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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.

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 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.

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 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.

Abstract Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations.

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.

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate‐driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state‐of‐the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30‐year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070–2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate‐suitable areas for biodiversity. [ABSTRACT FROM AUTHOR]

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]