Ecography最新文献

Many indices based on presence-absence data that compare two communities have been proposed, with the aim to characterize community similarity, species turnover or beta-diversity, as well as other phenomena like community nestedness. These indices are often mathematically convertible to each other and are thus equivalent in terms of their information content. Based on this information equivalence, we classified all the indices to a few families, showing that only three families reflect ecologically relevant and directly interpretable phenomena, namely species turnover (family of Jaccard index that also includes Sørensen index of similarity), nestedness (the family of indices which compare species overlap with species richness of the species-poor community), and the uniformity of species richness (comparing species richness of the two communities). Importantly, our analysis shows that any attempt to partition indices, including Baselga's approach to partition turnover and nestedness (i.e. to control an index for an effect of a different phenomenon), leads either to an index belonging to one of the three abovementioned families, or produces indices that do not measure any ecologically relevant phenomenon. We provide guidance on how to apply pairwise indices to make proper inference about ecological phenomena.

Species distributions are shifting under global change, with mountain ecosystems among the most vulnerable. In such landscapes, the ability to track changing conditions is limited, threatening narrowly distributed species. As a mountain biodiversity hotspot in southwestern Europe, the Pyrenees harbors many such species, making it a key case study for climate vulnerability assessments.

This study implements a bioclimatic niche modeling pipeline to evaluate the impact of climate change on endemic Pyrenean plant species by 2100. Its objectives are to: 1) map current bioclimatic niche suitability, 2) forecast future spatial dynamics, and 3) identify potential climate refugia for conservation. Species occurrences were combined with 19 bioclimatic variables (1 × 1 km resolution) to characterize bioclimatic niche suitability, using an ensemble modeling approach integrating five algorithms (maximum entropy, generalized linear model, generalized additive model, gradient boosting machine, and random forest). Their future spatiotemporal dynamics were projected under four climate scenarios (Shared Socioeconomic Pathways (SSP): 126, 245, 370, 585) for four successive periods spanning 2021–2100.

By 2100, 69% of endemic species are projected to lose over 75% of their bioclimatic niche, and half are expected to face complete losses under high-emission scenarios. Only two species may gain suitable areas, highlighting the need for species-specific conservation strategies. Bioclimatic niches are projected to shift by ~ 180 m upslope and ~ 3 km in latitude on average, with areas of highest multi-species suitability, referred to as bioclimatic hotspots, becoming restricted to elevations above 2000 m. These trends intensify after 2041–2060 period, reflecting escalating climate pressures as the century progresses.

Our findings highlight the profound threat climate change may pose to endemic Pyrenean flora, with widespread bioclimatic niche losses projected by the century's end and high-elevation refugia emerging as key conservation priorities. Anticipating these shifts and integrating them into conservation planning will be crucial for mitigating high-elevation biodiversity loss in a rapidly changing world.

The scientific community remains divided on the most effective way to design landscapes for biodiversity conservation or restoration. Although there is a consensus that habitat loss is the main cause of biodiversity decline worldwide, the extent to which fragmentation (i.e. the division of remaining habitats into smaller areas) contributes to this decline is a subject of ongoing debate. The spatial arrangement of remaining patches and the nature and permeability of the intermediate matrix (i.e. how easily animals can move through it) are other elements related to habitat loss that are little considered. A better understanding of the effects of these factors on populations could help the community move forward.

Here, we conducted a multigenerational, landscape-scale experiment with the microarthropod Folsomia candida and quantified the respective effects of matrix resistance and inter-patch distance on colonization rate, population size and extinction, at fixed habitat amount. We found that the amount of reachable habitat in the landscape, encompassing both the quantity of habitat and the matrix resistance, is a good predictor of population size and extinction rate. Survival of individuals while crossing different matrix types was the key underlying mechanism, as it determined both colonization rate and demography, preventing individuals from reaching and using remote or difficult-to-access patches. Our study shows that an explicit consideration of matrix resistance considerably improves both our understanding and our predictive ability of populations fate at landscape-scale. It also opens new avenues for landscape ecology theory as well as long-awaited perspectives for applied conservation.

Biological experiments are often short-lived due to logistical or resource-related challenges, and short-term observations are extrapolated to make long-term predictions. However, the effects of experimental treatments on biological communities and processes take time to develop. Consequently, the robustness of conclusions drawn from observations increases with the duration of the experiment. As a striking real-world example, and scattered throughout central Laos, thousands of large stone jars have been left behind from ancient burial rituals. The most famous sites in the Xiengkhouang province are collectively referred to as the Plain of Jars. These jars form a massive biological experiment: for approximately 2000 years, rainwater has interacted with the geological origin of each jar to create unique yet replicated aquatic ecosystems influenced by different tree cover levels. The layout of these jars, with clusters of up to several hundred jars separated by several kilometers, allows for controlled testing of multiple questions within ecology and evolution. Here, we report, for the first time, how these ancient mesocosms can be used to test ecosystem responses to local abiotic variation and disturbance. We show that tree cover dominates every jar ecosystem's state, and that variations in tree cover density create gradients in oxygen (O₂) and nutrient concentrations among jar ecosystems. These initial findings show that litter contribution to aquatic ecosystems leads to higher nutrient content and lower O₂ concentration, even in systems under different long-term selection, in the oldest man-made ecosystems ever analyzed. This first environmental analysis provides a fundamental understanding of a unique environment and offers trajectories for future exploration.

As key members of the terrestrial food webs and vital contributors to wood decomposition, beetles play essential roles in ecosystem services but are experiencing widespread declines under climate change. While protecting and restoring forests with high tree species diversity is widely acknowledged as a nature-based solution for climate change mitigation, it remains uncertain whether it helps maintain the stability of higher trophic communities (e.g. beetles) under climate change. Here, we used the comprehensive forest and ground-dwelling beetle inventory dataset spanning the entire latitudinal range of the Japanese archipelago, monitored from 2004 to 2018, to investigate how tree species diversity affects the temporal stability of beetle biomass. We found that tree species diversity increased beetle biomass and its temporal stability. Specifically, higher tree diversity supported greater beetle and trophic diversity, which enhanced the asynchronous population dynamics across species and trophic levels (i.e. species and trophic asynchrony). Meanwhile, higher beetle and trophic diversity promoted temporal stability at the species level (i.e. species stability). Higher asynchrony and species stability jointly increased temporal stability within beetle communities. Our results underscore the potential of conservation efforts targeting forest diversity to uphold the ecosystem functions of higher trophic level communities (e.g. beetles) under climate change.

River networks are complex ecosystems characterized by a continuous exchange of material and energy through longitudinal gradients. These ecosystems are threatened by various human-induced stressors, which frequently co-occur and may interact in complex ways, potentially triggering cascading effects throughout the river network. Aiming at assessing single and combined effects of flow intermittency and light pollution on macroinvertebrate communities, we performed a multiple stressors experiment in 18 flow-through mesocosms. Each mesocosm was designed to mimic a simplified river network, with two upstream tributaries merging downstream, allowing us to assess both local and cascading effects. The experiment was performed in summer 2021 over seven weeks, applying the stressors either separately or co-occurring in the upstream sections, following a randomized block design. Flow intermittency was simulated as the ponded phase of the drying process, whereas light pollution was applied with LED strips set to 10 lux. Drifting macroinvertebrates were sampled weekly during the treatment phase, and benthic macroinvertebrates were sampled at the end of the treatment phase. Both stressors, when applied individually, reduced benthos richness and abundance, whereas drift decreased with flow intermittency and increased with light pollution. When co-occurring upstream, stressors showed the dominant effects of flow intermittency on the benthos and interactive effects on the drift. The effects of the single stressors and their interactions cascaded along the river network, with stronger downstream effects when stressors co-occurred upstream. These findings show that the spatial distribution of multiple stressors along the river network can affect their resultant downstream effects, highlighting the importance of framing multiple-stressors research in a spatial context. Considering the pressing needs of the growing human population, our results represent a step forward in anticipating the effects of cumulative stressors and in informing efficient conservation strategies for protecting freshwater ecosystems.

Tree-related microhabitats (TreMs), such as water-filled tree holes (WTHs), are important structures for forest biodiversity, providing habitats for many specialized species, which are however impaired by the intensive forest management of the past. Strategies to maintain and promote TreMs in managed forests, e.g. by establishing old-growth forest patches as stepping stones, have been implemented, but their success has rarely been tested. We experimentally created WTHs in old-growth patches that were established to connect forest nature reserves (FNRs) in a beech forest in Germany. Eight years after creation, we sampled, identified, and measured traits of the invertebrate community that colonized the WTHs. We then investigated how spatial and environmental variables affected taxonomic and functional attributes of communities and populations. A total of 2407 individuals of 13 species were sampled, the majority of which were insect larvae. Abundance, as well as taxonomic and functional diversity attributes and community composition, were influenced by environmental and spatial factors, generally supporting the patch-dynamics and species-sorting metacommunity archetype. At the population level, both spatial and environmental factors affected the abundance and functional diversity of body size distributions, suggesting that dispersal capacities, microhabitat requirements, and competitive abilities of individual species structure communities. The distance to the FNRs had a positive effect on total invertebrate abundance and the abundance of the specialized marsh beetle Prionocyphon serricornis, and a weak negative effect on the functional diversity of the community. Our study underpins the stepping-stone concept of connecting FNRs. The species colonized all newly created microhabitats from source populations, indicating that these patches increase connectivity between the FNRs and thus contribute to forest biodiversity conservation. The negative effects of distance to FNRs on functional diversity suggest that distances between habitat patches should be kept small for such a strategy to be successful and sustainable in the long term.

Future climate change is expected to influence ecosystem dynamics and the biogeographic distribution of biomes. Such shifts would have profound impacts on biodiversity and the provision of ecosystem services that are essential for humans. A robust understanding of potential future biome changes is therefore required to inform conservation and adaptation strategies. Here, we compared future biome changes in Africa modeled using a process-based dynamic vegetation model (aDGVM) and species distribution models (SDMs) for different representative concentration pathway (RCP) scenarios, and assessed the impacts of plant-physiological effects of elevated CO₂ on vegetation. We show that species distribution models can reproduce biome patterns simulated by the aDGVM for current climate conditions (k values between 0.69 and 0.80 for different scenarios, indicating substantial agreement). However, future biome projections differed between modeling approaches. Projections from SDMs showed the lowest magnitude of biome shifts (14.1% of the area for RCP4.5) and were more similar to aDGVM simulations that excluded the effects of elevated CO₂ on vegetation. Projections from the aDGVM that included CO₂ effects showed the highest magnitude of biome changes (25% of the area for RCP4.5). The aDGVM showed mainly transitions towards more wood-dominated biomes, whereas SDMs projected forest dieback, particularly in the RCP8.5 scenario. Annual precipitation, dry-season precipitation and, in the aDGVM, CO₂, were the main contributors explaining the most frequent biome transitions, followed by temperature. We conclude that all models project biome changes, primarily along biome boundaries, but the extent differs. CO₂ effects, as included in process-based models, strongly influenced future vegetation. Different modeling approaches are necessary to quantify the range of possible future biome shifts and identify areas with high likelihood of undesired vegetation change.

A number of modelling frameworks exist to estimate resilience from ecological datasets. A subset of these frameworks seeks to estimate the whole ‘stability landscape', which can be used to calculate resilience and identify stable states and tipping points. These methods provide opportunities for insights into possible causes and consequences of variation in ecosystem resilience and dynamics. However, because such models can be complex to implement, there has so far been a substantial barrier to their application in ecological research. Here, we present the ‘mixglm' package for R software, which parametrizes stability landscapes using a mixture model approach. It provides tools for the calculation of resilience, identification of stable states and tipping points, as well as visualization functions. Flexible model specification allows the mean, precision, and probability of each mixture component to be linked to multiple predictors, such as environmental covariates. ‘mixglm' is based on Bayesian inference via NIMBLE and supports normal, beta, gamma, and negative binomial distributed response variables. We illustrate the use of ‘mixglm' with a published case of tree cover in South America, which reports a stability landscape with distinct stable states. Using ‘mixglm', we replicated the identification of these states. Moreover, we quantified the uncertainty of our estimates, and computed resilience estimates of South America's forests. We also conducted a power analysis to provide guidance regarding required sample sizes. ‘mixglm' can be readily used to describe stability landscapes and identify stable states in most spatial datasets, and it is accompanied by tools for the calculation of resilience estimates.