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Publisher Copyright: © 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America. The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modelling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, i.e., 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting habitat suitability modelling, i.e., the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas was considerable, but our analysis also revealed many potentially high-quality forest stands outside protected areas. However, many of these sites are increasingly threatened by logging due to increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and corresponding approach would also be feasible and recommendable elsewhere where similar data are available. The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modeling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, that is, 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting-habitat suitability modeling, that is, the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas (PAs) was considerable, but our analysis also revealed many potentially high-quality forest stands outside PAs. However, many of these sites are increasingly threatened by logging because of increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and a corresponding approach would also be feasible and recommendable elsewhere where similar data are available.

Aim Nitrogen deposition is a major global driver of change in plant communities, but its impacts on higher trophic levels are insufficiently understood. Here, we introduce and test a novel conceptual trait-based model describing how the effects of soil eutrophication cascade to higher trophic levels across differential plant–herbivore interactions. Location Northern Europe. Methods We synthesize previous literature on the effects of nitrogen on plants and herbivorous insects as well as relevant multispecies patterns of insect communities concerning species dietary breadth, body size, dispersal propensity and voltinism in order to derive the model. We empirically evaluate the proposed, hitherto untested, four main model pathways using statistical modelling and data on 1064 northern European butterfly and moth species, their life-history traits, phylogeny and population trends. Results We show that across all species: (1) larval dietary breadth and host plant foliar nitrogen content are positively and equally strongly related to insect body size, and that (2) multivoltinism, host plant preferences for soil nitrogen, body size and larval dietary breadth are positively related to population trends of butterflies and moths as predicted by the model. Positive relationships between plant foliar nitrogen content and body size as well as multivoltinism and population trends are the first multispecies demonstrations for these patterns. Main conclusions Soil nitrogen enrichment amplifies the diverging trends of herbivorous insects feeding on nitrophilous versus nitrophobous plants through differential plant–herbivore interactions, causing predictable changes in community composition at higher trophic levels. A positive foliar nitrogen–insect body size relationship, now empirically supported, is the integrating link within this cascade. As nitrogen deposition is a global driver, our model suggests that a major future trend may be an increased dominance of insects that are large, dispersive, multivoltine, dietary generalists or specialized on nitrophilous plant species at the expense of species preferring oligotrophic environments.

Summary Rapid environmental changes are threatening biodiversity and exposing species to novel ecological and evolutionary pressures. The scientific community increasingly recognizes the need for dynamic models integrating sufficient complexity both to improve our understanding of species' responses to environmental changes and to inform effective management strategies. Using three illustrative examples, we introduce a novel modelling platform, RangeShifter, which integrates complex population dynamics and dispersal behaviour, includes plastic and evolutionary processes and simulates scenarios on spatially explicit landscapes. The software provides functionality for a wide variety of modelling applications ranging from applied questions, where it can be parameterized for real landscapes and species to compare alternative potential management interventions, to purely theoretical studies of species' eco-evolutionary dynamics and responses to different environmental pressures. RangeShifter provides an important tool for facilitating the advancement of ecological theory on species' spatial dynamics in response to environmental changes, and linking it directly to application in biodiversity conservation.

Aim To quantify whether species distribution models (SDMs) can reliably forecast species distributions under observed climate change. In particular, to test whether the predictive ability of SDMs depends on species traits or the inclusion of land cover and soil type, and whether distributional changes at expanding range margins can be predicted accurately. Location Finland Methods Using 10-km resolution butterfly atlas data from two periods, 1992–99 (t1) and 2002–09 (t2), with a significant between-period temperature increase, we modelled the effects of climatic warming on butterfly distributions with boosted regression trees (BRTs) and generalized additive models (GAMs). We evaluated model performance by using the split-sample approach with data from t1 (‘nonindependent validation’), and then compared model projections based on data from t1 with species’ observed distributions in t2 (‘independent validation’). We compared climate-only SDMs to SDMs including land cover, soil type, or both. Finally, we related model performance to species traits and compared observed and predicted distributional shifts at northern range margins.

Summary 1. Dynamic simulation models are a promising tool for assessing how species respond to habitat fragmentation and climate change. However, sensitivity of their outputs to impacts of spatial resolution is insufficiently known. 2. Using an individual-based dynamic model for species’ range expansion, we demonstrate an inherent risk of substantial biases resulting from choices relating to the resolution at which key patterns and processes are modelled. 3. Increasing cell size leads to overestimating dispersal distances, the extent of the range shift and population size. Overestimation accelerates with cell size for species with short dispersal capacity and is particularly severe in highly fragmented landscapes. 4. The overestimation results from three main interacting sources: homogenisation of spatial information, alteration of dispersal kernels and stabilisation/aggregation of population dynamics. 5. We urge for caution in selecting the spatial resolution used in dynamic simulations and other predictive models and highlight the urgent need to develop upscaling methods that maintain important patterns and processes at fine scales.

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well-sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine-learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression-based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over-prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.

Aim Habitat fragmentation is a major driver of biodiversity loss but it is insufficiently known how much its effects vary among species with different life-history traits; especially in plant communities, the understanding of the role of traits related to species persistence and dispersal in determining dynamics of species communities in fragmented landscapes is still limited. The primary aim of this study was to test how plant traits related to persistence and dispersal and their interactions modify plant species vulnerability to decreasing habitat area and increasing isolation. Location Five regions distributed over four countries in Central and Northern Europe. Methods Our dataset was composed of primary data from studies on the distribution of plant communities in 300 grassland fragments in five regions. The regional datasets were consolidated by standardizing nomenclature and species life-history traits and by recalculating standardized landscape measures from the original geographical data. We assessed the responses of plant species richness to habitat area, connectivity, plant life-history traits and their interactions using linear mixed models. Results We found that the negative effect of habitat loss on plant species richness was pervasive across different regions, whereas the effect of habitat isolation on species richness was not evident. This area effect was, however, not equal for all the species, and life-history traits related to both species persistence and dispersal modified plant sensitivity to habitat loss, indicating that both landscape and local processes determined large-scale dynamics of plant communities. High competitive ability for light, annual life cycle and animal dispersal emerged as traits enabling species to cope with habitat loss. Main conclusions In highly fragmented rural landscapes in NW Europe, mitigating the spatial isolation of remaining grasslands should be accompanied by restoration measures aimed at improving habitat quality for low competitors, abiotically dispersed and perennial, clonal species.

Aim We investigate the importance of interacting species for current and potential future species distributions, the influence of their ecological characteristics on projected range shifts when considering or ignoring interacting species, and the consistency of observed relationships across different global change scenarios.

Aim An increase in multivoltinism in ectothermic animals has been proposed by several authors as a possible outcome of climate warming, especially in high latitudes. We tested this prediction with large-scale empirical monitoring data for boreal moth communities. Location Finland, northern Europe. Methods Our data set comprised observations of multivoltine species made in the Finnish moth monitoring scheme ‘Nocturna’ trap sites during the period 1993– 2006 along an exceptionally long latitudinal gradient of 1000 km. To compare recent changes in moth multivoltinism with a longer time period, we gathered older time series of moth observations from five locations. We used generalized linear mixed models (GLMMs) to detect possible temporal and geographical trends in the annual occurrence of multivoltinism. We also identified areas where the recent changes in multivoltinism have been greatest. Monthly average temperatures of spring and summer periods and annual sum of growing degree days above 5 °C (GDD5) were used as explanatory variables to distinguish the main climatic correlates of moth multivoltinism. Results We observed a clear increase in the occurrence of moth multivoltinism during the period 1993–2006. The incidence as well as the recent increase in multivoltinism were highest in southernmost Finland and decreased towards the north. We also detected a weaker, although significantly positive, trend of moth multivoltinism in southern Finland during the period 1963–92, suggesting that this increasing trend might already have begun earlier. The most important climatic correlates for the annual occurrence of moth multivoltinism were the mean summer temperature (periods May–July and June–August) and GDD5, but all the tested climatic variables showed a significant univariate relationship with the occurrence of moth multivoltinism. All climatic variables showed an increasing trend during the period 1993–2006. Main conclusions The occurrence of multivoltinism has increased in northern European moth communities during recent decades, apparently as a response to increasing temperatures during the spring and summer seasons. The increase in multivoltinism was greatest in the southernmost parts of Finland, whereas in the northern landscapes recent warming has triggered multivoltinism in only relatively few moth species.

Intensification or abandonment of agricultural land use has led to a severe decline of semi-natural habitats across Europe. This can cause immediate loss of species but also time-delayed extinctions, known as the extinction debt. In a pan-European study of 147 fragmented grassland remnants, we found differences in the extinction debt of species from different trophic levels. Present-day species richness of long-lived vascular plant specialists was better explained by past than current landscape patterns, indicating an extinction debt. In contrast, short-lived butterfly specialists showed no evidence for an extinction debt at a time scale of c. 40 years. Our results indicate that management strategies maintaining the status quo of fragmented habitats are insufficient, as time-delayed extinctions and associated co-extinctions will lead to further biodiversity loss in the future.

Aim To analyse the effect of the inclusion of soil and land-cover data on the performance of bioclimatic envelope models for the regional-scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991-2006 in Belgium. For each group separately, the well-surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land-cover, (3) climate and soil, and (4) climate, land-cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike's information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land-cover classes or predominant soil types led to similar improvements in the performance relative to the climate-only models for both taxonomic groups. In addition, the joint inclusion of land-cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land-cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land-cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional-scale species-climate impact models, particularly when predicting future range shifts of species under climate change.

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.

Aim Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species. Location North-eastern Finland, Europe. Methods The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. Results The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Main conclusions Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.

Aim Understanding the spatial patterns of species distribution and predicting the occurrence of high biological diversity and rare species are central themes in biogeography and environmental conservation. The aim of this study was to model and scrutinize the relative contributions of climate, topography, geology and land-cover factors to the distributions of threatened vascular plant species in taiga landscapes in northern Finland. Location North-east Finland, northern Europe. Methods The study was performed using a data set of 28 plant species and environmental variables at a 25-ha resolution. Four different stepwise selection algorithms [Akaike information criterion (AIC), Bayesian information criterion (BIC), adaptive backfitting, cross selection] with generalized additive models (GAMs) were fitted to identify the main environmental correlates for species occurrences. The accuracies of the distribution models were evaluated using fourfold cross-validation based on the area under the curve (AUC) derived from receiver operating characteristic plots. The GAMs were tentatively extrapolated to the whole study area and species occurrence probability maps were produced using GIS techniques. The effect of spatial autocorrelation on the modelling results was also tested by including autocovariate terms in the GAMs. Results According to the AUC values, the model performance varied from fair to excellent. The AIC algorithm provided the highest mean performance (mean AUC = 0.889), whereas the lowest mean AUC (0.851) was obtained from BIC. Most of the variation in the distribution of threatened plant species was related to growing degree days, temperature of the coldest month, water balance, cover of mire and mean elevation. In general, climate was the most powerful explanatory variable group, followed by land cover, topography and geology. Inclusion of the autocovariate only slightly improved the performance of the models and had a minor effect on the importance of the environmental variables. Main conclusions The results confirm that the landscape-scale distribution patterns of plant species can be modelled well on the basis of environmental parameters. A spatial grid system with several environmental variables derived from remote sensing and GIS data was found to produce useful data sets, which can be employed when predicting species distribution patterns over extensive areas. Landscape-scale maps showing the predicted occurrences of individual or multiple threatened plant species may provide a useful basis for focusing field surveys and allocating conservation efforts.

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.

We investigated whether the inclusion of topographical heterogeneity in bioclimatic envelope models would significantly alter predictions of climate change - induced broad-scale butterfly species range size changes in Europe. Using generalized additive models, and data on current climate and species distributions and two different climate scenarios (HadCM3A2 and HadCM3B2) for the period 2051-2080, we developed predictions of the currently suitable area and potential range size changes of 100 European butterfly species. The inclusion of elevation range increased the predictive accuracy of climate-only models for 86 of the 100 species. The differences in projected future distributions were most notable in mountainous areas, where the climate-topography models projected only ca. half of the species losses than the climate-only models. By contrast, climate-topography models estimated double the losses of species than climate-only models in the flatlands regions. Our findings suggest that disregarding topographical heterogeneity may cause a significant source of error in broad-scale bioclimatic modelling. Mountainous regions are likely to be even more important for future conservation of species than had until now been predicted, based on bioclimatic envelope models that did not take an explicit account of elevational range of grid squares.

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 fourowl 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. © 2007 The AuthorsGlobal Ecology and Biogeography, Journal compilation © 2007 Blackwell Publishing Ltd.

Aim We explored the relative contributions of climatic and land-cover factors in explaining the distribution patterns of butterflies in a boreal region. Location Finland, northern Europe. Methods Data from a national butterfly atlas survey carried out during 1991–2003, with a 10-km grain grid system, were used in these analyses. We used generalized additive models (GAM) and hierarchical partitioning (HP) to explore the main environmental correlates (climate and land-cover) of the realized niches of 98 butterfly species. The accuracy of the distribution models (GAMs) was validated by resubstitution and cross-validation approaches, using the area under the curve (AUC) derived from the receiver operating characteristic (ROC) plots. Results Predictive accuracies of the 98 individual environment–butterfly models varied from low to very high (cross-validated AUC values 0.48–0.99), with a mean of 0.79. The results of both the GAM and HP analyses were broadly concordant. Most of the variation in butterfly distributions is associated with growing degree-days, mean temperature of the coldest month and cover of built-up area in all six phylogenetic groups (butterfly families). There were no statistically significant differences in predictive accuracy among the different butterfly families. Main conclusions About three-quarters of the distributions of butterfly species in Finland appear to be governed principally by climatic, predominantly temperature-related, factors. This indicates that many butterfly species may respond rapidly to the projected climate change in boreal regions. By determining the ecological niches of multiple species, we can project their range shifts in response to changes in climate and land-cover, and identify species that are particularly sensitive to forecasted global changes.

We examined the influence of ‘seasonal fine-tuning’ of climatic variables on the performance of bioclimatic envelope models of migrating birds. Using climate data and national bird atlas data from a 10 × 10 km uniform grid system in Finland, we tested whether the replacement of one ‘baseline’ set of variables including summer (June–August) temperature and precipitation variables with climate variables tailored (‘fine-tuned’) for each species individually improved the bird-climate models. The fine-tuning was conducted on the basis of time of arrival and early breeding of the species. Two generalized additive models (GAMs) were constructed for each of the 63 bird species studied, employing (1) the baseline climate variables and (2) the fine-tuned climate variables. Model performance was measured as explanatory power (deviance change) and predictive power (area under the curve; AUC) statistics derived from cross-validation. Fine-tuned climate variables provided, in many cases, statistically significantly improved model performance compared to using the same baseline set of variables for all the species. Model improvements mainly concerned bird species arriving and starting their breeding in May–June. We conclude that the use of the fine-tuned climate variables tailored for each species individually on the basis of their arrival and critical breeding periods can provide important benefits for bioclimatic modelling.

The limited availability of resources for conservation has led to the development of many quantitative methods for selecting reserves that aim to maximize the biodiversity value of reserve networks. In published analyses, species are often considered equal, although some are in much greater need of protection than others. Furthermore, representation is usually treated as a threshold: a species is either represented or not, but varying levels of representation over or under a given target level are not valued differently. We propose that a higher representation level should also have higher value. We introduce a framework for reserve selection that includes species weights and benefit functions for under- and overrepresentation (number of locations for each species). We applied the method to conservation planning for herb-rich forests in southern Finland. Our use of benefit functions and weighting changed the identity of about 50% of the selected sites at different funding levels and improved the representation of rare and threatened species. We also identified a small area of additional land that would substantially enhance the existing reserve network. We suggest that benefit functions and species weighting should be considered as standard options in reserve-selection applications. Resumen: La limitada disponibilidad de recursos para la conservacion ha llevado al desarrollo de muchos metodos cualitativos para la seleccion de reservas que tienen como meta maximizar el valor de la biodiversidad de las redes de reservas. En los analisis publicados, las especies son consideradas iguales a menudo, a pesar del hecho de que algunas tienen mayor necesidad de proteccion que otras. Mas aun, la representacion generalmente es tratada como umbral: una especie es representada o no, pero los niveles de representacion por arriba o por debajo de un nivel determinado no son valorados diferentemente. Proponemos que un mayor nivel de representacion tambien deberia tener un mayor valor. Introducimos un marco de referencia para la seleccion de reservas que incluye la ponderacion de especies y funciones de beneficio para la falta de- y la sobre-representacion (numero de localidades para cada especie). Aplicamos el metodo a la planificacion de la conservacion de bosques ricos en hierbas del sur de Finlandia. Nuestro uso de las funciones de beneficio y ponderaciones cambio la identidad de alrededor de 50% de los sitios seleccionados en diferentes niveles de financiamiento y mejoro la representacion de especies raras y amenazadas. Tambien identificamos una pequena area de terreno adicional que daria realce sustancial a la actual red de reservas. Sugerimos que se considere a las funciones de beneficio y a la ponderacion de especies como opciones estandar en la seleccion de reservas.

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

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.

One of the main goals in nature conservation and land use planning is to identify areas important for biodiversity. One possible cost-effective surrogate for deriving appropriate estimates of spatial patterns of species richness is provided by predictive modeling based on remote sensing and topographic data. Using bird species richness data from a spatial grid system (105 squares of 0.25 km2 within an area of 26.25 km2), we tested the usefulness of Landsat TM satellite-based remote sensing and topographic data in bird species richness modeling in a boreal agricultural-forest mosaic in southwestern Finland. We built generalized linear models for the bird species richness and validated the accuracy of the models with an independent test area of 50 grid squares (12.5 km2). We evaluated separately the modeling performance of habitat structure, habitat composition, topographical-moisture variables and all variables in the model-building and model-test areas. Areas of high observed and predicted bird species ri...

Summary 1 Numerical studies of the relationship between birds and their habitat are important because they provide understanding of the impacts of natural and human factors on avian diversity. However, collinearity between explanatory variables and spatial autocorrelation can hamper the detection of key environmental factors underlying bird–environment relationships identified by traditional regression approaches. This study utilized two alternative statistical methods to address these difficulties in biodiversity modelling. 2 We examined bird abundance patterns, spatial structure and relationship to environmental factors in an agricultural–forest mosaic landscape in Finland. We used data from 105 grid squares each 25 ha in size. Using variation partitioning and hierarchical partitioning methods, we determined the independent and joint effects of habitat cover, landscape structure and spatial variables on the total number of bird pairs and that of agricultural bird pairs. 3 The explanatory variables highlighted as important predictors of bird patterns by the two methods generally coincided well. The total number of bird pairs was negatively related to agricultural land, and positively to cover of forests and landscape heterogeneity. However, a clear majority of the explained variation in bird patterns was related to the joint effect of predictors, and the independent contributions of predictors were small. The univariate importance of landscape heterogeneity decreased greatly if the habitat cover variables were considered simultaneously. 4 Most of the explained variation in the number of agricultural bird pairs was related to the joint effects of the explanatory variables. In addition, the independent effect of habitat cover variables was considerable and agricultural birds showed a positive relationship with semi-natural grasslands. 5 Synthesis and applications. Variation partitioning and hierarchical partitioning approaches provide deeper insights into bird–environment relationships than traditional regression methods. This is particularly so when they are employed in a complementary manner. Our results indicate that a major part of the spatial structure in bird patterns in agricultural–forest mosaics can be caused by the clumping of habitats either preferred or avoided by birds. Moreover, at a scale of 25 ha, the abundance of bird pairs is not necessarily related to landscape heterogeneity as such, but depends more on the distribution of the most important habitats for birds.

Different numerical techniques were used to detect and describe the major ecological-biogeographical patterns of vascular plant distributions at the meso-scale level in a subarctic region in Finland. The distribution patterns of 231 native taxa in 362 1 km2 grid squares of the Kevo Nature Reserve were analysed by two-way indicator species analysis and detrended correspondence analysis, and were subsequently related to twenty-eight geographical, topographical, geological, and vegetational variables using simple discriminant functions and canonical correspondence analysis with associated Monte Carlo permutation tests. The floristic variation detected reflects variations in environmental factors operative at the regional and local scales. No major broad-scale coherent geographical patterns were detected; instead, the spatial distribution of the grids with a similar floristic composition shows a scattered distribution. All the numerical techniques reveal a major gradient from alpine areas to lowland sites with rivers and rocky outcrops, and the most important explanatory variables for predicting the main floristic variation are all associated with altitude. The floristic patterns represented by the second ordination gradient mainly correlate with the abundance of mires. Partial ordinations indicate that both the geographical and geological variables explain relatively little of the species distributional patterns. Although the meso-scale approach reveals much about the plant-environment relationships in the study area, the floristic variation appears to be determined mainly by fine-scale factors. In the most heterogeneous grids, the grid size used fails to detect accurately the ecological patterns of the species present.

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.