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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.

Recent actions to mitigate biodiversity loss in agricultural environments appear insufficient despite the considerable efforts channeled via the European Union’s Common Agricultural Policy. One likely reason for this failure is the limited attention paid to the regional and landscape level ecological characteristics in farmland conservation planning. We demonstrate how to obtain conservation prioritization solutions that would address simultaneously three goals, including two landscape level targets: minimizing local habitat quality loss, maximizing habitat connectivity, and incorporating landscape heterogeneity. As these goals may be contradictory, we investigate the potential trade-offs between them. We used the Zonation prioritization tool to examine how our three goals could be implemented in the agricultural landscapes of southwest Finland. We used measures of (i) biodiversity value of grasslands, (ii) connectivity between grasslands, and (iii) landscape heterogeneity which comprised of (land cover type based) compositional heterogeneity and (field margin based) configurational heterogeneity. Integration of landscape heterogeneity measures and habitat connectivity resulted in some tradeoffs with local habitat quality, the most prominent observation being that landscape heterogeneity co-varied with grassland connectivity. Among the two landscape heterogeneity parameters, inclusion of compositional heterogeneity resulted in more clustered prioritization solutions than configurational heterogeneity, which had a spatially more balanced impact. Concordance among landscape scale factors implies high potential for reconstruction of a functioning network of semi-natural grasslands in areas under intensive agricultural use. Broader scale multi-objective planning approaches can thus importantly support targeting biodiversity conservation planning and mediating the implementation of Common Agricultural Policy objectives. peerReviewed

Biodiversity crisis calls for better knowledge of the status of biodiversity, but also drivers and pressures behind the ecosystem change and the vulnerability of habitats. There is growing demand for the biodiversity data for decision-making on national, regional and global scales. However, the operationalized biodiversity and ecosystem monitoring systems which can integrate remote sensing, in situ and modelling data are still rare despite of that there are good co-operation initiatives such as Australian TERN and US NEON in place. In Finland, we have launched the Finnish Ecosystem Observatory (FEO) as a reaction to such a need to upgrade and modernize biodiversity and ecosystem monitoring schemes. We have started a development of national research and monitoring infrastructure/platform which can integrate different data types and provide timely data and knowledge for various decision-making purposes. In this short paper, we describe three case studies as examples of different data types and different monitoring needs: i) habitat mapping for large area, ii) hydrological condition of aapa mires, and iii) automated Aspen mapping for boreal forests. We will evaluate the possibilities to operationalize the production of such data sets in a long-term as well as the challenges and bottlenecks related either to data, processing or applicability. Finally, we will also discuss how FEO and similar kind of biodiversity monitoring platforms could be further developed, and how the network of such observatories could co-operate in the future.

The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27 024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a-1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilised for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits. The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27,024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a−1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilized for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.

Intensive management of boreal forests impairs forest biodiversity and species of old-growth forest. Effective measures to support biodiversity require detection of locations valuable for conservation. We applied species distribution models (SDMs) to a species of mature forest, the northern goshawk (Accipiter gentilis, goshawk), that is often associated with hotspots of forest biodiversity. We located optimal sites for the goshawk on a landscape scale, assessed their state under intensified logging operations and identified characteristics of goshawks' nesting sites in boreal forests. Optimal sites for the goshawk covered only 3.4% of the boreal landscape and were mostly located outside protected areas, which highlights the importance of conservation actions in privately-owned forests. Furthermore, optimal sites for the goshawk and associated biodiversity were under threat. Half of them were logged to some extent and 10% were already lost or notably deteriorated due to logging shortly after 2015 for which our models were calibrated. Habitat suitability for the goshawk increased with increasing volume of Norway spruce (Picea abies) peaking at 220 m(3) ha(-1), and with small quantities of birches (Betula spp.) and other broad-leaved trees. Threats to biodiversity of mature spruce forests are likely to accelerate in the future with increasing logging pressures and shorter rotation periods. Logging should be directed less to forests with high biodiversity. Continuous supply of mature spruce forests in the landscape should be secured with a denser network of protected areas and measures that aid in sparing large entities of mature forest on privately-owned land. peerReviewed

Significance Plant functional traits are central instruments in developing understanding and predicting biodiversity patterns and ecosystems processes. Snow is an important ecological factor in cold climates, but its contribution to the evolution of functionality of tundra vegetation is poorly known and insufficiently addressed in the research. We show here that snow has a fundamental effect in mediating climate change impacts on functional composition and diversity of Arctic tundra vegetation. As a whole, Arctic landscapes may lose spatial heterogeneity because plant communities will be functionally more alike, although the local functional diversity may increase. Our results highlight that future snow conditions and their fine-scale variability should be acknowledged in the next generation of Arctic vegetation−ecosystem models., The Arctic is one of the least human-impacted parts of the world, but, in turn, tundra biome is facing the most rapid climate change on Earth. These perturbations may cause major reshuffling of Arctic species compositions and functional trait profiles and diversity, thereby affecting ecosystem processes of the whole tundra region. Earlier research has detected important drivers of the change in plant functional traits under warming climate, but studies on one key factor, snow cover, are almost totally lacking. Here we integrate plot-scale vegetation data with detailed climate and snow information using machine learning methods to model the responsiveness of tundra communities to different scenarios of warming and snow cover duration. Our results show that decreasing snow cover, together with warming temperatures, can substantially modify biotic communities and their trait compositions, with future plant communities projected to be occupied by taller plants with larger leaves and faster resource acquisition strategies. As another finding, we show that, while the local functional diversity may increase, simultaneous biotic homogenization across tundra communities is likely to occur. The manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow conditions. Given this, realistic assessments of future ecosystem functioning require acknowledging the role of snow in tundra vegetation models.

The amount of High Nature Value farmland (HNVf) is a commonly used environmental indicator for assessing the performance of the Common Agricultural Policy, to support sustainable agriculture and monitor changes in agricultural land use in Europe. HNVf comprises agricultural areas of semi-natural state, low-intensity farming and fine-scale landscape mosaics of different habitat types. For a successful implementation, the identification of HNVf should correctly reflect the variation in biodiversity values between different agricultural landscapes. We examined how well the Finnish HNVf indicator and the sub-indicators constituting it – recalculated for the purposes of this study for five study regions – reflect the variation in bird and butterfly species richness and diversity patterns at different spatial scales. We found that butterfly diversity index was positively associated with the HNVf indicator at the finest scale of 0.5 km × 0.5 km squares. Among the HNVf sub-indicators, extensive cultivation of grasslands was most strongly related to the farmland bird diversity and the density of edge to the butterfly diversity. Thus, the HNVf concept reflects well the distribution of butterflies in the Finnish agricultural landscapes but insufficiently the diversity patterns of farmland birds. Importantly, semi-natural vegetation and long-term pastures – the backbone of the concept – presently occur in small and highly fragmented patches in agricultural landscapes in Finland. The Pan-European concept of HNVf has restricted application to farmland birds of this boreal country and the national HNVf concept may need to be revised.

Increased attention is being paid to the ecological drivers and conservation measures which could mitigate climate change-induced pressures for species survival, potentially helping populations to remain in their present-day locations longer. One important buffering mechanism against climate change may be provided by the heterogeneity in topography and consequent local climate conditions. However, the buffering capacity of this topoclimate has so far been insufficiently studied based on empirical survey data across multiple sites and species. Here, we studied whether the fine-grained air temperature variation of protected areas (PAs) affects the population changes of declining northern forest bird species. Importantly to our study, in PAs harmful land use, such as logging, is not allowed, enabling the detection of the effects of temperature buffering, even at relatively moderate levels of topographic variation. Our survey data from 129 PAs located in the boreal zone in Finland show that the density of northern forest species was higher in topographically heterogeneous PAs than in topographically more homogeneous PAs. Moreover, local temperature variation had a significant effect on the density change of northern forest birds from 1981&ndash, 1999 to 2000&ndash, 2017, indicating that change in bird density was generally smaller in PAs with higher topographic variation. Thus, we found a clear buffering effect stemming from the local temperature variation of PAs in the population trends of northern forest birds.

Snow has far-reaching effects on ecosystem processes and biodiversity in high-latitude ecosystems, but these have been poorly considered in climate change impact models1,2. Here, to forecast future trends in species occurrences and richness, we fitted species–environment models with temperature data from three climate scenarios and simulated up to a 40% decrease in snow cover duration (SCD)3. We used plot-scale data on 273 vascular plant, moss and lichen species in 1,200 study sites spanning a wide range of environmental conditions typical for mountainous Arctic landscapes (within 165 km2). According to the models, a rise in temperature increased overall species richness and caused only one species to lose all suitable habitat. In contrast, a shorter SCD tempered the effect of increasing temperature on species richness and led to accelerated rates of species’ local extinctions after a tipping point at 20–30% SCD decrease. All three species groups showed similar extinction rates but contrasting species richness responses. Our simulations indicate that future biodiversity patterns in Arctic regions are highly dependent on the evolution of snow conditions. Climate impact models that ignore the effects of snow cover change may provide biased biodiversity projections, with potentially erratic implications for Arctic nature conservation planning. Arctic biodiversity patterns will be highly dependent on the evolution of snow conditions, according to simulation results that integrate observations of vascular plants, mosses and lichens over a range of Arctic landscapes.

Agricultural intensification has caused drastic declines in the area and species richness of semi-natural grasslands across Europe. Novel habitats, such as power line clearings, provide alternative habitats and niches for grassland species, and might therefore mitigate these declines. However, it is not fully understood which environmental factors determine the occurrence of grassland species in the clearings. Identifying the most important drivers for grassland species occurrence would help understand the value of the clearings for grassland conservation and target enhanced management into clearings with most potential as grassland habitat. We studied the effects of local environmental conditions, and past and present connectivity to semi-natural grasslands, on the species richness of grassland plants and butterflies in 43 power line clearings in Finland. The results of generalized linear models and hierarchical partitioning showed that increasing time since clear-cut and amount of clearing residue decreased the species richness of both species groups, while the cover of mesic habitats increased it. However, the two species groups showed also divergent responses. Present-day local environmental conditions appeared to be the sole driver of grassland butterfly species richness, whereas the richness of grassland plants was related both to current conditions and historical connectivity to grasslands in 1870–1880s. This suggests the presence of an extinction debt in the studied grassland plant communities, emphasizing the need for enhanced management to increase suitable grassland habitat in the clearings. This would diminish the potential future losses of grassland plant species in the clearings and create valuable habitat for grassland butterflies as well.

All Nordic countries are committed to implement the Convention on Biological Diversity (CBD) and the CBD Strategic Plan for Biodiversity 2011-2020. According to the CBD Aichi Targets, areas of particular importance for biodiversity and ecosystem services should be protected through ecologically representative and well-connected systems of protected areas as well as other effective area-based conservation measures. The ecosystems of the northern parts of Norway, Sweden, Finland and North-West Russia are very diverse and unique even globally. This region is characterized by extensive boreal forests and vast areas of tundra, intermingled by various freshwater bodies. Climate change and loss of natural habitats due to intensive land-use and unsustainable use of natural resources are the most serious threats to biodiversity in this region, also called the Barents Region.

Refugia, the sites preserving conditions reminiscent of suitable climates, are projected to be crucial for species in a changing climate, particularly at high latitudes. However, the knowledge of current locations of high-latitude refugia and particularly their ability to retain suitable conditions under future climatic changes is limited. Occurrences of refugia have previously been mainly assessed and modelled based solely on climatic features, with insufficient attention being paid to potentially important landscape-scale factors. Here, climate-only models and full' models incorporating topo-edaphic landscape-scale variables (radiation, soil moisture and calcareousness) were developed and compared for 111 arctic-alpine plant species in Northern Fennoscandia. This was done for both current and future climates to determine cells with resilient climatic suitability harbouring refugia. Our results show that topographic and edaphic landscape-scale predictors both significantly improve models of arctic-alpine species distributions and alter projections of refugia occurrence. The predictions of species-climate models ignore landscape-scale ecological processes and may thus provide inaccurate estimates of extinction risk and forecasts of refugia where species can persist under a changing climate.

Determining the drivers, patterns and hotspots of biodiversity can be of critical importance in supporting regional conservation planning. However, as biodiversity hotspots can be described with several different metrics, it is important to investigate their congruence as well as the spatial overlap of hotspots with protected areas. Here, by using extensive data on climate, topography, soil characteristics and vascular plants combined with boosted regression tree models, we determine the patterns and key drivers of plant diversity hotspots along broad environmental gradients in northernmost Europe spanning from taiga landscapes to treeless tundra. We assess plant diversity with four metrics – species richness, range-rarity richness, threatened species richness, and local contribution to beta diversity – and examine their congruence with each other as well as with contemporary conservation areas. We found that climate plays an important role in governing species diversity, though topo-edaphic are highlighted alongside climatic predictors in determining the diversity patterns of many threatened, near-threatened, and range-restricted species. Importantly, the different diversity metrics have contrasting drivers and, overall, their hotspots have low congruence. Furthermore, existing protected areas appear to offer limited coverage for hotspots of vascular plant diversity. Modelling the various facets of diversity and their drivers, such as the topo-edaphic setting, may provide useful information to help conserve diversity in a changing climate. Projected patterns of different aspects of diversity and their congruency can provide insights into the processes underlying biodiversity and be employed to assess the representativeness of protected area networks.

European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity.

Climate change is a major threat to biodiversity, causing species to move to new climatically suitable areas, and thus increasing the extinction probability of species inhabiting fragmented landscapes. This highlights the need for climate-wise conservation strategies. With such strategies, a well-connected network of protected areas (PAs) is one of the most important means to support species survival. An extensive and representative PA network can enhance the resilience of regional populations of species, resulting in slower species loss in landscapes with a significant proportion of area of habitat being protected. This paper presents analyses of both the observed (1974–2010) and the predicted changes (by 2051–2080) in boreal bird populations in Finland. Firstly, the results show some general patterns of climate change on bird species: (1) species are shifting their ranges towards north, (2) range sizes of many species are declining, and (3) these changes are different in northern and southern species and in species occupying different habitats. Secondly, the paper looks more into the role of protected area (PA) network in securing birds in a changing climate and concludes that at least in Finland, open habitats, such as open mires and mountain heaths, change more rapidly in their species composition in protected areas than for example old-growth forests. However, generally, species decline less within than outside PAs showing that protected areas alleviate climate change effects on bird species of conservation concern. This finding, further supported by results from elsewhere in Europe, provides evidence for the resilience of PA networks in preserving species under climate change. Representative PA network that includes high cover for key habitats is hence needed in all latitudinal zones. The projected efficiency of the PA network in maintaining biodiversity was partly dependent on the strength of climate change varying with respect to future scenarios. This suggests that a flexibly adaptive climate-wise conservation planning is required to be better prepared for preserving biodiversity in the face of uncertain climate change. Thirdly, the paper discusses several aspects of climate change studies and avian biodiversity that have been hitherto understudied especially in the northern biomes. The paper suggests that future studies should concentrate on (1) abundance-based models and prioritisations, (2) species’ adaptive capacity (ability to avoid the impacts of climate change through dispersal and/or evolutionary change) and sensitivity (limited potential to persist in situ under changing climate) to climate change, (3) the role of the landscape matrix around the PAs and (4) the effects of the biogeophysical features of the PAs themselves. In conclusion, we envision that improved assessments regarding the ability of PA networks to maintain biodiversity in northern biomes are needed to enhance our ability to perform climate-wise conservation planning.

Aim Biotic interactions have a central role in defining species assemblages, realized both through negative and positive impacts. However, forecasts of how these interactions affect biodiversity across landscapes are challenging (and lacking) because the outcome of interactions depends not only on the identity of the interacting species but also on local environmental conditions. Thus, we study how biotic interactions manifest across a landscape. Location High-latitude northern Finland and Norway (69° N, 21° E). Methods We modelled the influence of a dominant shrub, Empetrum hermaphroditum, on spatial patterns of species richness of vascular plants, bryophytes and lichens (using cover of the shrub as a proxy for the frequency and intensity of interaction with other species) across a topographically variable landscape. Results The relationship between the cover of the dominant shrub and species richness differed between guilds, being strongest for vascular plant richness, and varied considerably along environmental gradients. The dominant shrub showed stronger negative impact on vascular plant richness under less abiotically extreme conditions. For lichens, the relationship was the opposite: under mild conditions, species richness increased with the cover of the shrub. Incorporating specific leaf area (SLA) data into analyses revealed that the dominant shrub affected species with high SLA (typically competitive species of resource-rich environments) most negatively. Main conclusions Our findings show how the impact of a dominant species varies across a landscape, with distinctly different effects on competitive and stress-tolerant vascular plants, and on bryophytes and lichens. Spatial predictions of biodiversity trends under global environmental change could, therefore, critically benefit from accounting for context-dependent impacts of biotic interactions.

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic–alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species vary among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic–alpine biodiversity patterns and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.

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.

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.

Agricultural intensification is a major driver of biodiversity decline throughout Europe. Agri-environment schemes governed by EU regulation are a significant tool in combating this decline but despite high spending, experiences of their effectiveness have been mixed. Their effectiveness might be improved by targeting them to locations with high biodiversity value, and particularly by spatial coordination to enhance habitat connectivity and the associated ecological processes, such as dispersal. We show, with an example of semi-natural grassland conservation in South-Western Finland, how spatial conservation planning tools, here the Zonation software, could help in assessing the habitat connectivity and allocating management actions. We assign highest priority to sites that have been classified as nationally important and that have been under management, and let connectivity influence the Zonation prioritizations. According to Zonation outputs, 25–30% of highest-ranking grasslands in our study area are without management contracts, indicating weak connectivity of managed sites, whereas Natura 2000 areas are spatially better located. A 50% expansion of the current network would be adequate to bring its value close to that of a network created from scratch, but as the contracts are temporary, reallocation of the contracts from the least valuable, funded sites to more valuable, unfunded ones would be even more effective. Current policy instruments supporting farmland biodiversity are strongly constrained by EU regulation, and appear to be too inflexible to take the spatial differences in conservation values into account. Better communication and incentives to encourage farmer participation to these voluntary programs are needed.

Species ranges are expected to move polewards following the changing climate, which poses novel challenges to the protected area network, particularly at northern latitudes. Here we study how well protected areas are likely to sustain populations of birds of conservation concern under a changing climate in northern Europe, in Finland. We fitted bioclimatic envelope models generated for 100 bird species to climate scenario data for the years 2051–2080 and three alternative emission scenarios in a 10-km grid system to predict changes in the species probability of occurrence. We related the projected changes in the climatic suitability to the amount of protected preferred habitat for the study species in the 10-km grid cells, and based on the cover of four main CORINE Land Cover classes in each conservation area in Finland. The probability of occurrence of all species (except marshland birds) decreased according to all scenarios, the decline being greatest in southern and smallest in northern boreal zones. This decline was slightly greater in unprotected than in protected areas for species of forests, mires and mountain habitats. The climatically suitable areas for the species were predicted to shift northwards, but the potential gain of southern species of conservation concern appears not to compensate for the loss of northern species. Thus, a representative protected area network is needed in all boreal zones. Overall, our results show that species-specific habitat preferences and habitat availability should be taken into account when assessing the efficiency of a protected area network in a changing climate.

Measures of geodiversity may provide a potentially useful surrogate for biodiversity patterns in insufficiently surveyed areas. However, their reliability in modelling the spatial variation in species richness is inadequately understood. We investigated whether the explanatory and predictive power of species richness models can be improved by considering explicit measures of geodiversity (variability of earth surface materials, forms and processes) in addition to climate and topography variables. Vascular plant species richness was modelled in two study areas in Northern Europe, Finland at the resolution of 500 or 1000 m, and as a function of three geodiversity (geological, geomorphological and hydrological diversity) variables, and six climate and topography variables. Variation partitioning was used to identify the independent and shared contributions of the geodiversity, climate and topography variable groups in explaining the spatial patterns of species richness. Generalized additive models were used to explore the ability of the different explanatory variables in predicting plant species richness within and between the study areas. In both the study areas, the inclusion of measures of geodiversity improved the explanatory power, predictive ability and robustness of the plant species richness models. In conclusion, the explicit measures of geodiversity appear to be promising surrogates of biodiversity, which reflect important abiotic resource factors, and may thus provide an equally, or even more reliable basis for transferring biodiversity models to new areas than models based on climate and topography variables.

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.

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.

Reliable identification of hotspot areas with high numbers of threatened plant species has a central role in conservation planning. We investigated the potentiality of identifying the distribution, richness and hotspots of threatened plant species at a 25 ha resolution using eight state-of-the-art modelling techniques (GLM, GAM, MARS, ANN, CTA, GBM, MDA and RF) in a taiga landscape in north-eastern Finland. First, the individual species models developed based on occurrence records of 28 species in the 1677 grid squares and derived from different statistical techniques were extrapolated to the whole study area of 41 750 km 2 . Second, the projected presence/absence maps were then combined to create species richness maps, and the top 5% of grid cells ranked by species richness were classified as hotspots. Finally, we created an overall summary map by combining the individual hotspot maps from all eight modelling techniques and identified areas where the individual hotspots maps overlapped most. There were distinguishing differences in projections of the geographic patterns of species richness and hotspots between the modelling techniques. Most of the modelling techniques predicted several hotspot locations sporadically around the study area. However, the overall summary map showed the highest predictive performance based on Kappa statistics, indicating that the locations where the hotspot maps from the eight models coincided most harboured highest observed species richness. Moreover, the summary map filtered out the patchy structures of individual hotspot maps. The results show that the choice of modelling technique may affect the accuracy and prediction of hotspot patterns. Such differences may hamper the development of useful biodiversity model applications for conservation planning, and thus it is beneficial if the conservation decision-making can be based on sets of alternative maps and overlaying of predictions from multiple models.

Vulnerability of 100 European butterfly species to climate change was assessed using 13 different criteria and data on species distributions, climate, land cover and topography from 1,608 grid squares 30′ × 60′ in size, and species characteristics increasing the susceptibility to climate change. Four bioclimatic model-based criteria were developed for each species by comparing the present-day distribution and climatic suitability of the occupied grid cells with projected distribution and suitability in the future using the HadCM3-A2 climate scenario for 2051–2080. The proportions of disadvantageous land cover types (bare areas, water, snow and ice, artificial surfaces) and cultivated and managed land in the occupied grid squares and their surroundings were measured to indicate the amount of unfavourable land cover and dispersal barriers for butterflies, and topographical heterogeneity to indicate the availability of potential climatic refugia. Vulnerability was also assessed based on species dispersal ability, geographical localization and habitat specialization. Northern European species appeared to be amongst the most vulnerable European butterflies. However, there is much species-to-species variation, and species appear to be threatened due to different combinations of critical characteristics. Inclusion of additional criteria, such as life-history species characteristics, topography and land cover to complement the bioclimatic model-based species vulnerability measures can significantly deepen the assessments of species susceptibility to climate change.

Measurements of primary productivity and its heterogeneity based on satellite images can provide useful estimates of species richness and distribution patterns. However, species richness at a given site may depend not only on local habitat quality and productivity but also on the characteristics of the surrounding landscape. In this study we investigated whether the predictions of species richness of plant families in northern boreal landscape in Finland can be improved by incorporating greenness information from the surrounding landscape, as derived from remotely sensed data (mean, maximum, standard deviation and range values of NDVI derived from Landsat ETM), into local greenness models. Using plant species richness data of 28 plant families from 440 grid cells of 25 ha in size, generalized additive models (GAMs) were fitted into three different sets of explanatory variables: (1) local greenness only, (2) landscape greenness only, and (3) combined local and landscape greenness. The derived richness–greenness relationships were mainly unimodal or positively increasing but varied between different plant families, and depended also on whether greenness was measured as mean or maximum greenness. Incorporation of landscape level greenness variables improved significantly both the explanatory power and cross-validation statistics of the models including only local greenness variables. Landscape greenness information derived from remote sensing data integrated with local information has thus the potentiality to improve predictive assessments of species richness over extensive and inaccessible areas, especially in high-latitude landscapes. Overall, the significant relationship between plants and surrounding landscape quality detected here suggests that landscape factors should be considered in preserving species richness of boreal environments, as well as in conservation planning for biodiversity in other environments.

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.

Using species and environmental data from an extensive grassland area in south-western Finland, we investigated the effect of patch area and connectivity, management and local habitat variables on the occurrence of spring-flowering vascular plants and their richness in boreal agricultural landscapes. Generalized linear models (GLM) and variation partitioning were used to study the explanatory power of the three groups of variables and their combined contributions on the richness and occurrence of six spring-flowering plant species. Generalized additive models (GAMs) and associated cross-validation tests were used to evaluate the predictability of the species occurrence and richness patterns. Present-day grassland patch area and connectivity were important predictors for occurrence and richness of the studied plant species. In addition, local habitat factors, especially radiation, accounted for major fractions of occurrence patterns of the studied species. Hybrid models including variables from all three variable groups had higher explanatory power and predictive capability than partial models. However, performance of the separate single-species models varied considerably between the six study species. Exclusion of radiation or connectivity from the hybrid models decreased their predictive performance, suggesting that these factors are of particular importance for grassland plant species at their northern range margins. When developing conservation and management planning for grassland plant species in Northern Europe, attention should be paid to well-connected networks of grassland patches including large, steeply-sloped patches with a favorable microclimate.

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.

This paper examines the potential impact of climate change on grassland butterfly species in Finland. It combines multiple climate change scenarios and different impact models for bioclimatic suitability to capture multi-faceted aspects of uncertainty. It also evaluates alternative options to enhance the adaptation of grassland biodiversity. Due to the long-term decline of semi-natural grasslands, their current extent in Finland is much lower than the minimum level estimated to ensure the survival of butterfly species. Projected locations of the climatically most suitable areas for butterfly species varied considerably between different modelling techniques and climate change scenarios. This uncertainty needs to be taken into account in planning adaptation responses. Analysis of potential adaptation options considered the promotion of existing measures based on the agri-environmental scheme (AES), as well as new measures, including species translocation and dispersal corridors. Current AES options were compared using a cost-effectiveness analysis (CEA). The CEA results indicated that buffer zones are the most cost-effective AES measure, although environmental fallows and buffer zones had broadly similar cost-effectiveness. The cost of translocation was relatively modest compared to that of dispersal corridors, due to the high number of habitat stepping stones required along potential dispersal corridors. A questionnaire survey of Finnish farmers revealed that a third of the respondents supported increases in nature conservation. Thus, large increases of the uptake of biodiversity-related AES measures among farmers may prove to be difficult. Given the small areas currently assigned for such measures, the prospects for the adaptation of grassland butterflies to climate change in Finland appear unfavourable.

The two main goals of this study are: (i) to examine the range shifts of a currently northwards expanding species, the map butterfly (Araschnia levana), in relation to annual variation in weather, and (ii) to test the capability of a bioclimatic envelope model, based on broad-scale European distribution data, to predict recent distributional changes (2000–2004) of the species in Finland. A significant relationship between annual maximum dispersal distance of the species and late summer temperature was detected. This suggests that the map butterfly has dispersed more actively in warmer rather than cooler summers, the most notable dispersal events being promoted by periods of exceptionally warm weather and southerly winds. The accuracy of the broad-scale bioclimatic model built for the species with European data using Generalized Additive Models (GAM) was good based on split-sample evaluation for a single period. However, the model’s performance was poor when applied to predict range shifts in Finland. Among the many potential explanations for the poor success of the transferred bioclimatic model, is the fact that bioclimatic envelope models do not generally account for species dispersal. This and other uncertainties support the view that bioclimatic models should be applied with caution when they are used to project future range shifts of species.

Using data from 46 sites in southern Finland and ordination methods, we examined plant-environment relationships in boreal mesic semi-natural grasslands at two spatial scales (grain sizes), using plots of 0.25 ha and 1 × 1 m. We applied the variation partitioning approach to determine the pure fractions of environmental variable groups and their joint effects on plant species compositional variation in the studied grasslands. The variables related to land-use intensity and high nutrient level (especially phosphorus) had a major role in explaining the species composition at both scales, although soil heterogeneity and habitat characteristics also accounted for a notable amount of the species compositional variation at the 0.25 ha grain size. At the 1 × 1 m grain size, the majority of the species compositional variation was related to the “pure” spatial differences between the studied grasslands (i.e. the site identity (dummy 0/1) variable), whereas the impacts of within-site variation of local environmental factors were considerably smaller. High nutrient levels and variables related to low land-use intensity, e.g. litter accumulation, were also significantly correlated with floristic variation at the 1 × 1 m grain size. Rare and declining grassland species are associated with low-nutrient grassland sites and patches. The main recommendation for the management planning of boreal semi-natural grasslands is that the first restoration attempts should be targeted to areas where nutrient levels, particularly that of phosphorus, are relatively low. Soil properties and plant species composition can provide useful guidelines for defining the correct management procedures for different sites.

Pulsatilla patens (L.) Mill. (Ranunculaceae) is a threatened plant which in Fennoscandia favours south-facing, warm slopes of pine-dominated esker forests. The cessation of cattle grazing, modern forestry practices, and especially efficient fire prevention have resulted in closure of undergrowth vegetation in these forests. Using generalized linear models (GLM), we studied the relationships between habitat factors (covers of field and ground layer and amount of litter) and the population structure of P. patens in 48 populations in Finland in order to identify favourable conditions for regeneration. The largest populations occurred at sites with intermediate values of both ground and field layer. The number of juvenile plants was also highest at intermediate values of ground layer. Dense moss layer and abundant litter had a negative effect on the flowering of P. patens. In conclusion, creation and maintenance of habitat heterogeneity to prevent the closure of undergrowth vegetation is of paramount importance for the successful reproduction of P. patens.

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.

Complementarity-based algorithms, that delimit sets of sites maximizing the representation of biodiversity, may be useful for targeting management resources to a group of prioritised grassland sites. A heuristic algorithm MSET was applied to select complementarity sets of semi-natural grassland sites in southern Finland. Site selection was carried out for three different data sets (grassland, indicator and rare species) of vascular plant species occurrences, and three target levels of species occurrences (1, 5 or 10 times). The overlap of the derived sets of sites varied between 46% and 88%, suggesting that algorithms can be sensitive to the delimitation of target species set. The results of algorithms based on species-occurrence data only should be critically evaluated with regard to the habitat quality and other species persistence issues. When applied with caution, complementarity-based methods, therefore offer a promising approach in prioritising grasslands.

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.

Complementarity of the nature reserve network in southernmost Finlandwas examined using a simple heuristic algorithm and occurrence data of 75characteristic herb-rich forest vascular plant species in 126 protected and 120non-protected sites. Three different minimum sets were selected to represent 1,5, or 10 occurrences of each species. In each minimum set there weresignificantly more protected than non-protected sites. Thus, although efficiencyis not maximal in the existing reserve network, the network does provide arepresentative basis for the conservation of herb-rich forest plants. However,some deficiencies were also noticed, particularly new reserves in floristicallydiverse herb-rich forests along watercourses would supplement the existingreserve network. On the other hand, the growing concern over the shortcomings ofreserve selection studies using presence/absence data is echoed here, becausemany of the protected forests not included in the minimum sets harbour importantnature conservation values. With regard to the five biological reserve selectioncriteria (e.g. occurrences of threatened species) considered here, theperformance of the existing reserve network is rather good. However, examinationof five reserve design criteria revealed some deficiencies, particularlyvulnerability of many reserves to potential edge effects.

The diversity of future landscapes might depend on our ability to predict their potential species richness. The predictability of patterns of vascular plant species richness in a Finnish agricultural river landscape was studied using generalized linear modeling, floristic records from fifty-three0.25-km grid squares in the “core” study area, and environmental variables derived from Landsat TM images and a digital elevation model. We built multiple regression models for the total number of plant species and the number of rarities, and validated the accuracy of the derived models with a test set of 52 grid squares. We tentatively extrapolated the models from the core study area to the whole study area of 601 km2 and produced species richness probability maps using GIS techniques. The results suggest that the local ‘hotspots’ of total flora (grid squares with > 200species) are mainly found in river valleys, where habitat diversity is high and a semi-open agricultural-forest mosaic occurs. The ‘hotspots’ of rare species (grid squares with > 4 rare species) are also found in river valleys, in sites where extensive semi-natural grasslands and herb-rich deciduous forests occur on steep slopes. We conclude that environmental variables derived from satellite images and topographic data can be used as approximate surrogates of plant species diversity in agricultural landscapes. Modeling of biological diversity based on satellite images and GIS can provide useful information needed in land use planning. However, due to the potential pitfalls in processing satellite imagery and model-building procedures, the results of predictive models should be carefully interpreted.

Global climate change is a major threat to biodiversity, posing increasing pressures on species to adapt in situ or shift their ranges. A protected area network is one of the main instruments to alleviate the negative impacts of climate change. Importantly, protected area networks might be expected to enhance the resilience of regional populations of species of conservation concern, resulting in slower species loss in landscapes with a significant amount of protected habitat compared to unprotected landscapes. Based on national bird atlases compiled in 1974–1989 and 2006–2010, this study examines the recent range shifts in 90 forest, mire, marshland, and Arctic mountain heath bird species of conservation concern in Finland, as well as the changes in their species richness in protected versus unprotected areas. The trends emerging from the atlas data comparisons were also related to the earlier study dealing with predictions of distributional changes for these species for the time slice of 2051–2080, developed using bioclimatic envelope models (BEMs). Our results suggest that the observed changes in bird distributions are in the same direction as the BEM-based predictions, resulting in a decrease in species richness of mire and Arctic mountain heath species and an increase in marshland species. The patterns of changes in species richness between the two time slices are in general parallel in protected and unprotected areas. However, importantly, protected areas maintained a higher level of species richness than unprotected areas. This finding provides support for the significance and resilience provision of protected area networks in preserving species of conservation concern under climate change.

This paper presents models based on empirical data which can be used to predict the patterns of species richness of vascular plants at the poorly explored mesoscale. Using generalized linear modelling, multiple regression models of species richness in the Kevo Nature Reserve, North Finland, are built with a training set of 257 grid squares and 33 environmental variables. We validated the accuracy of the derived models with an independent test set of 100 grid squares. Two different modelling approaches are used: one where species richness is treated straightforwardly as the response variable, and another where it is tentatively stratified into two groups according to taxon types, i.e. alpine taxa versus wide-spread and silvine (forest) taxa. However, the latter approach only marginally improved the accuracy of the predictions of total number of species. Linear altitudinal variables were among the best predictors of vascular plant richness at the mesoscale. As variables involving altitude are crude surrogates for energy-related factors, the results support the available energy hypothesis and advocate its significance in richness-environment relationships. Other important predictors of species richness included length of rivers and brooks, abundance of cliff walls, occurrences of steep-sided gorges and valleys, and relative abundance of gabbro in bedrock. However, the accuracy of the predictions in the derived models is relatively modest. This points towards the necessity of field work as a final guarantee to identify local hotspots of vascular plant species in a subarctic landscape.

National reserve networks are one of the most important means of species conservation, but their efficiency may be diminished due to the projected climatic changes. Using bioclimatic envelope models and spatial data on habitats and conservation areas, we studied how efficient the reserve network will be in preserving 100 forest, mire, marshland, and alpine bird species of conservation concern in Finland in 2051–2080 under three different climate scenarios. The occurrences of the studied bird species were related to the amount of habitat preferred by each species in the different boreal zones. We employed a novel integrated habitat suitability index that takes into account both the species’ probability of occurrence from the bioclimatic models and the availability of suitable habitat. Using this suitability index, the distribution of the topmost 5% suitability squares (“hotspots”) in the four bird species groups in the period 1971–2000 and under the three scenarios were compared with the location of reserves with the highest amounts of the four habitats to study the efficiency of the network. In species of mires, marshlands, and Arctic mountains, a high proportion of protected habitat was included in the 5% hotspots in the scenarios in 2051–2080, showing that protected areas cover a high proportion of occurrences of bird species. In contrast, in forests in the southern and middle boreal zones, only a small proportion of the protected habitat was included in the 5% hotspots, indicating that the efficiency of the protected area network will be insufficient for forest birds in the future. In the northern boreal zone, the efficiency of the reserve network in forests was highly dependent on the strength of climate change varying between the scenarios. Overall, there is no single solution to preserving biodiversity in a changing climate, but several future pathways should be considered.

This paper is an attempt, using statistical modelling techniques, to understand the patterns of vascular plant species richness at the poorly studied meso-scale within a relatively unexplored subarctic zone. Species richness is related to floristic-environmental composite variables, using occurrence data of vascular plants and environmental and spatial predictor variables in 362 1 km2 grid squares in the Kevo Nature Reserve. Species richness is modelled in two different way. First, by detecting the major floristic-environmental gradients with the ordination procedure of canonical correspondence analysis, and subsequently relating these ordination axes to species richness by generalized linear modelling. Second, species richness is directly related to the composite environmental factors of explanatory variables, using partial least squares regression. The most important explanatory variables, as suggested by both approaches, are relatively similar, and largely reflect the influence of altitude or altitudinally related variables in the models. The most prominent floristic gradient in the data runs from alpine habitats to river valleys, and this gradient is the main source of variation in species richness. Some local environmental variables are also relatively important predictors; the grid squares rich in vascular plant taxa are mainly located in the lowlands of the reserve and are characterised by rivers and brooks, as well as by abundant cliff walls. The two statistical models account for approximately the same amount of variation in the species richness, with more than half of the variation unexplained. Potential reasons for the relatively modest fit are discussed, and the results are compared to the characteristics of the diversity-environment relationships at both broader- and finer-scales.

Aim: Species distribution models built with geographically restricted data often fail to capture the full range of conditions experienced by species across their entire distribution area. Using such models to predict distribution shifts under future environmental change may, therefore, produce biased projections. However, restricted-scale models have the potential to include a larger sample of taxa for which distribution data are available and to provide finer-resolution projections that are better applied to conservation planning than the forecasts of broad-scale models. We examine the circumstances under which the projected shifts in species richness patterns derived from restricted-scale and broad-scale models are most likely to be similar. Location: Europe. Methods: The distribution of butterflies in Finland, Belgium/Netherlands and Spain was modelled based on restricted-scale (local) and broad-scale (continental) distribution and climate data. Both types of models were projected under future climate change scenarios to assess potential changes in species richness. Results: In Finland, species richness was projected to increase strongly based on restricted-scale models and to decrease slightly with broad-scale models. In Belgium/Netherlands, restricted-scale models projected a larger decrease in richness than broad-scale models. In Spain, both models projected a slight decrease in richness. We obtained similar projections based on restricted-scale and broad-scale models only in Spain because the climatic conditions available here covered the warm part of the distributions of butterflies better than in Finland and Belgium/Netherlands. Main conclusions: Restricted-scale models that fail to capture the warm part of species distributions produce biased estimates of future changes in species richness when projected under climatic conditions with no modern analogue in the study area. We recommend the use of distribution data beyond the boundaries of the study area to capture the part of the species response curves reflecting the climatic conditions that will prevail within that area in the future.

Local extinction of species can occur with a substantial delay following habitat loss or degradation. Accumulating evidence suggests that such extinction debts pose a significant but often unrecognized challenge for biodiversity conservation across a wide range of taxa and ecosystems. Species with long generation times and populations near their extinction threshold are most likely to have an extinction debt. However, as long as a species that is predicted to become extinct still persists, there is time for conservation measures such as habitat restoration and landscape management. Standardized long-term monitoring, more high-quality empirical studies on different taxa and ecosystems and further development of analytical methods will help to better quantify extinction debt and protect biodiversity.