Effects of grain size and niche breadth on species distribution modeling

Scale is a vital component to consider in ecological research, and spatial resolution or grain size is one of its key facets. Species distribution models (SDMs) are prime examples of ecological research in which grain size is an important component. Despite this, SDMs rarely explicitly examine the effects of varying the grain size of the predictors for species with different niche breadths. To investigate the effect of grain size and niche breadth on SDMs, we simulated four virtual species with different grain sizes/niche breadths using three environmental predictors (elevation, aspect, and percent forest) across two real landscapes of differing heterogeneity in predictor values. We aggregated these predictors to seven different grain sizes and modeled the distribution of each of our simulated species using MaxEnt and GLM techniques at each grain size. We examined model accuracy using the AUC statistic, Pearson's correlations of predicted suitability with the true suitability, and the binary area of presence determined from suitability above the maximum true skill statistic (TSS) threshold. Habitat specialists were more accurately modeled than generalist species, and the models constructed at the grain size from which a species was derived generally performed the best. The accuracy of models in the homogenous landscape deteriorated with increasing grain size to a greater degree than models in the heterogenous landscape. Variable effects on the model varied with grain size, with elevation increasing in importance as grain size increased while aspect lost importance. The area of predicted presence was drastically affected by grain size, with larger grain sizes over predicting this value by up to a factor of 14. Our results have implications for species distribution modeling and conservation planning, and we suggest more studies include analysis of grain size as part of their protocol.     

Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert-based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing meso-climate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species. All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation. Combining large citizen science databases to expert-based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.     

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.     

排序和广义线性模型与广义可加模型在植物种与环境关系研究中的应用

排序和广义线性模型(Generalized Linear Model,GLM)与广义可加模型(Goneralized Additive Model,GAM)是研究植物种与环境间关系的重要方法。基于线性模型的排序方法应限定于环境梯度较短的植被数据。而基于单峰模型的排序方法更适用于梯度较长的情况。PCA、CA／RA系列和CCA系列是常用的排序方法。同时进行环境数据和植被数据分析的CCA系列,能清楚地得出植物种与环境间的关系。CCA改进后的DCCA和PCCA,是现今较理想的排序方法。GLM和GAM实质上是用环境变量的高阶多项式来拟合植物种与环境变量的关系。GLM和GAM扩展了植物种与环境变量之间的关系模型,能深入地探讨植物种与环境间的关系。GLM主要是模型决定的,而GAM主要取决于原始数据。一般来说,排序能得出研究区域的主要环境梯度,提供了物种聚集和植物群落的概略描述。GLM与GAM对于深入研究单个植物种与环境间的关系具有优势。在实际研究中,两种方法结合使用能互补不足。     

Niche variability and its consequences for species distribution modeling

When species distribution models (SDMs) are used to predict how a species will respond to environmental change, an important assumption is that the environmental niche of the species is conserved over evolutionary time-scales. Empirical studies conducted at ecological time-scales, however, demonstrate that the niche of some species can vary in response to environmental change. We use habitat and locality data of five species of stream fishes collected across seasons to examine the effects of niche variability on the accuracy of projections from Maxent, a popular SDM. We then compare these predictions to those from an alternate method of creating SDM projections in which a transformation of the environmental data to similar scales is applied. The niche of each species varied to some degree in response to seasonal variation in environmental variables, with most species shifting habitat use in response to changes in canopy cover or flow rate. SDMs constructed from the original environmental data accurately predicted the occurrences of one species across all seasons and a subset of seasons for two other species. A similar result was found for SDMs constructed from the transformed environmental data. However, the transformed SDMs produced better models in ten of the 14 total SDMs, as judged by ratios of mean probability values at known presences to mean probability values at all other locations. Niche variability should be an important consideration when using SDMs to predict future distributions of species because of its prevalence among natural populations. The framework we present here may potentially improve these predictions by accounting for such variability.     

pH optima for Danish forest species compared with Ellenberg reaction values

Species distribution depends on the physiological and ecological niche where a species can exist and regenerate in resource competition with other species (niche limitation). The realized niche is influenced by local biotic processes that influence species behaviour and the shape of the response curves relative to environmental gradients. Processes on larger scales also influence the species niche through source-sink mechanisms (dispersal limitation) and the species richness of an area (pool limitation). Despite the growing evidence of skewed or irregular species response curves along gradients, many ecologists still assume symmetric, unimodal response curves along gradients in ecological interpretation. Ellenberg’s indicator system is probably the most common example. However, the assumption is not ecologically or statistically valid, due to the many different processes affecting the distribution of plant species. Here I present the results of Huisman-Olff-Fresco (HOF) regressions for 209 Danish forest species. HOF modelling is chosen to avoid the classical drawbacks of assuming symmetric, unimodal response patterns. I calculate the optima for all species with unimodal responses to soil pH and compare these with the Ellenberg indicator values for reaction (R), which are often used as a substitute for soil pH measurements. I demonstrate that the assumption of symmetric, unimodal species behaviour is violated in 54% of the cases and that pH optima and R indicator values for species are not always compatible. Ellenberg reaction scale has been used byEwald (Folia Geobot. 38: 357–366, 2003) as an indicator of which species are calcicole, i.e., whether they can grow and reproduce on calcareous soils. Such affinities of species, however, are related to both local niche properties and processes on large scales and cannot be generalized from a single empirical variable such as pH, nor from Ellenberg semi-ordinal indicator scale. I conclude that while the determination of whether species are calcicole or calcifuge requires more research, it is evident that Denmark contains a fairly balanced number of calciphytic and acidophytic species. This is probably due to the nearly equal areas with acidic and alkaline soils in Denmark, which also contribute to the high species richness of more than 500 vascular plant species in Danish forests.     

Calibration of Ellenberg indicator values for the Faroe Islands

Abstract. Elenberg's bio‐indication system for soil moisture (F), soil nitrogen (N) and soil reaction (R) was examined, based on 559 vegetation samples and environmental characteristics (vegetation cover, soil depth, soil moisture, chemical soil properties) from four Faroe islands. The original indicator values from central Europe were used for the calculation of weighted community indicator values of F, N and R. These were regressed with respect to environmental data, applying standard curvilinear regression and generalized linear modelling (GLM) and new predicted values of community indicator values were obtained from the best model. Faroe species optima values of 162 taxa for one or more of the three EUenberg scales were derived from fitting Huisman‐Olff‐Fresco (HOF) models of species abundance with respect to predicted community indicator values and are proposed as new EUenberg species indicator values to be used in the Faroe Islands. F was best correlated with a GLM model containing soil moisture, organic soil fraction, soil depth and total vegetation cover, R with a GLM model containing pH and calcium in % organic soil fraction, N with total phosphorus in % organic soil fraction. The calibrated species indicator scales are much truncated, as compared with the original values, resulting in significantly different overall distributions of the original and new species indicator values. The recalculated community indicator values are much better correlated to environmental measurements. Several species do not have clear optima, but linear or monotone relationships to the examined indicator scales. This probably indicates that the occurrence of some species in the Faroe Islands are either determined by factors other than moisture, pH or soil nutrient status or, given the young age and environmental instability of the islands, are governed by stochastic mechanisms. Extension of Ellenberg indicator values outside central Europe should always be carefully calibrated by means of adequate environmental data and adequate statistical models, such as HOF models, should be applied.     

Use and improvement of Ellenberg's indicator values in deciduous forests of the Boreo-nemoral zone in Sweden

Ellenberg's indicator values for light, moisture and reaction were tested in deciduous hardwood forests of the Boreo-nemoral zone m Sweden Weighted averages of indicator values were calculated and correlated with field measurements for two test sets of relevés The best, highly significant correlations were found for reaction, particularly for weighted averages based on vascular plant species Significant correlations were also found for light, whereas those for moisture were less good and only partly significant Weighted averages based on presence/absence values and those based on cover/abundance values were generally very similar to each other, but showed differences in their correlations with actual measurements
A training set of relevés was used to calculate optima and ecological amplitudes of the most common species Under certain conditions, the original indicator values were replaced by these optima in order to obtain improved indicator values for Swedish deciduous forests These were correlated with the same test sets of field measurements as the original values and clearly gave better results for light as well as for reaction regarding vascular plants No clear differences were found for moisture and reaction regarding bryophytes For the majority of species, the original indicator values also expressed the ecological optima in Swedish deciduous forests as determined by this study However, several species, most of them preferring more open habitats, showed ecological optima and amplitudes that considerably deviated from central European conditions In conclusion, Ellenberg's indicator values can successfully be used in south Swedish deciduous forests, particularly after calibration of the values according to regional deviations     

No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices

Various regression methods can be used to quantify the relationships between fish populations and their environment. Strong correlations often existing between environmental variables, however, can cause multicollinearity, resulting in overfitting in modeling. This study compares the performance of a regular generalized additive model (GAM) with raw environmental variables as explanatory variables (regular GAM) and a GAM based on principal component analysis (PCA-based GAM) in modeling the relationship between fish richness and diversity indices and environmental variables. The PCA-based GAM tended to perform better than the regular GAM in cross-validation tests, showing a higher prediction precision. The variables identified being significant in modeling differed between the two models, and differences between the two models were also found in the scope and range of predicted richness and diversity indices for demersal fish community. This implies that choices between these two statistical modeling approaches can lead to different ecological interpretations of the relationships between fish communities and their habitats. This study suggests that the PCA-based GAM is a better approach than the original GAM in quantifying the relationship between fish richness and diversity indices and environmental variables if the environmental variables are highly correlated.     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

GLM versus CCA spatial modeling of plant species distribution

Despite the variety of statistical methods available for static modeling of plant distribution, few studies directly compare methods on a common data set. In this paper, the predictive power of Generalized Linear Models (GLM) versus Canonical Correspondence Analysis (CCA) models of plant distribution in the Spring Mountains of Nevada, USA, are compared. Results show that GLM models give better predictions than CCA models because a species-specific subset of explanatory variables can be selected in GLM, while in CCA, all species are modeled using the same set of composite environmental variables (axes). Although both techniques can be readily ported to a Geographical Information System (GIS), CCA models are more readily implemented for many species at once. Predictions from both techniques rank the species models in the same order of quality; i.e. a species whose distribution is well modeled by GLM is also well modeled by CCA and vice-versa. In both cases, species for which model predictions have the poorest accuracy are either disturbance or fire related, or species for which too few observations were available to calibrate and evaluate the model. Each technique has its advantages and drawbacks. In general GLM will provide better species specific-models, but CCA will provide a broader overview of multiple species, diversity, and plant communities.     

Shifts in ecological behaviour of herbaceous forest species along a transect from northern central to North Europe

We investigated the ecological behaviour (the response to environmental factors in the field, synonymous to the term realized niche) of four closely related species pairs (Melica nutans, M. uniflora; Primula veris, P. elatior; Veronica chamaedrys, V. montana; Viola riviniana, V. reichenbachiana) across a transect from northern Central to North Europe. The second-mentioned species of each pair is confined in its geographical distribution to the southern parts of the studied transect. Sample plot data of deciduous forests were compiled from (1) Germany, S Niedersachsen, (2) Germany, northern Schleswig-Holstein, (3) Denmark, and (4) Boreo-nemoral Sweden. We compared the ecological optima and amplitudes of the response curves of species along the gradients for moisture, pH and nitrogen by means of phytosociological data, detrended correspondence analysis (DCA) and Ellenberg indicator values. pH measurements from Sweden were significantly correlated with the corresponding DCA sample plot scores and the plot averages of the Ellenberg values for reaction (pH). In accordance with our main hypothesis, the wide range species appeared to have broader ecological amplitudes on the northern margins of their distributional ranges, especially in Boreo-nemoral Sweden, than in the southern parts of the study area. Our findings are in contrast to theories claiming a reduced niche breadth of range-margin populations of species compared to range-centre populations. The shifts in ecological behaviour were particularly obvious with respect to soil acidity. We believe that these shifts are caused by changes in the competitive relationships between the species: in the north, the total pool of species in deciduous forests is comparatively small. The low species richness is likely to lead to reduced competition and to an expansion of the ecological amplitude, known as competitive release.     

Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes – dispersal, the abiotic environment and biotic interactions – onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock Tsuga canadensis and five additional co-occurring overstory tree species in 35 569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species–environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.     

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.     

Predicting the occurrence of rare Brazilian birds with species distribution models

Species distribution models (SDMs) yield reliable and needed predictions to identify regions that have similar environmental conditions and were used here to predict potential ranges of rare species to identify new localities were they might occur based on their occurrence probability (i.e. niche suitability). We modeled the potential distribution ranges of ten endangered or rare birds from the South American Cerrado biome, using four temperature- and four precipitation-related bioclimatic variables, three topographical variables, and nine different niche modeling methods for each species. We used an ensemble-forecasting approach to reach a consensus scenario to obtain the average distribution for each species based on the five best models generating a distribution map of each species. Model efficiency was related to sample size and not appropriate below ten independent spatial occurrences. The potential distributions of seven species revealed that their occurrence ranges might go beyond their known ranges, but that most of them seem to occur near the regions where they have already been reported. The models of only three species were considered unsatisfactory in helping identify their potential distribution. Models created maps with higher occurrence probability regions where rare Cerrado birds might occur. These range projections can potentially decrease the costs and improve the efficiency of future field searches. On methodological terms, the application of SDMs to predict species ranges should compare different modeling methods and evaluate the effect of sample size on their performance.     

Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico

Although long-standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well-studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model-based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM-derived richness estimates and phenological information for biotic factors affecting species distributions.     

Estimating species tolerance to human perturbation: Expert judgment versus empirical approaches

Species tolerances are frequently used in multi-metric ecological quality indices, and typically have the strongest responses to disturbances. Usually the tolerances of many species are based on expert judgment, with little support from empirical ecological or physiological data. This is particularly true for fish of Mediterranean-type rivers, in which there are many basin-endemic taxa with little information on basic life history traits. In addition, the apparent tolerance of native Mediterranean freshwater fish species to naturally harsh environments and their short-term resilience may mask responses to man-made pressures. Consequently, we evaluated different statistical techniques and procedures for quantifying Mediterranean lotic fish tolerances and compared expert judgment of species tolerances with empirically determined tolerance values. We used eight alternative approaches to compute fish tolerance values for the Mediterranean basins of SW Europe. Three types of approaches were used: (1) those based on the concept of niche breadth along an environment/pressure gradient (five models); (2) those based on deviations from expected values at disturbed sites as predicted by statistical models describing relationships between species and environmental variables (generalized linear modelling (GLM) and generalized additive modelling (GAM), two models); and (3) one model based on the relatively independent contributions of pressure variables to the data variation explained by statistical models. Tolerance estimates based on the used/available pressure gradient and the average general pressure value had the highest mean correlations with the expert judgment classification (mean r = 0.4) and with the other approaches (mean r of 0.48 and 0.46, respectively). The high degree of uncertainty in tolerance estimates should be accounted for when applying them in ecological assessments. Results also highlights the need for better designed research to separate effects of natural and disturbance gradients on species occurrences and densities.     

Assassin bugs of the subfamily Diaspidiinae (Heteroptera: Reduviidae): distribution and potential ecological niches

The prediction and definition of the conditions for the potentially suitable ecological niche of the subfamily Diaspidiinae was the main goal of this study. Our research was based on 283 specimens of all known species of assassin bugs belonging to the subfamily Diaspidiinae stored in European museum collections and a set of 21 environmental variables in the form of a 1 × 1 km grid covering Africa and Madagascar. Based on occurrence localities, as well as a digital elevation model and layer of the tree cover‐continuous fields, information about the distribution of each species is given. Using Maxent software, potentially useful ecological niches were modeled, which allowed for the creation of a map of the potential distribution of the members of this subfamily and for determining their climatic preferences. A jackknife test showed that annual precipitation, annual temperature range and tree cover‐continuous fields were the most important environmental variables affecting the distribution of the subfamily Diaspidiinae. An analysis of climatic preferences suggested that the representatives of the subfamily were linked mainly to the tropical climate. An analysis of environmental variables also showed that the subfamily preferred areas with herbaceous vegetation and some trees, and this preference is probably caused by the food preferences of their prey. On the basis of the museum data on the species occurrence, as well as ecological niche modeling methods, we provided new and valuable information on potentially suitable habitat and the possible range of distribution of the subfamily Diaspidiinae along with its climatic preferences.     

Analyzing niche stability and biogeography of Late Ordovician brachiopod species using ecological niche modeling

Gauging the potential impacts of environmental change on the geographic distributions of species is a central area of modern biogeographic analysis, often involving complex models of species–environment interactions. The geographic distribution of fossil species can also provide a framework to test the impact of environmental change on biogeography and ecological niches of species, yet few paleontological analyses have attacked this question in deep time. Herein we present a quantitative biogeographic analysis to examine the stability of ecological niches and geographic ranges of rhynchonelliform brachiopods during an interval of sea level change preserved in Upper Ordovician strata of the Cincinnati Arch.The intensive sampling, excellent preservation, and numerous prior paleoecological and sedimentological analyses within the tri-state region of Kentucky, Indiana, and Ohio provide a robust framework for detailed paleobiogeographic study. Quantitative biogeographic modeling methods incorporating GIS (Geographic Information Systems) are utilized in order to spatially analyze the geographic ranges of brachiopod species of the Corryville and Mt. Auburn Formations of the C3 (uppermost Maysvillian) depositional sequence.This study employs the ecological niche modeling program GARP (Genetic Algorithm using Rule-set Prediction) to predict the geographic distribution of eight brachiopod species during three time slices within the C3 sequence. This method estimates a species’ geographic range by modeling the ecological niche of the species based on a set of known species occurrence data coupled with environmental data inferred from sedimentologic proxies. Once environmental tolerances for a species are modeled; the species is predicted to occur wherever its preferred set of environmental conditions occurs within the study region.Distributional patterns were reconstructed for three time slices during the C3 sequence. Recovered range predictions were quantitatively analyzed for evidence of temporal range changes. Results indicate that average species range within the study area decreased and species tracked their preferred niche with high fidelity during the transition from the early to middle portions of the C3 depositional sequence, an interval of rapid relative sea level change. However, during the transition from the middle to late portions of the sequence, gradual shallowing within the basin and development of discontinuous habitat patches correlates with niche evolution of five of the eight species modeled. The average area a species occupied within the basin increased during this interval, but there is a mixed response including both increases and decreases in range size within the study group. In general, the species that exhibit niche evolution increased their geographic range size while those that continue to track their niche with high fidelity experience a decrease in geographic range size. During the latter half of the C3 sequence, previously continuous habitats become fragmented, thereby isolating individual populations and providing a mechanism for niche evolution. The rate of sea level change and the corresponding fragmentation of previously continuous habitats into isolated patches appear to be the primary controls on both mean geographic range size and relative degree of niche evolution.