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Abstract We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

Abstract Macroclimatic data are widely used to estimate the realized environmental niche of species and predict the current or the future spatial distribution of species. Because the realized niche is a subset of the fundamental niche—constrained by biotic interactions and dispersal limitations—proxies of the fundamental niche (e.g., thermal limits obtained from physiological experiments) are sometimes combined with macroclimatic data under the assumption that areas predicted as unsuitable from a realized niche perspective may belong to the species' fundamental niche. However, it is unclear whether this assumption is valid and whether thermal limits can be combined with macroclimatic data. Here, we explored these questions using available physiological thermal limits measured for 151 ectotherms. Specifically, we explored whether physiological thermal limits are larger than observed (realized) thermal limits measured using macroclimatic data, and what would be the effect of considering the physiological niche in addition to the realized niche for current and future predictions. Our results confirm previously raised concerns, as physiological limits can delimit a narrower range of thermal tolerance than the realized niche, particularly at the cold end of the thermal gradient where adaptive and/or facilitative mechanisms could allow species to survive in temperatures below physiological limits. These findings show that combining data on physiological thermal limits with macroclimatic data is dubious and that spatial predictions should be interpreted with caution because data on physiological thermal limits do not fit well with macroclimatic data that do not capture the conditions that organisms experience in the wild. While estimated physiological thermal limits are likely of value to complement species distribution studies, they are likely more useful in biophysical models that account for additional processes including the animal's behavior.

Changes in climate and land use represent significant risks of biodiversity loss globally, affect ecological stability, impact nature’s contributions to people (NCP, i.e. ecosystem services) and compromise human livelihood. As framings of conservation evolve to consider the interdependence between species and human needs, there is a growing recognition of the importance of NCP and biodiversity in conservation actions. However, knowledge on the interactions and spatial repartition of NCP and biodiversity remains limited. Here we show a comprehensive spatial assessment for 15 NCP and one biodiversity – distribution of threatened species – indicators in Switzerland. Indicators values were computed using a panel of mapping and modelling methods extracted from the literature, or specifically developed for this study. Through the analysis of their relationships, we reveal significant trade-offs and synergies in the spatial repartition of these indicators. Results from a spatial bundle analysis performed on the 16 indicators revealed the existence of four bundles showing a heterogeneous repartition over the Swiss landscape. Furthermore, we identified that topography (slope), climate (temperature and precipitations), and habitat (forest and meadows) were among the most influential factors to explain the spatial distribution of the four bundles. We conclude that various significant relationships exist between NCP and biodiversity indicators in Switzerland, emphasizing the importance of informed conservation approaches considering both NCP and biodiversity supply. This work helps fill the gap in our understanding of the links among different NCP, between NCP and biodiversity, and highlight their relationship to climate and land use, providing key insights for optimizing conservation efforts.

Abstract Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Abstract Since the late 1990s, Nature’s Contributions to People (NCPs; i.e. ecosystem services) were used as a putative leverage for fostering nature preservation. NCPs have largely been defined and mapped at the landscape level using land use and cover classifications. However, NCP mapping attempts based directly on individual species are still uncommon. Given that species shape ecosystems and ultimately deliver NCPs, mapping NCPs based on species distribution data should deliver highly meaningful results. This requires first establishing a census of the species-to-NCP relationships. However, datasets quantifying these relationships across several species and NCPs are rare. Here, we fill this gap by compiling literature and expert knowledge to establish the relationships of 1816 tracheophyte and 250 vertebrate species with 17 NCPs in the Swiss Alps. We illustrated the 31,098 identified species-NCP relationships for the two lineages and discuss why such a table is a key initial step in building spatial predictions of NCPs directly from species data, e.g. to ultimately complement spatial conservation planning.

The introduction of alien plant species can lead to biological invasions, which have major impacts on people and the environment. Trachycarpus fortunei (Hook.) H. Wendl. (Arecaceae) is an alien plant (palm tree) that has been introduced as an ornamental into urban areas across the world, but in many regions, it has started invading forests and other natural environments leading to negative impacts. To improve understanding and guide management, this study sought to assess people's knowledge and perceptions of T. fortunei in Ticino, the region in Switzerland where invasions of the species are most common. To achieve this goal, an online survey was conducted, and a total of 487 responses were received. The formal name(s) (scientific or common names) of T. fortunei were unknown to almost all participants (89 %), and people mostly just called them “palm”. Most respondents were familiar with the term invasive alien species (IAS) (88 %) and were aware of the invasiveness (spread) of T. fortunei (73 %). The study showed that although people like to see the palm in the region (51 %) and enjoyed the related sense of place it provides, respondents have become aware of the challenges associated with invasions and the majority (65 %) would like to see more done to control the spread of T. fortunei invasions within natural areas, particularly forests. To improve management, a large number of respondents (63 %) mentioned that education and awareness programmes should be implemented to provide people with knowledge on how to deal with invasive alien plants and thus prevent further spread. Almost a third of respondents supported (32 %) the regulated sale of T. fortunei in an effort to reduce invasions. Educational level, gender, and age affected response patterns, and this needs to be accounted for within strategic management planning, in particular, within education and awareness-raising initiatives. Tailored and targeted educational campaigns and management plans need to be established to prioritise and improve control of this invasive palm in Switzerland in the long term.

Abstract Biodiversity is declining globally at unprecedented rates. Ecological niche models (ENMs) are one of the most widely used toolsets to appraise global change impacts on biodiversity. Here, we identify a variety of advantages of incorporating remotely sensed ecosystem functioning attributes (EFAs) into ENMs. The development of ENMs that explicitly incorporate ecosystem functioning will allow a more holistic and integrative perspective of the habitat dynamics. The synergies between the increasingly available open‐access satellite images and cloud‐based platforms for planetary‐scale geospatial analysis offer an unprecedented opportunity to incorporate ecosystem processes and disturbances (such as fires, insect outbreaks or droughts) that have been so far largely neglected in ecological niche characterization and modelling. The most paradigmatic example of EFAs is the application of time series of spectral vegetation indices related to primary productivity and carbon cycle. EFAs related to surface energy balance and water cycles derived from remote sensing products such as land surface temperature or soil moisture enable a fine‐scale characterization of the species' niche—eventually improving the predictive performance of ENMs. All these advantages confirm that a new generation of ENMs based on such EFAs would offer great perspectives to increase our ability to monitor habitat suitability trends and population dynamics. However, despite the technical advances and increasing effort of remote sensing community to develop integrative EFAs, ENMs have yet to make full profit of the most recent developments by integrating them in ENMs. A coordinated agenda for remote sensing experts and ecological modellers will be essential over the coming years to bridge the gap between remote sensing and ecology disciplines and to take full (and timely) advantage of the fast‐growing body of Earth observation data and remote sensing technologies—with special emphasis on the development and testing of new variables related to key processes driving ecosystem functioning.

Coprophagous beetles are essential for fecal matter removal and are thus considered key ecosystem services providers. Yet, our knowledge of these beetles’ distribution and ecology remains very limited. Here, we used Species Distribution Models (SDM) to investigate the species-environment relationships (i.e. their niche) and predict the geographic distribution of coprophagous beetles in the Western Swiss Alps. We used our own sampled data and existing national data from the Swiss faunal database to calibrate, for each species, a regional and a national SDM respectively. In both models, the best predictors were temperature and rock cover proportion, while a soil characteristic (∂13C) indicating its organic content and texture was important in the regional models and precipitations in the Swiss models. The model performed better for species specialized on low or high altitudes than for generalist species occurring in a large altitudinal range. The model performances were neither influenced by the size, nor by the nesting behavior (laying eggs inside or below the excrements) of the species. We also showed that species richness decreased with altitude. This study opens new perspective for a better knowledge of coprophagous beetle’s ecology and a useful tool for their conservation in mountain regions.

Species distribution models (SDMs) are algorithms designed to infer the distribution of species using environmental and biotic variables and have become an important tool for ecologists and conservation biologists seeking to understand the implications of environmental change.Global datasets of environmental variables at resolutions of a few metres are increasingly available. SDMs fitted using such high‐resolution data allow researchers to investigate how local factors affect species occurrences at unprecedented fine spatial scales.As the spatial resolution of SDMs increases, we see a critical need to consider the characteristics of habitat types within or around raster pixels. In particular, we argue that the effects of habitat patches (EHPs, including habitat area, habitat configuration, and habitat diversity), measured focusing on patches or landscapes, have yet to be fully realized in SDMs.We provide guidelines to incorporate EHPs in SDMs. We explain why this development is important, describe approaches to properly conduct such analyses, and discuss pitfalls we foresee in testing EHPs.Synthesis. Ensuring that SDMs incorporating EHPs are properly designed will be key to increasing model predictive performance and to understanding which environmental factors influence the distribution of species at fine spatial scales. At a crucial time for nature conservation, we foresee that this will be a key step forward to understanding and protecting biodiversity. [ABSTRACT FROM AUTHOR]

The rapid loss of biodiversity in freshwater systems asks for a robust and spatially explicit understanding of species' occurrences. As two complementing approaches, habitat suitability models provide information about species' potential occurrence, while environmental DNA (eDNA) based assessments provide indication of species' actual occurrence. Individually, both approaches are used in ecological studies to characterize biodiversity, yet they are rarely combined. Here, we integrated high‐resolution habitat suitability models with eDNA‐based assessments of aquatic invertebrates in riverine networks to understand their individual and combined capacity to inform on species' occurrence. We used eDNA sampling data from 172 river sites and combined the detection of taxa from three insect orders (Ephemeroptera, Plecoptera, Trichoptera; hereafter EPT) with suitable habitat predictions at a subcatchment level (2 km2). Overall, we find congruence of habitat suitability and eDNA‐based detections. Yet, the models predicted suitable habitats beyond the number of detections by eDNA sampling, congruent with the suitable niche being larger than the realized niche. For local mismatches, where eDNA detected a species but the habitat was not predicted suitable, we calculated the minimal distance to upstream suitable habitat patches, indicating possible sources of eDNA signals from upstream sites subsequently being transported along the water flow. We estimated a median distance of 1.06 km (range 0.2–42 km) of DNA transport based on upstream habitat suitability, and this distance was significantly smaller than expected by null model predictions. This estimated transport distance is in the range of previously reported values and allows extrapolations of transport distances across many taxa and riverine systems. Together, the combination of eDNA and habitat suitability models allows larger scale and spatially integrative inferences about biodiversity, ultimately needed for the management and protection of biodiversity. [ABSTRACT FROM AUTHOR]

Predicting the presence or absence (occurrence-state) of species in a certain area is highly important for conservation. Occurrence-state can be assessed by network models that take suitable habitat patches as nodes, connected by potential dispersal of species. To determine connections, a connectivity threshold is set at the species' maximum dispersal distance. However, this requires field observations prone to underestimation, so for most animal species there are no trustable maximum dispersal distance estimations. This limits the development of accurate network models to predict species occurrence-state. In this study, we performed a sensitivity analysis of the performance of network models to different settings of maximum dispersal distance. Our approach, applied on six amphibian species in Switzerland, used habitat suitability modelling to define habitat patches, which were linked within a dispersal distance threshold to form habitat networks. We used network topological measures, patch suitability, and patch size to explain species occurrence-state in habitat patches through boosted regression trees. These modelling steps were repeated on each species for different maximum dispersal distances, including a species-specific value from literature. We evaluated mainly the predictive performance and predictor importance among the network models. We found that predictive performance had a positive relation with the distance threshold, and that almost none of the species-specific values from literature yielded the best performance across tested thresholds. With increasing dispersal distance, the importance of the habitat-quality-related variable decreased, whereas that of the topology-related predictors increased. We conclude that the sensitivity of these models to the dispersal distance parameter stems from the very different topologies formed with different movement assumptions. Most reported maximum dispersal distances are underestimated, presumably due to leptokurtic dispersal distribution. Our results imply that caution should be taken when selecting a dispersal distance threshold, considering higher values than those derived from field reports, to account for long-distance dispersers.

Epiphytes typically exhibit clustered distribution patterns, but predicting the spatial variation of their distribution at fine scales has long been a challenge. Taking advantage of a canopy crane giving access to 1.1 ha of lowland seasonal rainforest in Yunnan (China), we assess here which factors promote the probability that a given tree hosts epiphytes, and the variation of species richness and abundance of epiphytic spermatophytes and ferns among trees. Variation in epiphyte species richness as a function of host tree size, characteristics of its surrounding environment, topography and microclimatic conditions, were analyzed by Random Forest. Epiphytic spermatophytes and ferns occupied 2.3 and 10.8% of the available host trees, respectively. Significant models predicting which trees are more likely to host epiphytes than others were obtained, indicating that host tree characteristics and their local environment play a significant role in determining which host tree is most likely to be colonized. These models, as well as models for species richness and abundance, however, exhibited a moderate to low accuracy (r2 0.28 and 0.24 and of 0.12 and 0.14 for spermatophyte and fern richness and abundance, respectively). The best predictor of the presence of epiphytes on a tree, of its epiphytic species richness and abundance, was its DBH. In ferns, however, two peaks of species richness were observed, representing shade-loving ferns on small trees and sun-loving ferns on large trees. Microclimatic conditions and light intensity were the second best factor accounting for variation in species richness and abundance among trees. The contribution of liana infestation, host tree identity, and characteristics of neighboring trees were marginal. Our inclusion of a large number of host-tree characteristics and their local environment did not allow for an apparent improvement of model accuracy over studies with a more limited number of predictors, pointing to the role of chance upon tree colonization. Our results confirm the utmost importance of large trees with emergent canopies for the conservation of the epiphytic flora, but also indicate that epiphytic diversity assessments in tropical forests must also include small understorey trees, which should be further considered for conservation. The importance of the micro-climatic conditions that prevail at the level of each individual host tree further points to the necessity of maintaining a buffer zone around large host trees targeted for conservation.

Abstract To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two‐step approach to identify appropriate (re)introduction sites. The first step involves modeling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analyzing stationary ecological parameters, such as soil conditions, and relating them to population‐level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (Cypripedium calceolus L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid‐to‐sunny light conditions and specific soil physico‐chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide‐scale bioclimatic modeling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions.

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.