Biology subjects

Zimmermann, N. E.

Publications and source records attributed to Zimmermann, N. E..

9 recordsLinked to original sources

bioRxiv2025

Spatial Biodiversity Indicators and a Composite Index for Conservation Prioritization in Switzerland

Spatially explicit indicators that quantify to which extent landscapes support biodiversity are essential for guiding evidence-based conservation planning. Here, we present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators-- Complementarity, Extinction Risk, and Ecological Connectivity--developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices. In total, 272 spatial layers were produced and made openly accessible. Relationships among the three indicators were analyzed to assess their distinct contributions, and expert-based validations were performed to evaluate their ecological plausibility and relevance for conservation applications. This dataset provides a robust foundation to support spatial planning and conservation decision-making in Switzerland and can be used as a blueprint for analogue integration in other countries.

ecology↗

bioRxiv2023

Rank-based deep learning from citizen-science data to model plant communities

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. We show that recasting multispecies distribution modeling as a ranking problem allows analyzing ubiquitous citizen-science observations with unprecedented efficiency. Based on 6.7M observations, we jointly modeled the distributions of 2477 plant species and species aggregates across Switzerland, using deep neural networks (DNNs). Compared to commonly-used approaches, multispecies DNNs predicted species distributions and especially community composition more accurately. Moreover, their setup allowed investigating understudied aspects of ecology: including seasonal variations of observation probability explicitly allowed approximating flowering phenology, especially for small, herbaceous species; reweighting predictions to mirror cover-abundance allowed mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allowed assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

ecology↗

bioRxiv2023

Mapping global marine biodiversity under sparse data conditions

Sparse and spatiotemporally highly uneven sampling efforts pose major challenges to obtaining accurate species and biodiversity distributions. Here, we demonstrate how limited surveys can be integrated with global models to uncover hotspots and distributions of marine biodiversity. We test the skill of recent and advanced species distribution model setups to predict the global biodiversity of >560 phytoplankton species from 183,000 samples. Recent setups attain quasi-null skill, while models optimized for sparse data explain up to 91% of directly observed species richness variations. Using a refined spatial cross-validation approach to address data sparsity at multiple temporal resolutions we find that background choices are the most critical step. Predictor variables selected from broad sets of drivers and tuned for each species individually improve the models ability in identifying richness hotspots and latitude gradients. Optimal setups identify tropical hotspots, while common ones lead to polar hotspots disjunct from general marine diversity. Our results show that unless great care is taken to validate models, conservation areas in the ocean may be misplaced. Yet a game-changing advance in mapping diversity can be achieved by addressing data-sparse conditions that prevail for >80% of extant marine species. Authorship statementAll authors designed the research and contributed to the writing. D.R. designed the multiscale validation and predictor selection methods, developed the figures with input by M.V. and N.E.Z., performed research, and wrote the first draft.

ecology↗

bioRxiv2022

Precipitation is the main axis of tropical phylogenetic turnover across space and time

Early natural historians - Compte de Buffon, von Humboldt and De Candolle - established ecology and geography as two principal axes determining the distribution of groups of organisms, laying the foundations for biogeography over the subsequent 200 years, yet the relative importance of these two axes remains unresolved. Leveraging phylogenomic and global species distribution data for Mimosoid legumes, an pantropical plant clade of 3,400 species, we show that the water availability gradient from deserts to rainforests dictates turnover of lineages within continents across the tropics. We demonstrate that 95% of speciation occurs within a precipitation niche, showing profound phylogenetic niche conservatism, and that lineage turnover boundaries coincide with isohyets of precipitation. We reveal similar patterns on different continents, implying that evolution and dispersal follow universal processes.

evolutionary biology↗

bioRxiv2022

Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain

O_LIPredictions from species distribution models (SDMs) that rely on presence-only data are strongly influenced by how pseudo-absences are derived. However, which strategies to generate pseudo-absences give rise to faithful SDMs in complex mountainous terrain, and whether species-specific or generic strategies perform better remain open questions. C_LIO_LIHere, across 500 plant species, we investigated comprehensively how predictions of SDMs at a 93 m spatial resolution are influenced by pseudo-absence strategies, using the complex topography of the Swiss mountains as a model system. We used five generic (random, equal-stratified, proportional-stratified, target, density) and three species-specific (target specific, density specific and geographic specific) approaches to derive pseudo-absence data. We conducted performance tests for each of our eight strategies in combination with (a) spatial bias, generated within our occurrence dataset on sites with highest sampling density, to investigate how this common bias problem influences the performance of pseudo-absence sampling strategies, and (b) a new approach to reduce model extrapolation in environmental space by including background data from all environmental conditions of the study area. SDMs were evaluated against an independent and well-sampled dataset of true presences and absences. C_LIO_LIThe random, the density (generic), and the geographic specific (species-specific) strategies consistently performed best, even in cases of strong spatial sampling bias in the occurrence data. Including a background of environmentally stratified pseudo-absences improved predictions of species distributions towards environmental extremes, and significantly reduced spatial extrapolations of model predictions in environmental space. C_LIO_LIOur results indicate that both generic and species-specific pseudo-absence strategies allow estimating robust SDMs and we provide clear recommendations which strategies to choose in complex terrain and when presence data are prone to high sampling bias. In datasets with strong sampling bias, most pseudo-absence strategies produce extrapolation problems and we additionally recommend environmentally stratified pseudo-absences in these cases. Overall, in species rich datasets the use of complex and computationally demanding, species-specific pseudo-absence strategies may not always be justified compared to simpler generic approaches. C_LI

ecology↗

bioRxiv2021

Blue and green food webs respond differently to elevation and land use

While aquatic (blue) and terrestrial (green) food webs are parts of the same landscape, it remains unclear whether they respond similarly to shared environmental gradients. We use empirical community data from hundreds of sites across Switzerland, and show that blue and green food webs have different structural and ecological properties along elevation as a temperature proxy, and among various land-use types. Specifically, in green food webs, their modular structure increases with elevation and the overlap of consumers diet niche decreases, while the opposite pattern is observed in blue food webs. Such differences between blue and green food webs are particularly pronounced in farmland-dominated habitats, indicating that anthropogenic habitat modification moderates the climatic effects on food webs but differently in blue versus green systems. These findings indicate general structural differences between blue and green food webs and suggest their potential divergent future alterations through land use or climatic changes.

ecology↗

bioRxiv2021

Global plant-frugivore trait matching is shaped by climate and biogeographic history

Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions in particular can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (Arecaceae), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, i.e., trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.

ecology↗

bioRxiv2021

Interannual climate variability data improves niche estimates in species distribution models

AimClimate is an essential element of species niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. LocationGlobal. Time period1979-2013 Major taxa studiesMammal, Amphibians, Reptiles MethodsWe use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). ResultsMean performance of SDMs with climatic means as predictors was TSS=0.71 and AUC=0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS=0.74, mean AUC=0.92), as does the inclusion of temporal variability (mean TSS=0.80, mean AUC=0.94). Including both spatial and temporal variability in SDMs shows similarly high TSS and AUC scores. Main conclusionsAccounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

ecology↗

bioRxiv2021

Mapping tree species for restoration potential resilient to climate change

The restoration of forest ecosystems is associated with key benefits for biodiversity and ecosystem services. Where possible, ecosystem restoration efforts should be guided by a detailed knowledge of the native flora to regenerate ecosystems in a way that benefits natural biodiversity, ecosystem services, and natures contribution to people. Machine learning can map the ecological suitability of tree species globally, which then can guide restoration efforts, especially in regions where knowledge about the native tree flora is still insufficient. We developed an algorithm that combines ecological niche modelling and geographic distributions that allows for the high resolution (1km) global mapping of the native range and suitability of 3,987 tree species under current and future climatic conditions. We show that in most regions where forest cover could be potentially increased, heterogeneity in ecological conditions and narrow species niche width limit species occupancy, so that in several areas with reforestation potential, a large amount of potentially suitable species would be required for successful reforestation. Local tree planting efforts should consider a wide variety of species to ensure that the equally large variety of ecological conditions can be covered. Under climate change, a large fraction of the surface for restoration will suffer significant turnover in suitability, so that areas that are suitable for many species under current conditions will not be suitable in the future anymore. Such a turnover due to shifting climate is less pronounced in regions containing species with broader geographical distributions. This indicates that if restoration decisions are solely based on current climatic conditions, a large fraction of the restored area will become unsuitable in the future. Decisions on forest restoration should therefore take the niche width of a tree species into account to mitigate the risk of climate-driven ecosystem degradation.

ecology↗