Biology subjects

Karger, D. N.

Publications and source records attributed to Karger, D. N..

7 recordsLinked to original sources

bioRxiv2024

The accelerating exposure of European protected areas to climate change

1.All ecosystems are affected by climate change, but differences in the pace of change will render some areas more exposed than others. Such spatial patterns of risk are important when assessing the continued functionality of protected area (PA) networks or planning for their expansion. Europe is undertaking an expansion of the PA network to cover 30% of its land and sea surface, but this must account for climate risk. Here, we estimate four metrics of future climate risk across Europe - local velocity, distance velocity, magnitude, and residence time - and assess the level of climate exposure of European PAs vs non-protected control sites. We also evaluate the intensity of climate risks on >1,000 European species of conservation concern, associated with Natura2000 sites. Our results show large spatial differences in climate change exposure across Europe, with faster pace and farther shifts in climate in the Boreal, Steppic, and Pannonian regions but slower changes in the Mediterranean, Alpine, Artic, and Macronesian regions. Climate change magnitude was higher for the Alpine, Mediterranean, and Steppic regions, implying large local differences between present and future climate. These spatial risk patterns were largely consistent across scenarios, but with up to three times higher risk under the most pessimistic vs the most optimistic scenario. Large variation in climate exposure for species of conservation concern was revealed, including 11 species which are highly dependent on Natura2000 sites and predicted to experience rapid climate change. Our results provide guidance for managing European PAs, and expanding their coverage, by pinpointing areas offering more stable climates. We emphasize the need for connectivity across the network, to support species adaption via range shifting. This is especially the case in areas facing high climate change magnitude but low climate velocity, implying that climate conditions similar to current ones will be found nearby.

ecology↗

bioRxiv2023

Megafauna diversity and functional declines in Europe from the Last Interglacial (LIG) to the present

Aim: to estimate European megafauna diversity and functional declines in the present compared to the nearest in time modern-analogue climate period prior to the worldwide diffusion of Homo sapiens. Location: Europe. Time period: Last Interglacial (LIG; ca. 127,000 years ago) to present. Major taxa studied: wild, large ([≥]10 kg) terrestrial mammals. Methods: we assessed the distribution of 48 European megafauna species during the LIG using hindcasting modelling and fossil records. Then, we estimated the decline in megafauna community diversity and potential trait-based functional effects from the LIG to the present, accounting for climate differences between the two periods. Results: from the LIG to the present, species richness and community biomass across Europe dropped by 74.3% ({+/-} 9.9% SD) and 96.7% ({+/-} 4.1% SD), respectively. Functional diversity dropped by 59.1% ({+/-} 11.8% SD) for herbivores and by 48.2% ({+/-} 25.0% SD) for carnivores, while trait-informed potential vegetation and meat consumptions dropped by 91.1% ({+/-} 7.4% SD) and 61.2% ({+/-} 17.2% SD), respectively. The loss in megafauna diversity and associated ecological processes were high everywhere, but particularly in western Europe for carnivores and in the East European Plain for herbivores. We found that potential megafauna richness and functional patterns in the two periods were near identical if only climate-driven differences were considered. Main conclusions: severe, size-biased defaunation has degraded megafauna assemblages and megafauna-mediated ecological processes across Europe from the LIG to the present. These patterns cannot be explained by climate differences between the two periods, thus were likely driven by the impact of prehistoric Homo sapiens. The results suggest that the structure of wild ecosystems of the present strongly deviates from the evolutionary norm, notably with decreased functional heterogeneity and decreased fluxes of biogeochemical compounds across the trophic networks, highlighting the importance of ambitious policies of megafauna community restoration to support ecosystems functioning.

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↗

bioRxiv2022

Machine learning improves global models of plant diversity

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, neural networks) and conventional statistical methods (generalised linear models, generalised additive models) to model species richness and phylogenetic richness of vascular plants worldwide based on 830 regional plant inventories including c. 300,000 species and predictors of past and present environmental conditions. Machine learning showed an outstanding performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modelling techniques (ensemble predictions) to reveal global patterns and centres of plant diversity at multiple resolutions down to 7,774 km2. Our predictive maps provide the most accurate estimates of global plant diversity available to date at grain sizes relevant for conservation and macroecology.

ecology↗

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

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↗