Using ground‐derived data to assess the environmental niche of the spinose ear tick,Otobius megnini

Four layers of environmental information, namely 1) monthly atmospheric temperature and rainfall, 2) annual ground and underground moisture, evaporation, surface adjacent specific humidity, and temperature, 3) monthly Normalized Derived Vegetation Index (NDVI), and 4) soil physical attributes, were used separately to define the expected geographical distribution and environmental niche of the spinose ear tick, Otobius megnini (Dugès) (Acari: Ixodida: Argasidae), an endophilic argasid, in both tropical and neotropical regions. The best predictive values were obtained from ground‐derived climate. Air‐derived features ranked second. The remaining environmental information had poor discriminatory abilities. The most informative variables in the distribution of neotropical populations are ground temperatures, with surface humidity ranking second. In the tropics, surface humidity is the most important factor delineating the distribution of O. megnini. Marginality scores are similar for tick populations in both biogeographical regions, but specialization factors are different, supporting the findings that both populations are regulated by different variables. Similarly, models trained with records of one biogeographical region and projected into the other one, resulted in poorer predictions than when trained with the homologous set of records. Populations of the tick in the tropics experience a different range of temperatures than their neotropical relatives, whereas marginality scores are similar. The conclusion is that each population uses particular portions of the environmental niche, probably because of different climate or competitor constraints on either biogeographical region.     

Living on the edge: do central and marginal populations of plants differ in habitat suitability?

The performance of populations at the edge of specie’s distribution range may differ substantially from central populations. Here, we develop a modelling framework to estimate ecological niches (i.e. climatic) of four locally endangered plant species and measure the distance of marginal (geographically) populations to the species’ niche centroid in order to analyse whether marginal populations are outside of the optimal ecological niche of each species. Our results show that for three of the four studied species, which have their populations located at the margins of their distribution ranges, are also at the margins of their climate gradients. These results would support the hypothesis that marginality within the set of habitable conditions (i.e. ecological niche) may represent an important determinant on performance of some plant populations, as well as a plausible explanation to the degree of stagnancy or regression experienced by species in those regions where their populations are at the margin of their ecological niche and/or they are restricted to microrefugia with ecological conditions very different from those around them. Finally, this study aims to be a theoretical base from which to advance on, including other types of factors (e.g. biotic interactions, topography, human influence and population fluctuations through time), which will allow for a better understanding of the complex network of factors that occur in marginal plant populations.     

A comparative evaluation of presence-only methods for modelling species distribution

In spite of increasing application of presence-only models in ecology and conservation and the growing number of such models, little is known about the relative performance of different modelling methods, and some of the leading models (e.g. GARP and ENFA) have never been compared with one another. Here we compare the performance of six presence-only models that have been selected to represent an increasing level of model complexity [BIOCLIM, HABITAT, Mahalanobis distance (MD), DOMAIN, ENFA, and GARP] using data on the distribution of 42 species of land snails, nesting birds, and insectivorous bats in Israel. The models were calibrated using data from museum collections and observation databases, and their predictions were evaluated using Cohen's Kappa based on field data collected in a standardized sampling design covering most parts of Israel. Predictive accuracy varied between modelling methods with GARP and MD showing the highest accuracy, BIOCLIM and ENFA showing the lowest accuracy, and HABITAT and DOMAIN showing intermediate accuracy levels. Yet, differences between the various models were relatively small except for GARP and MD that were significantly more accurate than BIOCLIM and ENFA. In spite of large differences among species in prevalence and niche width, neither prevalence nor niche width interacted with the modelling method in determining predictive accuracy. However, species with relatively narrow niches were modelled more accurately than species with wider niches. Differences among species in predictive accuracy were highly consistent over all modelling methods, indicating the need for a better understanding of the ecological and geographical factors that influence the performance of species distribution models.     

The tick Ixodes ricinus: distribution and climate preferences in the western Palaearctic

In this study, multivariate spatial clustering on monthly normalized difference vegetation index (NDVI) maps is used to classify ecological regions over the western Palaearctic. This classification is then used to delineate the distribution and climate preferences of populations (clades) of the tick Ixodes ricinus L. (Acari: Ixodidae) from a geographically extensive dataset of tick records and a gridded 2.5-km resolution climate dataset. Using monthly layers of the NDVI, regions of similar ecological attributes were defined and nine populations with significant differences in critical climate parameters (P< 0.005) were detected. Grouping of tick records according to other categories, such as political divisions, a 4 degrees x 4 degrees grid overlying the study area, or the CORINE) and USGS) vegetation classification schemes did not provided significantly separated populations (P = 0.094-0.304). Factor analysis and hierarchical tree clustering provided an ecological overview of these tick clades: two Mediterranean and one Scandinavian (western) clades are clearly separated from a node that includes clades of different parts of central Europe and the British Isles, with contrasting affinities between the different clades. The capture records of these ecologically separated clades produce a clear bias when bioclimate envelope modelling is applied to the mapping of habitat suitability for the tick in the western Palaearctic. The best-performing methods (Cohen's kappa = 0.834-0.912) use partial models developed with data from each ecoregion, which are then overlapped over the region of study. It is concluded that the use of ecologically derived ecoregions is an objective step in assessing the presence of ecologically different clades, and provides a guide in the development of data partitioning for habitat suitability modelling.     

Presence-absence versus presence-only modelling methods for predicting bird habitat suitability

Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide‐ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.     

Uncertainty of bioclimate envelope models based on the geographical distribution of species

Aim We explored the effects of prevalence, latitudinal range and spatial autocorrelation of species distribution patterns on the accuracy of bioclimate envelope models of butterflies. Location Finland, northern Europe. Methods The data of a national butterfly atlas survey (NAFI) carried out in 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAM) were constructed, for each of 98 species, to estimate the probability of occurrence as a function of climate variables. Model performance was measured using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were related to the species’ geographical attributes using multivariate GAM. Results Accuracies of the climate–butterfly models varied from low to very high (AUC values 0.59–0.99), with a mean of 0.79. The modelling performance was related negatively to the latitudinal range and prevalence, and positively to the spatial autocorrelation of the species distribution. These three factors accounted for 75.2% of the variation in the modelling accuracy. Species at the margin of their range or with low prevalence were better predicted than widespread species, and species with clumped distributions better than scattered dispersed species. Main conclusions The results from this study indicate that species’ geographical attributes highly influence the behaviour and uncertainty of species–climate models, which should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

A comparison of approaches for modelling the occurrence of marine animals

Approaches for modelling the distribution of animals in relation to their environment can be divided into two basic types, those which use records of absence as well as records of presence and those which use only presence records. For terrestrial species, presence–absence approaches have been found to produce models with greater predictive ability than presence-only approaches. This study compared the predictive ability of both approaches for a marine animal, the harbour porpoise (Phoceoena phocoena). Using data on the occurrence of harbour porpoises in the Sea of Hebrides, Scotland, the predictive abilities of one presence–absence approach (generalised linear modelling—GLM) and three presence-only approaches (Principal component analysis—PCA, ecological niche factor analysis—ENFA and genetic algorithm for rule-set prediction—GARP) were compared. When the predictive ability of the models was assessed using receiver operating characteristic (ROC) plots, the presence–absence approach (GLM) was found to have the greatest predictive ability. However, all approaches were found to produce models that predicted occurrence significantly better than a random model and the GLM model did not perform significantly better than ENFA and GARP. The PCA had a significantly lower predictive ability than GLM but not the other approaches. In addition, all models predicted a similar spatial distribution. Therefore, while models constructed using presence–absence approaches are likely to provide the best understanding of species distribution within a surveyed area, presence-only models can perform almost as well. However, careful consideration of the potential limitations and biases in the data, especially with regards to representativeness, is needed if the results of presence-only models are to be used for conservation and/or management purposes. Guest editor: V. D. Valavanis Essential Habitat Mapping in the Mediterranean     

Scaling of species distribution models across spatial resolutions and extents along a biogeographic gradient. The case of the Iberian mole Talpa occidentalis

Scaling is a key process in modelling approaches since it allows for translating information from one scale to another. However, the success of this procedure may depend on ‘source’ and ‘target’ scales, but also on the biogeographic/ecological context of the study area. We aimed to quantify the performance and success of scaling species distribution model (SDM) predictions across spatial resolution and extent along a biogeographic gradient using the Iberian mole as study case. We ran separate MaxEnt models at two extents (national and regional) using independent datasets (species locations and environmental predictors) collected at 10 km and 50 m resolutions respectively. Model performance and success of scaling SDMs were quantified on the basis of accuracy measures and spatial predictions. Complementarily, we calculated marginality and tolerance as indicators of habitat availability and niche truncation along the biogeographic gradient. Model performance increased with resolution and extent, as well as from north to south (mainly for high resolution models). When regional models were validated at different scales, their performance reduced severely, particularly in the case of coarse resolution models (some of them performed worse than random). However, when the 10 km‐national model was downscaled within regions, it performed better (AUC_test: 0.82, 0.85 and 0.55 respectively for Galicia, Madrid and Granada) than models specifically calibrated within each region at 10 km (0.47, 0.65, 0.44). Indeed, it also had a better accuracy when projected at 50 m (0.77, 0.91, 0.79) than models fitted at that resolution (0.62, 0.83, 0.96) in two of the three cases. The success of scaling model predictions decreased along the biogeographic gradient, being these differences associated to niche truncation. Models representing non‐truncated niches were more successfully scaled across resolutions and extents (particularly in areas not offering all possible habitats for species), which has important implications for SDM applications.     

Predicted Distribution of Major Malaria Vectors Belonging to the Anopheles dirus Complex in Asia: Ecological Niche and Environmental Influences

Methods derived from ecological niche modeling allow to define species distribution based on presence-only data. This is particularly useful to develop models from literature records such as available for the Anopheles dirus complex, a major group of malaria mosquito vectors in Asia. This research defines an innovative modeling design based on presence-only model and hierarchical framework to define the distribution of the complex and attempt to delineate sibling species distribution and environmental preferences. At coarse resolution, the potential distribution was defined using slow changing abiotic factors such as topography and climate representative for the timescale covered by literature records of the species. The distribution area was then refined in a second step using a mask of current suitable land cover. Distribution area and ecological niche were compared between species and environmental factors tested for relevance. Alternatively, extreme values at occurrence points were used to delimit environmental envelopes. The spatial distribution for the complex was broadly consistent with its known distribution and influencing factors included temperature and rainfall. If maps developed from environmental envelopes gave similar results to modeling when the number of sites was high, the results were less similar for species with low number of recorded presences. Using presence-only models and hierarchical framework this study not only predicts the distribution of a major malaria vector, but also improved ecological modeling analysis design and proposed final products better adapted to malaria control decision makers. The resulting maps can help prioritizing areas which need further investigation and help simulate distribution under changing conditions such as climate change or reforestation. The hierarchical framework results in two products one abiotic based model describes the potential maximal distribution and remains valid for decades and the other including a biotic mask easy to update with frequently available information gives current species distribution.     

Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties

Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species’ ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.     

Pitch the niche – taking responsibility for the concepts we use in ecology and species distribution modelling

In a discussion it is often easier to staunchly reject or offer resolute support for an idea. This third paper on the niche concept aims to develop a balanced argument by exploring general principles for determining an appropriate level for pitching the niche concept that will guide better use and less abuse of niche concepts. To do this we first have to accept that niche concepts are not necessarily essential for ecology. Rather than to improve niche concepts, our aim should then be to pitch the niche in terms of ecology. This aim helps us develop an ‘ultimate goal of the niche’ by which we can evaluate the concepts we use. For species distribution modelling, there has been a focus on the niche as an equilibrium outcome that perhaps has less relevance for disequilibrium situations (e.g. climate change projections). As is the case for much of ecology, more causal explanations of species' distributions use alternative terminologies and less frequently use the word ‘niche’. We suggest that niche concepts that are better aligned with the rest of ecology could arise from taking more responsibility for our own implementations, and by explaining our models with terms other than niche. A general, holistic niche concept promotes this view and promotes practical thinking about what we are modelling and how we interpret those models, which in turn should help inspire and support innovative modelling approaches in species distribution modelling.     

Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance

Aim To investigate the impact of geographical bias on the performance of ecological niche models for invasive plant species. Location South Africa and Australia. Methods We selected 10 Australian plants invasive in South Africa and nine South African plants invasive in Australia. Geographical bias was simulated in occurrence records obtained from the native range of a species to represent two scenarios. For the first scenario (A, worst‐case) a proportion of records were excluded from a specific region of a species’ range and for the second scenario (B, less extreme) only some records were excluded from that specific region of the range. Introduced range predictions were produced with the Maxent modelling algorithm where models were calibrated with datasets from these biased occurrence records and 19 bioclimatic variables. Models were evaluated with independent test data obtained from the introduced range of the species. Geographical bias was quantified as the proportional difference between the occurrence records from a control and a biased dataset, and environmental bias was expressed as either the difference in marginality or tolerance between these datasets. Model performance [assessed using the conventional and modified AUC (area under the curve of receiver‐operating characteristic plots) and the maximum true skill statistic] was compared between models calibrated with occurrence records from a biased dataset and a control dataset. Results We found considerable variation in the relationship between geographical and environmental bias. Environmental bias, expressed as the difference in marginality, differed significantly across treatments. Model performance did not differ significantly among treatments. Regions predicted as suitable for most of the species were very similar when compared between a biased and control dataset, with only a few exceptions. Main conclusions The geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this we did not find a significant effect on model performance. Differences in the environmental spaces occupied by the species in their native and invaded ranges may explain why we did not find a significant effect on model performance.     

Model-based uncertainty in species range prediction

Aim Many attempts to predict the potential range of species rely on environmental niche (or ‘bioclimate envelope’) modelling, yet the effects of using different niche‐based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy‐guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence‐only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy‐guiding applications along with a full appreciation of uncertainty.     

Cryptic niche conservatism among evolutionary lineages of an invasive lizard

Aim There is increasing evidence that the quality and breadth of ecological niches vary among individuals, populations, evolutionary lineages and therefore also across the range of a species. Sufficient knowledge about niche divergence among clades might thus be crucial for predicting the invasion potential of species. We tested for the first time whether evolutionary lineages of an invasive species vary in their climate niches and invasive potential. Furthermore, we tested whether lineage‐specific models show a better performance than combined models. Location Europe. Methods We used species distribution models (SDMs) based on climatic information at native and invasive ranges to test for intra‐specific niche divergence among mitochondrial DNA (mtDNA) clades of the invasive wall lizard Podarcis muralis. Using DNA barcoding, we assigned 77 invasive populations in Central Europe to eight geographically distinct evolutionary lineages. Niche similarity among lineages was assessed and the predictive power of a combination of clade‐specific SDMs was compared with a combined SDM using the pooled records of all lineages. Results We recorded eight different invasive mtDNA clades in Central Europe. The analysed clades had rather similar realized niches in their native and invasive ranges, whereas inter‐clade niche differentiation was comparatively strong. However, we found only a weak correlation between geographic origin (i.e. mtDNA clade) and invasive occurrences. Clades with narrow realized niches still became successful invaders far outside their native range, most probably due to broader fundamental niches. The combined model using data for all invasive lineages achieved a much better prediction of the invasive potential. Conclusions Our results indicate that the observed niche differentiation among evolutionary lineages is mainly driven by niche realization and not by differences in the fundamental niches. Such cryptic niche conservatism might hamper the success of clade‐specific niche modelling. Cryptic niche conservatism may in general explain the invasion success of species in areas with apparently unsuitable climate.     

Body‐mass or sex‐biased tick parasitism in roe deer (Capreolus capreolus)? A GAMLSS approach

Macroparasites feeding on wildlife hosts follow skewed distributions for which basic statistical approaches are of limited use. To predict Ixodes spp. tick burden on roe deer, we applied Generalized Additive Models for Location, Scale and Shape (GAMLSS) which allow incorporating a variable dispersion. We analysed tick burden of 78 roe deer, sampled in a forest region of Germany over a period of 20 months. Assuming a negative binomial error distribution and controlling for ambient temperature, we analysed whether host sex and body mass affected individual tick burdens. Models for larval and nymphal tick burden included host sex, with male hosts being more heavily infested than female ones. However, the influence of host sex on immature tick burden was associated with wide standard errors (nymphs) or the factor was marginally significant (larvae). Adult tick burden was positively correlated with host body mass. Thus, controlled for host body mass and ambient temperature, there is weak support for sex-biased parasitism in this system. Compared with models which assume linear relationships, GAMLSS provided a better fit. Adding a variable dispersion term improved only one of the four models. Yet, the potential of modelling dispersion as a function of variables appears promising for larger datasets.     

Reconstructing the demise of Tethyan plants: climate‐driven range dynamics of Laurus since the Pliocene

Aim Climate changes are thought to be responsible for the retreat and eventual extinction of subtropical lauroid species that covered much of Europe and North Africa during the Palaeogene and early Neogene; little is known, however, of the spatial and temporal patterns of this demise. Herein we calibrate ecological niche models to assess the climatic requirements of Laurus L. (Lauraceae), an emblematic relic from the Tethyan subtropical flora, subsequently using these models to infer how the range dynamics of Laurus were affected by Plio‐Pleistocene climate changes. We also provide predictions of likely range changes resulting from future climatic scenarios. Location The Mediterranean Basin and Macaronesian islands (Canaries, Madeira, Azores). Methods We used a maximum‐entropy algorithm (Maxent) to model the relationship between climate and Laurus distribution over time. The models were fitted both to the present and to the middle Pliocene, based on fossil records. We employed climatic reconstructions for the mid‐Pliocene (3 Ma), the Last Glacial Maximum (21 ka) and a CO₂‐doubling future scenario to project putative species distribution in each period. We validated the model projections with Laurus fossil and present occurrences. Results Laurus preferentially occupied warm and moist areas with low seasonality, showing a marked stasis of its climatic niche. Models fitted to Pliocene conditions successfully predicted the current species distribution. Large suitable areas existed during the Pliocene, which were strongly reduced during the Pleistocene, but humid refugia within the Mediterranean Basin and Macaronesian islands enabled long‐term persistence. Future climate conditions are likely to re‐open areas suitable for colonization north of the current range. Main conclusions The climatic requirements of Laurus remained virtually unchanged over the last 3 Myr. This marked niche conservatism imposed largely deterministic range dynamics driven by climate conditions. This species's relatively high drought tolerance might account for the survival of Laurus in continental Europe throughout the Quaternary whilst other Lauraceae became extinct. Climatic scenarios for the end of this century would favour an expansion of the species's range towards northern latitudes, while severely limiting southern populations due to increased water stress.     

Simulating tick distributions over sub-Saharan Africa: the use of observed and simulated climate surfaces

Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or high‐resolution model‐derived climate data [Division of Atmospheric Research limited‐area model (DARLAM)] for predictive species modelling, using tick distributions from sub‐Saharan Africa. Location The analysis is restricted to sub‐Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 × 60 km. Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions The use of data derived from high‐resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km² for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.     

Empirical realised niche models for British higher and lower plants – development and preliminary testing

Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables? Location: Great Britain. Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi‐natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover‐weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity. Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables. Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.     

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence‐only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC‐induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.