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.     

Species traits are associated with the quality of bioclimatic models

Aim To analyse the effects of nine species trait variables on the accuracy of bioclimatic envelope models built for 98 butterfly species. Location Finland, northern Europe. Methods Data from a national butterfly atlas monitoring scheme (NAFI) collected from 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAMs) were constructed for 98 butterfly species to predict their occurrence as a function of climatic variables. Modelling accuracy was measured as the cross‐validation area under the curve (AUC) of the receiver–operating characteristic plot. Observed variation in modelling accuracy was related to species traits using multiple GAMs. The effects of phylogenetic relatedness among butterflies were accounted for by using generalized estimation equations. Results The values of the cross‐validation AUC for the 98 species varied between 0.56 and 1.00 with a mean of 0.79. Five species trait variables were included in the GAM that explained 71.4% of the observed variation in modelling accuracy. Four variables remained significant after accounting for phylogenetic relatedness. Species with high mobility and a long flight period were modelled less accurately than species with low mobility and a short flight period. Large species (>50 mm in wing span) were modelled more accurately than small ones. Species inhabiting mires had especially poor models, whereas the models for species inhabiting rocky outcrops, field verges and open fells were more accurate compared with other habitats. Main conclusions These results draw attention to the importance of species traits variables for species–climate impact models. Most importantly, species traits may have a strong impact on the performance of bioclimatic envelope models, and certain trait groups can be inherently difficult to model reliably. These uncertainties should be taken into account by downweighting or excluding species with such traits in studies applying bioclimatic modelling and making assessments of the impacts of climate change.     

The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

Climate change and the future distributions of aquatic macrophytes across boreal catchments

Aim Aquatic–terrestrial ecotones are vulnerable to climate change, and degradation of the emergent aquatic macrophyte zone would have severe ecological consequences for freshwater, wetland and terrestrial ecosystems. Our aim was to uncover future changes in boreal emergent aquatic macrophyte zones by modelling the occurrence and percentage cover of emergent aquatic vegetation under different climate scenarios in Finland by the 2050s. Location Finland, northern Europe. Methods Data derived from different GIS sources were used to estimate future emergent aquatic macrophyte distributions in all catchments in Finland (848 in total). We used generalized additive models (GAM) with a full stepwise selection algorithm and Akaike information criterion to explore the main environmental determinates (climate and geomorphology) of emergent aquatic macrophyte distributions, which were derived from the national subclass of CORINE land‐cover classification. The accuracy of the distribution models (GAMs) was cross‐validated, using percentage of explained deviance and the area under the curve derived from the receiver‐operating characteristic plots. Results Our results indicated that emergent aquatic macrophytes will expand their distributions northwards from the current catchments and percentage cover will increase in all of the catchments in all climate scenarios. Growing degree‐days was the primary determinant affecting distributions of emergent aquatic macrophytes. Inclusion of geomorphological variables clearly improved model performance in both model exercises compared with pure climate variables. Main conclusions Emergent aquatic macrophyte distributions will expand due to climate change. Many emergent aquatic plant species have already expanded their distributions during the past decades, and this process will continue in the years 2051–80. Emergent aquatic macrophytes pose an increasing overgrowth risk for sensitive macrophyte species in boreal freshwater ecosystems, which should be acknowledged in management and conservation actions. We conclude that predictions based on GIS data can provide useful ‘first‐filter’ estimates of changes in aquatic–terrestrial ecotones.     

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.     

Potential changes in the distributions of latitudinally restricted Australian butterfly species in response to climate change

This study assessed potential changes in the distributions of Australian butterfly species in response to global warming. The bioclimatic program, BIOCLIM, was used to determine the current climatic ranges of 77 butterfly species restricted to Australia. We found that the majority of these species had fairly wide climatic ranges in comparison to other taxa, with only 8% of butterfly species having a mean annual temperature range spanning less than 3 °C. The potential changes in the distributions of 24 butterfly species under four climate change scenarios for 2050 were also modelled using BIOCLIM. Results suggested that even species with currently wide climatic ranges may still be vulnerable to climate change; under a very conservative climate change scenario (with a temperature increase of 0.8–1.4 °C by 2050) 88% of species distributions decreased, and 54% of species distributions decreased by at least 20%. Under an extreme scenario (temperature increase of 2.1–3.9 °C by 2050) 92% of species distributions decreased, and 83% of species distributions decreased by at least 50%. Furthermore, the proportion of the current range that was contained within the predicted range decreased from an average of 63% under a very conservative scenario to less than 22% under the most extreme scenario. By assessing the climatic ranges that species are currently exposed to, the extent of potential changes in distributions in response to climate change and details of their life histories, we identified species whose characteristics may make them particularly vulnerable to climate change in the future.     

Dominant climate influences on North American bird distributions

Aim Geographic distributions of species are constrained by several factors acting at different scales, with climate assumed to be a major determinant at broad extents. Recent studies, however, have challenged this statement and indicated that climate may not dominate among the factors governing geographic distributions of species. Here, we argue that these results are misleading due to the lack of consideration of the geographic area that has been accessible to the species. Location North America. Methods We generated null distributions for 75 North American endemic and 19 non‐endemic bird species. For each species, climatic envelopes of observed and null distributions were modelled using neural networks and generalized linear models, and seven climatic predictors. Values of the area under the receiver–operating characteristic curve (AUC) based on models of observed distributions were compared with corresponding AUC values for the null distributions. Results More than 82% of the endemic species showed AUC higher for the observed than for the null distributions, while 63% of the non‐endemic species showed such a pattern. Main conclusions We demonstrate a dominant climatic signal in shaping North American bird distributions. Our results attest to the importance of climate in determining species distributions and support the use of climate‐envelope models for estimating potential distributional areas at the appropriate spatial scales.     

Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions

Aim We explore the impact of calibrating ecological niche models (ENMs) using (1) native range (NR) data versus (2) entire range (ER) data (native and invasive) on projections of current and future distributions of three Hieracium species. Location H. aurantiacum, H. murorum and H. pilosella are native to Europe and invasive in Australia, New Zealand and North America. Methods Differences among the native and invasive realized climatic niches of each species were quantified. Eight ENMs in BIOMOD were calibrated with (1) NR and (2) ER data. Current European, North American and Australian distributions were projected. Future Australian distributions were modelled using four climate change scenarios for 2030. Results The invasive climatic niche of H. murorum is primarily a subset of that expressed in its native range. Invasive populations of H. aurantiacum and H. pilosella occupy different climatic niches to those realized in their native ranges. Furthermore, geographically separate invasive populations of these two species have distinct climatic niches. ENMs calibrated on the realized niche of native regions projected smaller distributions than models incorporating data from species’ entire ranges, and failed to correctly predict many known invasive populations. Under future climate scenarios, projected distributions decreased by similar percentages, regardless of the data used to calibrate ENMs; however, the overall sizes of projected distributions varied substantially. Main conclusions This study provides quantitative evidence that invasive populations of Hieracium species can occur in areas with different climatic conditions than experienced in their native ranges. For these, and similar species, calibration of ENMs based on NR data only will misrepresent their potential invasive distribution. These errors will propagate when estimating climate change impacts. Thus, incorporating data from species’ entire distributions may result in a more thorough assessment of current and future ranges, and provides a closer approximation of the elusive fundamental niche.     

The ability of climate envelope models to predict the effect of climate change on species distributions

Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO₂ conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。     

Predicting the past distribution of species climatic niches

Predicting past distributions of species climatic niches, hindcasting, by using climate envelope models (CEMs) is emerging as an exciting research area. CEMs are used to examine veiled evolutionary questions about extinctions, locations of past refugia and migration pathways, or to propose hypotheses concerning the past population structure of species in phylogeographical studies. CEMs are sensitive to theoretical assumptions, to model classes and to projections in non-analogous climates, among other issues. Studies hindcasting the climatic niches of species often make reference to these limitations. However, to obtain strong scientific inferences, we must not only be aware of these potential limitations but we must also overcome them. Here, I review the literature on hindcasting CEMs. I discuss the theoretical assumptions behind niche modelling, i.e. the stability of climatic niches through time and the equilibrium of species with climate. I also summarize a set of 'recommended practices' to improve hindcasting. The studies reviewed: (1) rarely test the theoretical assumptions behind niche modelling such as the stability of species climatic niches through time and the equilibrium of species with climate; (2) they only use one model class (72% of the studies) and one palaeoclimatic reconstruction (62.5%) to calibrate their models; (3) they do not check for the occurrence of non-analogous climates (97%); and (4) they do not use independent data to validate the models (72%). Ignoring the theoretical assumptions behind niche modelling and using inadequate methods for hindcasting CEMs may well entail a cascade of errors and naïve ecological and evolutionary inferences. We should also push integrative research lines linking macroecology, physiology, population biology, palaeontology, evolutionary biology and CEMs for a better understanding of niche dynamics across space and time.     

Species traits explain recent range shifts of Finnish butterflies

This study provides a novel systematic comparative analysis of the species characteristics affecting the range margin shifts in butterflies towards higher latitudes, while taking phylogenetic relatedness among species into account. We related observed changes in the northern range margins of 48 butterfly species in Finland between two time periods (1992–1996 and 2000–2004) to 11 species traits. Species with positive records in at least ten 10 km × 10 km grid squares (in the Finnish National Butterfly Recording Scheme, NAFI) in both periods were included in the study. When corrected for range size change, the 48 butterfly species had shifted their range margins northwards on average by 59.9 km between the study periods, with maximum shifts of over 300 km for three species. This rate of range shifts exceeds all previously reported records worldwide. Our findings may be explained by two factors: the study region is situated in higher latitudes than in most previous studies and it focuses on the period of most prominent warming during the last 10–15 years. Several species traits exhibited a significant univariate relationship with the range margin shift according to generalized estimation equations (GEE) taking into account the phylogenetic relatedness among species. Nonthreatened butterflies had on average expanded their ranges strongly northwards (84.5 km), whereas the distributions of threatened species were stationary (−2.1 km). Hierarchical partitioning (HP) analysis indicated that mobile butterflies living in forest edges and using woody plants as their larval hosts exhibited largest range shifts towards the north. Thus, habitat availability and dispersal capacity of butterfly species are likely to determine whether they will be successful in shifting their ranges in response to the warming climate.     

Can climate data from METEOSAT improve wildlife distribution models?

Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez-Seoane et al. using habitat variables for three agro-steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate-habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate-habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change.     

The contributions of topoclimate and land cover to species distributions and abundance: fine‐resolution tests for a mountain butterfly fauna

Aim Models relating species distributions to climate or habitat are widely used to predict the effects of global change on biodiversity. Most such approaches assume that climate governs coarse‐scale species ranges, whereas habitat limits fine‐scale distributions. We tested the influence of topoclimate and land cover on butterfly distributions and abundance in a mountain range, where climate may vary as markedly at a fine scale as land cover. Location Sierra de Guadarrama (Spain, southern Europe) Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) in a 10,800 km² region, and derived generalized linear models (GLMs) for species occurrence and abundance based on topoclimatic (elevation and insolation) or habitat (land cover, geology and hydrology) variables sampled at 100‐m resolution using GIS. Models for each year were tested against independent data from the alternate year, using the area under the receiver operating characteristic curve (AUC) (distribution) or Spearman's rank correlation coefficient (r_s) (abundance). Results In independent model tests, 74% of occurrence models achieved AUCs of > 0.7, and 85% of abundance models were significantly related to observed abundance. Topoclimatic models outperformed models based purely on land cover in 72% of occurrence models and 66% of abundance models. Including both types of variables often explained most variation in model calibration, but did not significantly improve model cross‐validation relative to topoclimatic models. Hierarchical partitioning analysis confirmed the overriding effect of topoclimatic factors on species distributions, with the exception of several species for which the importance of land cover was confirmed. Main conclusions Topoclimatic factors may dominate fine‐resolution species distributions in mountain ranges where climate conditions vary markedly over short distances and large areas of natural habitat remain. Climate change is likely to be a key driver of species distributions in such systems and could have important effects on biodiversity. However, continued habitat protection may be vital to facilitate range shifts in response to climate change.     

Climatic Niche Evolution in New World Monkeys (Platyrrhini)

Despite considerable interest in recent years on species distribution modeling and phylogenetic niche conservatism, little is known about the way in which climatic niches change over evolutionary time. This knowledge is of major importance to understand the mechanisms underlying limits of species distributions, as well as to infer how different lineages might be affected by anthropogenic climate change. In this study we investigate the tempo and mode climatic niche evolution in New World monkeys (Platyrrhini). Climatic conditions found throughout the distribution of 140 primate species were investigated using a principal component analysis, which indicated that mean temperature (particularly during the winter) is the most important climatic correlate of platyrrhine geographical distributions, accounting for nearly half of the interspecific variation in climatic niches. The effects of precipitation were associated with the second principal component, particularly with respect to the dry season. When models of trait evolution were fit to scores on each of the principal component axes, significant phylogenetic signal was detected for PC1 scores, but not for PC2 scores. Interestingly, although all platyrrhine families occupied comparable regions of climatic space, some aotid species such as Aotus lemurinus, A. jorgehernandezi, and A. miconax show highly distinctive climatic niches associated with drier conditions (high PC2 scores). This shift might have been made possible by their nocturnal habits, which could serve as an exaptation that allow them to be less constrained by humidity during the night. These results underscore the usefulness of investigating explicitly the tempo and mode of climatic niche evolution and its role in determining species distributions.     

Ice age distributions of European small mammals: insights from species distribution modelling

Aim In addition to the traditionally recognized Last Glacial Maximum (LGM, 21 ka) refuge areas in the Mediterranean region, more northerly LGM distributions for temperate and boreal taxa in central and eastern Europe are increasingly being discussed based on palaeoecological and phylogeographical evidence. Our aim was to investigate the potential refuge locations using species distribution modelling to estimate the geographical distribution of suitable climatic conditions for selected rodent species during the LGM. Location Eurasia. Methods Presence/absence data for seven rodent species with range limits corresponding to the limits of temperate or boreal forest or arctic tundra were used in the analysis. We developed predictive distribution models based on the species present‐day European distributions and validated these against their present‐day Siberian ranges. The models with the best predictors of the species distributions across Siberia were projected onto LGM climate simulations to assess the distribution of climatically suitable areas. Results The best distribution models provided good predictions of the present‐day Siberian ranges of the study species. Their LGM projections showed that areas with a suitable LGM climate for the three temperate species (Apodemus flavicollis, Apodemus sylvaticus and Microtus arvalis) were largely restricted to the traditionally recognized southern refuge areas, i.e. mainly in the Mediterranean region, but also southernmost France and southern parts of the Russian Plain. In contrast, suitable climatic conditions for the two boreal species (Clethrionomys glareous and Microtus agrestis) were predicted as far north as southern England and across southern parts of central and eastern Europe eastwards into the Russian Plain. For the two arctic species (Lemmus lemmus and Microtus oeconomus), suitable climate was predicted from the Atlantic coast eastward across central Europe and into Russia. Main conclusions Our results support the idea of more northerly refuge areas in Europe, indicating that boreal species would have found suitable living conditions over much of southern central and eastern Europe and the Russian Plain. Temperate species would have primarily found suitable conditions in the traditional southern refuge areas, but interestingly also in much of the southern Russian Plain.     

Threat spots and environmental determinants of red-listed plant,butterfly and bird species in boreal agricultural environments

The aims of this study were (1) to examine the geographic distribution of red-listed species of agricultural environments and identify their national threat spots (areas with high diversity of threatened species) in Finland and (2) to determine the main environmental variables related to the richness and occurrence patterns of red-listed species. Atlas data of 21 plant, 17 butterfly and 11 bird species recorded using 10 km grid squares were employed in the study. Generalized additive models (GAMs) were constructed separately for species richness and occurrence of individual species of the three species groups using climate and land cover predictor variables. The predictive accuracy of models, as measured using correlation between the observed and predicted values and AUC statistics, was generally good. Temperature-related variables were the most important determinants of species richness and occurrence of all three taxa. In addition, land cover variables had a strong effect on the distribution of species. Plants and butterflies were positively related to the cover of grasslands and birds to small-scale agricultural mosaic as well as to arable land. Spatial coincidence of threat spots of plants, butterflies and birds was limited, which emphasizes the importance of considering the potentially contrasting environmental requirements of different taxa in conservation planning. Further, it is obvious that the maintenance of various non-crop habitats and heterogeneous agricultural landscapes has an essential role in the preservation of red-listed species of boreal rural environments.     

Escape from natural enemies during climate-driven range expansion: a case study

Abstract.  1. A major, and largely unexplored, uncertainty in projecting the impact of climate change on biodiversity is the consequence of altered interspecific interactions, for example between parasitoids and their hosts. The present study investigated parasitism in the Brown Argus butterfly, Aricia agestis ; a species that has expanded northward in Britain during the last 30  years in association with climate warming.
2.  Aricia agestis larvae suffered lower mortality from parasitoids in newly colonised areas compared with long-established populations. This result was consistent over four consecutive generations (2 years) when comparing one population of each type, and also when several populations within the historical and recently colonised range of the species were compared within a single year. Thus, A. agestis appears to be partially escaping from parasitism as it expands northwards.
3. Reduced parasitism occurred despite the fact that several of the parasitoid species associated with A. agestis were already present in the newly colonised areas, supported predominantly by an alternative host species, the Common Blue butterfly, Polyommatus icarus .
4. As the species expand their distributions into areas of increased climatic suitability, invasion fronts may escape from natural enemies, enhancing rates of range expansion. The results suggest that the decoupling of interspecific interactions may allow some species to exploit a wider range of environments and to do so more rapidly than previously thought possible.     

Distribution patterns of boreal marshland birds: modelling the relationships to land cover and climate

Aim The aims of this work were (1) to study how well land‐cover and climatic data are capable of explaining distribution patterns of ten bird species breeding and/or feeding primarily on marshes and other wetlands and (2) to compare the differences between red‐listed and common marshland species in explanatory variables, and to study the predictability of their distribution patterns. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were used in the analyses. Land‐cover data based on CORINE (Coordination of Information on the Environment) classification and climatic variables were compiled using the same 10 × 10 km grid. Generalized additive models (GAM) with a stepwise selection procedure were used to select relevant explanatory variables and to examine the complexity of the response shapes of the different species to each variable. The original data set was randomly divided into model training (70%) and model evaluation (30%) sets. The final models of common and red‐listed bird species richness were validated by fitting them to the model evaluation set, and the correlation between observed and predicted species richness was calculated. We assessed the discrimination ability of the binary models (single species) with the area under the curve (AUC) of a receiver operating characteristic (ROC) plot and the Kappa coefficient. Results Cover of marshland, shoreline length and mean temperature in April–June were significantly (P < 0.01) related to the common marshland species richness. Cover and clumping of marshland and mean temperature and precipitation in April–June were selected in the model of red‐listed marshland species richness. The level of discrimination in our single species models varied in ROC from fair to excellent (AUC values 0.70–0.95). Cover of marshland was included in all GAM models built for the target species, but clumping of marshland, shoreline length and cover of mires also appeared as important predictors in single species models. Seven species had statistically significant relationships with climatic variables in the multivariate GAMs. Cover of marshland was highest in squares in which the red‐listed bittern Botaurus stellaris, marsh harrier Circus aeruginosus and great reed warbler Acrocephalus arundinaceus and the water rail Rallus aquaticus were observed. Main conclusions Cover of marshland was the only variable which was included in all the models, reinforcing the close connection between the studied species and marshlands. Broad‐scale clumping of marshlands was important for the red‐listed species, probably due to the much lower population sizes of red‐listed species than those of common species. Land‐cover data produced in CORINE seems to be well suited for modelling the distribution patterns of marshland birds. Although climatic variables also strongly affect the studied marshland birds, habitat availability plays a crucial role in their occurrence. The distribution patterns of marshland birds at the scale of 10 × 10 km reflect the interplay between habitat availability and direct climatic variables.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.