – a single environmental variable – can be used to estimate the probability that an environment is suitable with species distribution models. We show that species distribution models, which ignore numerous facets of consumer resource dynamics such as the presence of a competitor or the dynamics of depletable resources, can furnish useful predictions for the probability that an environment is suitable in some circumstances. These results provide a rigorous link between complex mechanistic models of species interactions and species distribution models. In so doing they demonstrate that unmeasured biotic interactions can have strong and counterintuitive consequences on species distribution models.     

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal‐dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long‐lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species–environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species’ traits.     

Does governance play a role in the distribution of invasive alien species?

Invasive alien species (IAS) constitute a major threat to global biological diversity. In order to control their spread, a detailed understanding of the factors influencing their distribution is essential. Although international trade is regarded as a major force structuring spatial patterns of IAS, the role of other social factors remains unclear. Despite studies highlighting the importance of strong governance in slowing drivers of biodiversity loss such as logging, deforestation, and agricultural intensification, no study has yet analyzed its contribution to the issue of IAS. Using estimates of governance quality and comprehensive spatiotemporal IAS data, we performed multiple linear regressions to investigate the effect of governance quality upon the distribution of species listed under “100 of the worst” IAS in 38 Eurasian countries as defined by DASIE. Our model suggested that for countries with higher GDP, stronger governance was associated with a greater number of the worst IAS; in contrast, for the lowest GDP countries under analysis, stronger governance was associated with fewer of these IAS. We elucidate how the quality of governance within a country has implications for trade, tourism, transport, legislation, and economic development, all of which influence the spread of IAS. While our findings support the common assumption that strengthening governance benefits conservation interventions in countries of smaller economy, we find that this effect is not universal. Stronger governance alone cannot adequately address the problem of IAS, and targeted action is required in relatively high‐GDP countries in order to stem the influx of IAS associated with high volumes of trade.     

Are comparisons of species distribution models biased? Are they biologically meaningful?

A major problem in ecology is to understand how environmental requirements change over space and time. To this end, numerous authors have attempted to use comparisons of species’ distributions as a surrogate for comparisons of environmental requirements. Unfortunately, it is currently unclear when comparisons of species’ distributions produce reliable inferences about changes in environmental requirements. To address this problem, I develop an analytic model that identifies the conditions under which a comparison of species’ distribution models can serve as surrogate for a comparison of environmental requirements. This work demonstrates that 1) comparisons of species’ distributions typically produce biased comparisons of environmental requirements, 2) assuming distribution models are fit appropriately, it is possible to compare environmental requirements of distinct taxa, 3) there are multiple biologically relevant questions we can address using comparisons of distribution models, with each question corresponding to a distinct measure of the difference between distribution models. By developing an analytic model for comparisons of species’ distributions this work helps to clarify and remedy poorly understood sources of error associated with existing methods.     

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S‐SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over‐predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S‐SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank‐ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S‐SDMs. S‐SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S‐SDMS.     

On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10^–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.     

Do species distribution models explain spatial structure within tree species ranges?

Aim To evaluate the ability of species distribution models (SDMs) to predict the spatial structure of tree species within their geographical ranges (how trees are distributed within their ranges). Location Continental Spain. Methods We used an extensive dataset consisting of c. 90,000 plots (1 plot km^?2) where presence/absence data for 23 common Mediterranean and Atlantic tree species had been surveyed. We first generated SDMs relating the presence or absence of each species to a set of 16 environmental predictors, following a stepwise modelling process based on maximum likelihood methods. Superimposing spatial correlograms generated from the predictions of the SDMs over those generated from the raw data allowed a model–observation comparison of the nature, scale and intensity (level of aggregation) of spatial structure with the species ranges. Results SDMs predicted accurately the nature and scale of the spatial structure of trees. However, for most species, the observed intensity of spatial structure (level of aggregation of species in space) was substantially greater than that predicted by the SDMs. On average, the intensity of spatial aggregation was twice that predicted by SDMs. In addition, we also found a negative correlation between intensity of aggregation and species range size. Main conclusions Standard SDM predictions of spatial structure patterns differ among species. SDMs are apparently able to reproduce both the scale and intensity of species spatial structure within their ranges. However, one or more missing processes not included in SDMs results in species being substantially more aggregated in space than can be captured by the SDMs. This result adds to recent calls for a new generation of more biologically realistic SDMs. In particular, future SDMs should incorporate ecological processes that are likely to increase the intensity of spatial aggregation, such as source–sink dynamics, fine‐scale environmental heterogeneity and disequilibrium.     

How to assess the prediction accuracy of species presence–absence models without absence data?

It is very common that only presence data are available in ecological niche modeling. However, most existing methods for evaluating the accuracy of presence–absence (binary) predictions of species require presence–absence data. The aim of this study is to present a new method for accuracy assessment that does not rely on absence data. Two new statistics F_pb and F_cpb were derived based on presence–background data. With generated six virtual species, we used DOMAIN, generalized linear modeling (GLM), and maximum entropy (MAXENT) to produce different species presence–absence predictions. To investigate the effectiveness of the new statistics in accuracy assessment, we used F_pb, F_cpb, the traditional F‐measure (F), kappa coefficient, true skill statistic (TSS), area under the receiver operating characteristic curve (AUC), and the contrast validation index (CVI) to evaluate the accuracy of predictions, and the behaviors of these accuracy measures were compared. The effectiveness of F_pb for threshold selection and estimation of species prevalence was also investigated. Experimental results show that F_cpb is an estimate of F. The Pearson's correlation coefficient (COR) between F_cpb and F is 0.9882, with a root‐mean‐square error (RMSE) of 0.0171. In general, F_pb, F_cpb, F, kappa coefficient, TSS, and CVI can sort models by the accuracy of binary prediction, but AUC is not appropriate to evaluate the accuracy of binary prediction. For DOMAIN, GLM, and MAXENT, finding the threshold by maximizing F_pb and by maximizing F result in similar accuracies. In addition, the estimation of species prevalence based on binary output with maximizing F_pb as the thresholding method is significantly more accurate than simply averaging the original continuous output. The best estimate of prevalence is provided by the binary output of MAXENT, with an RMSE of 0.0116. Finally, we conclude that the new method is promising in accuracy assessment, threshold selection, and estimation of species prevalence, all of which are important but challenging problems with presence‐only data. Because it does not require absence data, the new method will have important applications in ecological niche modeling.     

The shape of things to come in the study of the origin of species?

Perhaps Darwin would agree that speciation is no longer the mystery of mysteries that it used to be. It is now generally accepted that evolution by natural selection can contribute to ecological adaptation, resulting in the evolution of reproductive barriers and, hence, to the evolution of new species (Schluter & Conte 2009 ; Meyer 2011 ; Nosil 2012 ). From genes that encode silencing proteins that cause infertility in hybrid mice (Mihola et al. 2009 ), to segregation distorters linked to speciation in fruit flies (Phadnis & Orr 2009 ), or pollinator‐mediated selection on flower colour alleles driving reinforcement in Texan wildflowers (Hopkins & Rausher 2012 ), characterization of the genes that drive speciation is providing clues to the origin of species (Nosil & Schluter 2011 ). It is becoming apparent that, while recent work continues to overturn historical ideas about sympatric speciation (e.g. Barluenga et al. 2006 ), ecological circumstances strongly influence patterns of genomic divergence, and ultimately the establishment of reproductive isolation when gene flow is present (Elmer & Meyer 2011 ). Less clear, however, are the genetic mechanisms that cause speciation, particularly when ongoing gene flow is occurring. Now, in this issue, Franchini et al. ( 2014 ) employ a classic genetic mapping approach augmented with new genomic tools to elucidate the genomic architecture of ecologically divergent body shapes in a pair of sympatric crater lake cichlid fishes. From over 450 segregating SNPs in an F2 cross, 72 SNPs were linked to 11 QTL associated with external morphology measured by means of traditional and geometric morphometrics. Annotation of two highly supported QTL further pointed to genes that might contribute to ecological divergence in body shape in Midas cichlids, overall supporting the hypothesis that genomic regions of large phenotypic effect may be contributing to early‐stage divergence in Midas cichlids.     

Poisoning the pika: must protection of grasslands be at the expense of biodiversity?

正The plateau pika(Ochotona curzoniae)plays a keystone role in the grassland ecosystem on the Qinghai-Tibetan Plateau(QTP)(Smith and Foggin,1999;Lai and Smith,2003),but in spite of its ecological importance it has been targeted for widespread poisoning including across the Sanjiangyuan National Nature Reserve(SNNR).Plateau pikas make burrows that provide homes to many small birds and     

Does seed retention potential affect the distribution of plant species in highly fragmented calcareous grasslands?

Long-distance dispersal is a crucial factor in the life-cycle of plants, especially in our modern, highly fragmented landscapes. Because natural herds of large animals have disappeared and grazing practices have been abandoned, important potential vectors for seed dispersal over large distances may have been lost. In the context of the re-establishment of grazing management for nature conservation purposes, it is therefore important to gain insight in the ability of grazing animals to act as seed dispersal vectors. Whereas local dispersal mainly occurs through standard vectors typically described based on morphological adaptations of the diaspore, large herbivores act as non-standard seed dispersers. Therefore, traditional dispersal classes are loosing scientific relevance and continuous predictors of dispersal potential have been proposed. Here, we explored whether dispersal related plant traits, including the "seed retention potential", could explain the distribution patterns of 180 plant species over 64 fragmented semi-natural calcareous grasslands in Belgium. The distribution of habitat specialist plant species was strongly determined by the degree of isolation of the grasslands. Interestingly, species distribution patterns were clearly linked with a species' potential to migrate through large grazers, as quantified by its retention potential: species producing seeds with high retention capacity were less affected by habitat isolation. Categorical dispersal classes based on seed morphology did not explain a species' response to fragment isolation. Although seed retention potential outperformed simple seed dimensional traits, plant height, which is an indicator of epizoochorous attachment potential, was even more important. Therefore we suggest further extension of the epizoochorous retention potential model by incorporating basic ecological mechanisms that effectively contribute to successful dispersal events.     

What is the shape of the distribution of protein conformations at equilibrium?

According to the thermodynamic hypothesis, the native state of proteins is that in which the free energy of the system is at its lowest, so that at normal temperature and pressure, proteins evolve to that state. We selected four proteins representative of each of the four classes, and for each protein make four simulations, one starting from the native structure and the other three starting from the structure obtained by threading the sequence of one protein onto the native backbone fold of the other three proteins. Because of their large conformational distances with respect to the native structure, the three alternative initial structures cannot be considered as local minima within the native ensemble of the corresponding protein. As expected, the initial native states are preserved in the .5?μs simulations performed here and validate the simulations. On the other hand, when the initial state is not native, an analysis of the trajectories does not reveal any evolution towards the native state, during that time. These results indicate that the distribution of protein conformations is multipeak shaped, so that apart from the peak corresponding to the native state, there are other peaks associated with average structures that are very different from the native and that can last as long as the native state.     

Spatial distribution patterns of Rhynchostegium megapolitanum at the landscape scale ‐ an expanding species?

Question: Is Rhynchostegium megapolitanum an expanding species? Location: Viennese Basin (120‐220 m a.s.1.), Austria. Methods: 121 dry grasslands, were investigated for the occurrence of R. megapolitanum. Nineteen environmental variables at 50 randomly selected sites, species composition at sites with and without R. megapolitanum and the spatial patterns of distribution of the species at the landscape scale were analysed. We compared actual distribution data of three rare species (Didymodon acutus, Pleurochaete squarrosa R. megapolitanum) and a common one (Brachythecium rutabulum) with the distribution obtained by vouchers that were collected between 1860 and 1940 in the investigated area. We calculated a GIS based model pattern and compared it with the actual distribution. Results: R. megapolitanum was detected in 28 of these sites, almost 50 % of its populations produced sporophytes. We found significant differences between sites with and without R. megapolitanum with regard to grassland size, the percentage of silt and of sand in the soil. There were fewer occurrences of historic herbarium vouchers of R. megapolitanum than our current field survey discovered. The GIS based analyses of distribution patterns at the landscape scale showed a clustering of sites in which R. megapolitanum was present or absent. Simulations with a spatially realistic expansion model showed high similarities to the actual distribution of the species. Conclusions: All these analyses suggest that R. megapolitanum has been expanding in the investigated area. A significant increase in temperature and nitrogen deposition within the last hundred years might be the underlying cause for the species' spread.     

How well can the accuracy of comparative protein structure models be predicted?

Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the Calpha root-mean-squared deviation (RMSD) and native overlap (NO3.5A) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5A errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71.     

Are the speeds of species invasions regulated? The importance of null models

Understanding the mechanisms and patterns that govern the invasion of species is essential for coping with global change of the biological world. A recent study highlights the possibility, based on data from a wide range of different taxa, that the invasion speed of species could be governed by a regulatory process. In principle, it is possible that mechanisms such as Allee effects could cause the invasion fronts to be regulated, such that the change in the rate of spread is negatively related to the current rate. This is very similar to how some populations are regulated around an equilibrium size, and finding the regulation structure if true, is of both pure and applied interest. However, here we will argue that the methods used so far are incomplete, thus even though there is a theoretical possibility that the speed of species invasions are regulated, more scrutiny is needed for its detection. Analysing changes of the ratio of current and past rate of spread against current ratios may give the impression of regulation in null models that are in fact unregulated. In addition we show that the apparent pattern is highly influenced by the spatial scale of investigation. Our results show that detecting regulatory patterns in species invasions is similarly non-trivial as is detecting density-dependence per se, but necessary, given the importance of this problem.     

Effects of spatial scale and sample size in GPS-based species distribution models: are the best models trivial for red deer management?

Species distribution models (SDMs) are popular in conservation and management of a wide array of taxa. Often parameterized with coarse GIS-based environmental maps, they perform well in macro-ecological settings but it is debated if the models can predict distribution within broadly suitable “known” habitats of interest to local managers. We parameterized SDMs with GIS-derived environmental variables and location data from 82 GPS-collared female red deer (Cervus elaphus) from two study areas in Norway. Candidate GLM models were fitted to address the effect of spatial scale (landscape vs. home range), sample size, and transferability between study areas, with respect to predictability (AUC) and explained variance (Generalized R ² and deviance). The landscape level SDM captured variation in deer distribution well and performed best on all diagnostic measures of model quality, caused mainly by a trivial effect of avoidance of non-habitat (barren mountains). The home range level SDMs were far less predictable and explained comparatively little variation in space use. Landscape scale models stabilized at the low sample size of 5–10 individuals and were highly transferrable between study areas implying a low degree of individual variation in habitat selection at this scale. It is important to have realistic expectations of SDMs derived from digital elevation models and coarse habitat maps. They do perform well in highlighting potential habitat on a landscape scale, but often miss nuances necessary to predict more fine-scaled distribution of wildlife populations. Currently, there seems to be a trade-off between model quality and usefulness in local management.