Empirical Dynamic Material Flow Model for Tungsten in the USA

Multivariate polynomial regression (MPR) analysis was implemented to develop a nonlinear dynamic material flow model (DMFM) of tungsten in the United States for the years 1975–2000 without assumptions for lifetime distributions within reservoirs. Two external economic factors, the Consumer Price Index and the Industrial Production Index, were included as possible exogenous variables. Six types of vector time‐series models were developed using multilinear, simple interaction, and MPR models, each with and without the exogenous economic variables. The DMFMs developed in this work make one‐step‐ahead predictions. That is, the material flows in a given year were predicted using flows and exogenous variables from previous years. In contrast to approaches that utilize assumed lifetime distributions for material within reservoirs, such as the Weibull distribution, the approach used here is completely data driven. MPR models produced statistically better results than linear models for all 13 flows that were modeled. Four of these models used simple interaction terms (which we call linear interaction terms), and two of these incorporated exogenous variables. The other nine models utilized higher‐degree terms with interactions (called multivariate polynomial terms), and two of these incorporated exogenous variables. We conclude that nonlinear vector time series are capable of identifying complex relationships among material flows and exogenous variables. An understanding of these relationships has potential for managing, conserving, and/or forecasting the use of a resource.     

Confronting different models of community structure to species-abundance data: a Bayesian model comparison

Species abundances are undoubtedly the most widely available macroecological data, but can we use them to distinguish among several models of community structure? Here we present a Bayesian analysis of species‐abundance data that yields a full joint probability distribution of each model's parameters plus a relatively parameter‐independent criterion, the posterior Bayes factor, to compare these models. We illustrate our approach by comparing three classical distributions: the zero‐sum multinomial (ZSM) distribution, based on Hubbell's neutral model, the multivariate Poisson lognormal distribution (MPLN), based on niche arguments, and the discrete broken stick (DBS) distribution, based on MacArthur's broken stick model. We give explicit formulas for the probability of observing a particular species‐abundance data set in each model, and argue that conditioning on both sample size and species count is needed to allow comparisons between the two distributions. We apply our approach to two neotropical communities (trees, fish). We find that DBS is largely inferior to ZSM and MPLN for both communities. The tree data do not allow discrimination between ZSM and MPLN, but for the fish data ZSM (neutral model) overwhelmingly outperforms MPLN (niche model), suggesting that dispersal plays a previously underestimated role in structuring tropical freshwater fish communities. We advocate this approach for identifying the relative importance of dispersal and niche‐partitioning in determining diversity of different ecological groups of species under different environmental conditions.     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

On the distribution of developmental errors: comparing the normal, gamma, and log-normal distribution

The amount of between‐individual variation in the unobservable developmental instability (DI) has been the subject of intense recent debates. The unexpectedly high estimates of between‐individual variation in DI based on distributional characteristics of observable asymmetry values (of on average bilaterally symmetric traits) rely on statistical models that assume an underlying normal distribution of developmental errors. This prompted doubts on the assumption of the Gaussian nature of developmental errors. However, when applying other candidate distributions [log‐normal and gamma (γ)], recent analyses of empirical datasets have indicated that estimates remain generally high. Yet, all estimates were based on bilaterally symmetric traits, which did not allow for a formal comparison of the alternative distributions. In the present study, we extend a recent statistical model to allow statistical comparison of the different distributions based on traits that developed repeatedly under the same conditions, such as flower traits and regrown feathers. We analyse simulated and empirical data and show that: (1) it is statistically difficult to differentiate among the three alternatives when variances are small relative to the mean, as is often the case with DI; (2) the normal distribution fits the log‐normal or γ relatively well under those circumstances; (3) the deviance information criterion (DIC) is able to pick up differences in model fit among the three alternative distributions, yet more strongly so when levels of DI were high; (4) empirical datasets show a better fit of the normal over the log‐normal and γ‐distributions as judged by the DIC; and (5) estimates of between‐individual variation in DI in the three empirical datasets were relatively high (> 50%) under each distributional assumption. In conclusion, and based on our three datasets, the normal approximation appears to be a reasonable choice for statistical models of DI and the remarkably high estimates of variation in DI cannot be attributed to non‐normal developmental noise. Nevertheless, our method should be applied to a broad range of traits and organisms to evaluate the generality of this result. We argue that there is an urgent need for studies that reveal the underlying mechanisms of developmental noise and stability, as well as the role of developmental selection, in order to be able to determine the biological importance of the highly skewed distributions of developmental instability often observed. © 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92 , 197–210.     

Estimation in the progressive illness‐death model: A nonexhaustive review

Multistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so‐called “illness‐death” model plays a central role in the theory and practice of these models. Many time‐to‐event datasets from medical studies with multiple end points can be reduced to this generic structure. In these models one important goal is the modeling of transition rates but biomedical researchers are also interested in reporting interpretable results in a simple and summarized manner. These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions. We will give a review of some of the available methods for estimating such quantities in the progressive illness‐death model conditionally (or not) on covariate measures. For some of these quantities estimators based on subsampling are employed. Subsampling, also referred to as landmarking, leads to small sample sizes and usually to heavily censored data leading to estimators with higher variability. To overcome this issue estimators based on a preliminary estimation (presmoothing) of the probability of censoring may be used. Among these, the presmoothed estimators for the cumulative incidences are new. We also introduce feasible estimation methods for the cumulative incidence function conditionally on covariate measures. The proposed methods are illustrated using real data. A comparative simulation study of several estimation approaches is performed and existing software in the form of R packages is discussed.     

Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales

Abstract. Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alternative methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive models and classification tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better accuracy than GLM with quadratic and polynomial terms. This is probably because distributions at finer scales represent a linear sub‐sample of entire realized niches of species. In contrast to GLM, the performance of GAM was constant across scales being more data‐oriented. The predictive accuracy of GAM was always at least equal to other techniques, suggesting that this modelling approach is more robust to variations of scale because it can deal with any response shape.     

Predictability of stream insect distributions is dependent on niche position,but not on biological traits or taxonomic relatedness of species

Species distributions can be analysed under two perspectives: the niche‐based approach, which focuses on species–environment relationships; and the dispersal‐based approach, which focuses on metapopulation dynamics. The degree to which each of these two components affect species distributions may depend on habitat fragmentation, species traits and phylogenetic constraints. We analysed the distributions of 36 stream insect species across 60 stream sites in three drainage basins at high latitudes in Finland. We used binomial generalised linear models (GLMs) in which the predictor variables were environmental factors (E models), within‐basin spatial variables as defined by Moran's eigenvector maps (M models), among‐basin variability (B models), or a combination of the three (E + M + B models) sets of variables. Based on a comparative analysis, model performance was evaluated across all the species using Gaussian GLMs whereby the deviance accounted for by binomial GLMs was fitted on selected explanatory variables: niche position, niche breadth, site occupancy, biological traits and taxonomic relatedness. For each type of model, a reduced Gaussian GLM was eventually obtained after variable selection (Bayesian information criterion). We found that niche position was the only variable selected in all reduced models, implying that marginal species were better predicted than non‐marginal species. The influence of niche position was strongest in models based on environmental variables (E models) or a combination of all types of variables (E + M + B models), and weakest in spatial autocorrelation models (M models). This suggests that species–environment relationships prevail over dispersal processes in determining stream insect distributions at a regional scale. Our findings have clear implications for biodiversity conservation strategies, and they also emphasise the benefits of considering both the niche‐based and dispersal‐based approaches in species distribution modelling studies.     

Decomposing the heterogeneity of species distributions into multiple scales: a hierarchical framework for large‐scale count surveys

We introduce a novel spatially explicit framework for decomposing species distributions into multiple scales from count data. These kinds of data are usually positively skewed, have non‐normal distributions and are spatially autocorrelated. To analyse such data, we propose a hierarchical model that takes into account the observation process and explicitly deals with spatial autocorrelation. The latent variable is the product of a positive trend representing the non‐constant mean of the species distribution and of a stationary positive spatial field representing the variance of the spatial density of the species distribution. Then, the different scales of emergent structures of the distribution of the population in space are modelled from the latent density of the species distribution using multi‐scale variogram models. Multi‐scale kriging is used to map the spatial patterns previously identified by the multi‐scale models. We show how our framework yields robust and precise estimates of the relevant scales both for spatial count data simulated from well‐defined models, and in a real case‐study based on seabird count data (the common guillemot Uria aalge) provided by large‐scale aerial surveys of the Bay of Biscay (France) performed over a winter. Our stochastic simulation study provides guidelines on the expected uncertainties of the scales estimates. Our results indicate that the spatial structure of the common guillemot can be modelled as a three‐level hierarchical system composed of a very broad‐scale pattern (～ 200 km) with a stable location over time that might be environmentally controlled, a broad‐scale pattern (～ 50 km) with a variable shape and location, that might be related to shifts in prey distribution, and a fine‐scale pattern (～ 10 km) with a rather stable shape and location, that might be controlled by behavioural processes. Our framework enables the development of robust, scale‐dependent hypotheses regarding the potential ecological processes that control species distributions.     

Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

Should ecologists prefer model‐ over distance‐based multivariate methods?

Ecological data sets often record the abundance of species, together with a set of explanatory variables. Multivariate statistical methods are optimal to analyze such data and are thus frequently used in ecology for exploration, visualization, and inference. Most approaches are based on pairwise distance matrices instead of the sites‐by‐species matrix, which stands in stark contrast to univariate statistics, where data models, assuming specific distributions, are the norm. However, through advances in statistical theory and computational power, models for multivariate data have gained traction. Systematic simulation‐based performance evaluations of these methods are important as guides for practitioners but still lacking. Here, we compare two model‐based methods, multivariate generalized linear models (MvGLMs) and constrained quadratic ordination (CQO), with two distance‐based methods, distance‐based redundancy analysis (dbRDA) and canonical correspondence analysis (CCA). We studied the performance of the methods to discriminate between causal variables and noise variables for 190 simulated data sets covering different sample sizes and data distributions. MvGLM and dbRDA differentiated accurately between causal and noise variables. The former had the lowest false‐positive rate (0.008), while the latter had the lowest false‐negative rate (0.027). CQO and CCA had the highest false‐negative rate (0.291) and false‐positive rate (0.256), respectively, where these error rates were typically high for data sets with linear responses. Our study shows that both model‐ and distance‐based methods have their place in the ecologist's statistical toolbox. MvGLM and dbRDA are reliable for analyzing species–environment relations, whereas both CQO and CCA exhibited considerable flaws, especially with linear environmental gradients.     

Tree size distributions in an old-growth temperate forest

Despite the wide variation in the structural characteristics in natural forests, tree size distribution show fundamental similarities that suggest general underlying principles. The metabolic ecology theory predicts the number of individual scales as the −2 power of tree diameter. The demographic equilibrium theory predicts tree size distribution starting from the relationship of size distributions with growth and mortality at demographic equilibrium. Several analytic predictions for tree size distributions are derived from the demographic equilibrium theory, based on different growth and mortality functions. In addition, some purely phenomenological functions, such as polynomial function, have been used to describe the tree size distributions. In this paper, we use the metabolic ecology theory, the demographic equilibrium theory and the polynomial function to predict the tree size distribution for both the whole community and each species in an old-growth temperate forest in northeastern China. The results show that metabolic ecology theory predictions for the scaling of tree abundance with diameter were unequivocally rejected in the studied forest. Although these predictions of demographic theory are the best models for most of the species in the temperate forest, the best models for some species ( Tilia amurensis , Quercus mongolica and Fraxinus mandshurica ) are compound curves (i.e. rotated sigmoid curves), best predicted by the polynomial function. Hence, the size distributions of natural forests were unlikely to be invariant and the predictive ability of general models was limited. As a result, developing a more sophisticated theory to predict tree size distributions remains a complex, yet tantalizing, challenge.     

Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models

Aim Niche‐based distribution models are often used to predict the spread of invasive species. These models assume niche conservation during invasion, but invasive species can have different requirements from populations in their native range for many reasons, including niche evolution. I used distribution modelling to investigate niche conservatism for the Asian tiger mosquito (Aedes albopictus Skuse) during its invasion of three continents. I also used this approach to predict areas at risk of invasion from propagules originating from invasive populations. Location Models were created for Southeast Asia, North and South America, and Europe. Methods I used maximum entropy (Maxent ) to create distribution models using occurrence data and 18 environmental datasets. One native model was created for Southeast Asia; this model was projected onto North America, South America and Europe. Three models were created independently for the non‐native ranges and projected onto the native range. Niche overlap between native and non‐native predictions was evaluated by comparing probability surfaces between models using real data and random models generated using a permutation approach. Results The native model failed to predict an entire region of occurrences in South America, approximately 20% of occurrences in North America and nearly all Italian occurrences of A. albopictus. Non‐native models poorly predict the native range, but predict additional areas at risk for invasion globally. Niche overlap metrics indicate that non‐native distributions are more similar to the native niche than a random prediction, but they are not equivalent. Multivariate analyses support modelled differences in niche characteristics among continents, and reveal important variables explaining these differences. Main conclusions The niche of A. albopictus has shifted on invaded continents relative to its native range (Southeast Asia). Statistical comparisons reveal that the niche for introduced distributions is not equivalent to the native niche. Furthermore, reciprocal models highlight the importance of controlling bi‐directional dispersal between native and non‐native distributions.     

Species–habitat associations and demographic rates of forest trees

Niche‐driven effects on demographic processes generated in response to habitat heterogeneity partly shape local distributions of species. Thus, tree distributions are commonly studied in relation to habitat conditions to understand how niche differentiation contributes to species coexistence in forest communities. Many such studies implicitly assume that local abundance reflects habitat suitability, and that abundance is relatively stable over time. We compared models based on abundance with those based on demographic performance for making inferences about habitat association for 287 tree species from three large dynamic plots located in tropical, subtropical and temperate forests. The correlation between the predictions of the abundance‐based models and the demography‐based models varied widely, with correlation coefficients ranging nearly from ?1 to 1.This suggests that the two types of models capture different information about species–habitat associations. Demography‐based models evaluate habitat quality by focusing on population processes and thus should be preferred for understanding responses of tree species to habitat conditions, especially when habitat conditions are changing and species–habitat interactions cannot be considered to be at equilibrium.     

Unraveling the habitat preferences of two closely related bumble bee species in Eastern Europe

Co‐occurrence of closely related species is often explained through resource partitioning, where key morphological or life‐history traits evolve under strong divergent selection. In bumble bees (genus Bombus), differences in tongue lengths, nest sites, and several life‐history traits are the principal factors in resource partitioning. However, the buff‐tailed and white‐tailed bumble bee (Bombus terrestris and B. lucorum respectively) are very similar in morphology and life history, but their ranges nevertheless partly overlap, raising the question how they are ecologically divergent. What little is known about the environmental factors determining their distributions stems from studies in Central and Western Europe, but even less information is available about their distributions in Eastern Europe, where different subspecies occur. Here, we aimed to disentangle the broad habitat requirements and associated distributions of these species in Romania and Bulgaria. First, we genetically identified sampled individuals from many sites across the study area. We then not only computed species distributions based on presence‐only data, but also expanded on these models using relative abundance data. We found that B. terrestris is a more generalist species than previously thought, but that B. lucorum is restricted to forested areas with colder and wetter climates, which in our study area are primarily found at higher elevations. Both vegetation parameters such as annual mean Leaf Area Index and canopy height, as well as climatic conditions, were important in explaining their distributions. Although our models based on presence‐only data suggest a large overlap in their respective distributions, results on their relative abundance suggest that the two species replace one another across an environmental gradient correlated to elevation. The inclusion of abundance enhances our understanding of the distribution of these species, supporting the emerging recognition of the importance of abundance data in species distribution modeling.     

Fine‐grain,large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection

Large‐domain species distribution models (SDMs) fail to identify microrefugia, as they are based on climate estimates that are either too coarse or that ignore relevant topographic climate‐forcing factors. Climate station data are considered inadequate to produce such estimates, a viewpoint we challenge here. Using climate stations and topographic data, we developed three sets of large‐domain (450 000 km²), fine‐grain (50 m) temperature grids accounting for different levels of topographic complexity. Using these fine‐grain grids and the Worldclim data, we fitted SDMs for 78 alpine species over Sweden, and assessed over‐ versus underestimations of local extinction and area of microrefugia by comparing modelled distributions at species' rear edges. Accounting for well‐known topographic climate‐forcing factors improved our ability to model fine‐scale climate, despite using only climate station data. This approach captured the effect of cool air pooling, distance to sea, and relative humidity on local‐scale temperature, but the effect of solar radiation could not be accurately accounted for. Predicted extinction rate decreased with increasing spatial resolution of the climate models and with increasing number of topographic climate‐forcing factors accounted for. About half of the microrefugia detected in the most topographically complete models were not detected in the coarser SDMs and in the models calibrated from climate variables extracted from elevation only. Although major limitations remain, climate station data can potentially be used to produce fine‐grain topoclimate grids, opening up the opportunity to model local‐scale ecological processes over large domains. Accounting for the topographic complexity encountered within landscapes permits the detection of microrefugia that would otherwise remain undetected. Topographic heterogeneity is likely to have a massive impact on species persistence, and should be included in studies on the effects of climate change.     

The relative importance of climate and habitat in determining the distributions of species at different spatial scales: a case study with ground beetles in Great Britain

Experimental studies have shown that many species show preferences for different climatic conditions, or may die in unsuitable conditions. Climate envelope models have been used frequently in recent years to predict the presence and absence of species at large spatial scales. However, many authors have postulated that the distributions of species at smaller spatial scales are determined by factors such as habitat availability and biotic interactions. Climatic effects are often assumed by modellers to be unimportant at fine resolutions, but few studies have actually tested this. We sampled the distributions of 20 beetle species of the family Carabidae across three study sites by pitfall trapping, and at the national scale from monitoring data. Statistical models were constructed to determine which of two sets of environmental variables (temperature or broad habitat type) best accounted for the observed data at the three sites and at the national (Great Britain) scale. High‐resolution temperature variables frequently produced better models (as determined by AIC) than habitat features when modelling the distributions of species at a local scale, within the three study sites. Conversely, habitat was always a better predictor than temperature when describing species’ distributions at a coarse scale within Great Britain. Northerly species were most likely to occur in cool micro‐sites within the study sites, whereas southerly species were most likely to occur in warm micro‐sites. Effects of microclimate were not limited to species at the edges of their distribution, and fine‐resolution temperature surfaces should therefore ideally be utilised when undertaking climate‐envelope modelling.     

No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models

Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species’ fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil‐pollen data from 21 to 15 ka bp (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen‐based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no‐analog late glacial climates, such as Fraxinus, Ostrya/Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.