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Species distribution models analyse how species use different types of habitats. Their spatial predictions are often used to prioritize areas for conservation. Individuals may, however, prefer settling in habitat types of low quality compared to other available habitats. This ecological trap phenomenon is usually studied in a small number of habitat patches and consequences at the landscape level are largely unknown. It is therefore often unclear whether the spatial pattern of habitat use is aligned with the behavioural decisions made by the individuals during habitat selection or reflects actual variation in the quality of different habitat types. As species distribution models analyse the pattern of occurrence in different habitats, there is a conservation interest in examining what their predictions mean in terms of habitat quality when ecological traps are operating. Previous work in Belgium showed that red‐backed shrikes Lanius collurio are more attracted to newly available clear‐cut habitat in plantation forests than to the traditionally used farmland habitat. We developed models with shrike distribution data and compared their predictions with spatial variation in shrike reproductive performance used as a proxy for habitat quality. Models accurately predicted shrike distribution and identified the preferred clear‐cut patches as the most frequently used habitat, but reproductive performance was lower in clear‐cut areas than in farmland. With human‐induced rapid environmental changes, organisms may indeed be attracted to low‐quality habitats and occupy them at high densities. Consequently, the predictions of statistical models based on occurrence records may not align with variation in significant population parameters for the maintenance of the species. When species expand their range to novel habitats, such models are useful to document the spatial distribution of the organisms, but data on population growth rates are worth collecting before using model predictions to guide the spatial prioritization of conservation actions.

Measures of fitness such as reproductive performance are considered reliable indicators of habitat quality for a species. Such measures are, however, only available in a restricted number of sites, which prevents them from being used to quantify habitat quality across landscapes or regions. Alternatively, species presence records can be used along with environmental variables to build models that predict the distribution of species across larger spatial extents. Model predictions are often used for management purposes as they are assumed to describe the quality of the habitats to support a species. Yet, given that species are often present both in optimal and suboptimal areas, the use of data collected during the breeding season to build these models may potentially result in misleading predictions of habitat quality for the reproduction of the species, with potentially significant conservation consequences. In this study we analysed the relationship between fitness parameters informing on habitat quality for reproduction and predictions of species distribution models at multiple spatial scales using two independent sets of data. For 19 passerine bird species, we compared an indirect measure of reproductive performance (ratio of juveniles-to-adults) – obtained from Constant Effort Sites (CES) mist-netting data in Catalonia – with the predictions of models based on bird presence records collected during the Catalan Breeding Bird Atlas (CBBA). A positive relationship between the predictions derived from species distribution models and the reproductive performance of the species was found for almost half of the species at one or more spatial scales. This result suggests that species distribution models may help to predict habitat quality for some species over some extents. However, caution is needed as this is not consistent for all species at all scales. Further work based on species- and scale-specific approaches is now required to understand in which situations species distribution models provide predictions that are in line with reproductive performance.

Summary Up-to-date knowledge on species distribution is needed for efficient biodiversity conservation and management decision-making. Implementing efficient sampling strategies to identify previously unknown locations of species of conservation-concern is therefore a key challenge. Both structured expert judgement and habitat suitability models may help target sampling towards areas where chances to find the species are highest. However, practitioners often object to the use of models and believe they do not result in better decisions than the subjective opinion of experts, thus potentially constraining an optimal use of available methods and information. To illustrate the potential of habitat suitability models for guiding sampling strategies, we evaluated and compared the ability of experts and models to identify important areas for the conservation of a bird species (Lanius collurio) in Luxembourg. We conducted extensive fieldwork to find as many unknown bird territories as possible according to three independent sampling strategies: (i) a sampling strategy based on structured expert judgement, (ii) a sampling strategy based on the predictions of a habitat suitability model and (iii) a general-purpose stratified random sampling strategy used as a baseline reference. Both the expert-based and the model-based sampling strategies substantially outperformed the general-purpose sampling strategy in identifying new species records. In addition, the model-based sampling strategy performed significantly better than the expert-based sampling strategy. Synthesis and applications. This study explicitly shows that habitat suitability models can efficiently guide field data collection towards suitable areas for species of conservation-concern. Results may facilitate the involvement of practitioners in the development of habitat suitability models with the objective of maximizing the robustness of modelling applications in conservation practice and management decision-making.

Long-term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. Th e method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long-term monitoring projects. Th e large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units infl uences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the eff ect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a suffi ciently large number of sampling units that varies among species depending mainly on their prevalence. Th e integration of spatial modelling techniques into monitoring projects is a cost-eff ective approach as it off ers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.