Ecography最新文献

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.

Developing species distribution models (SDMs) requires high-quality species occurrence records. These records, stemming from various sources with different sampling procedures, are often archived in open-access databases, making automated data quality checks inevitable. Temporal, geographic, and taxonomic quality checks are usually conducted in SDM workflows, but checking for records distant in environmental space, i.e. outliers, is often ignored.

Here, we present ‘specleanr', an R package that contains 20 outlier detection methods (ODMs) that can be ensembled to identify potential outliers in environmental predictors. These methods are categorized into 1) species-specific ecological range, 2) univariate, and 3) multivariate ODMs. All potential outliers flagged by the different methods are pooled to identify absolute outliers (records appearing in multiple methods). The local regression (LOESS) method is then used to automatically set a threshold that optimally identifies the absolute outliers. Additionally, clustering records into poor, fair, moderate, very strong, and perfect outliers, as well as non-outliers, is possible based on each record's likelihood as a potential outlier, which allows expert assessment.

We demonstrated the approach to 15 fish species from the Danube River Basin, including native, alien, threatened, and common species. We fitted SDMs using bioclimatic and hydromorphological parameters. We compared the model area under the curve (AUC) before and after outlier removal using three scenarios: 1) the LOESS method, 2) removing very strong outliers, and 3) removing perfect outliers. The results showed a significant improvement in the model AUC, with generally small to moderate effect sizes after outlier removal.

‘specleanr' is generalizable across taxonomic groups, data types, ecological realms, and geographic regions. Beyond SDMs, it can also be broadly used in general data analysis where outlier detection is essential. We provide detailed vignettes to support package use. ‘specleanr' offers a user-friendly and reproducible approach for handling outliers in biogeographical modeling and general data analysis workflows.

Cultivation by humans is the primary mode of introduction for naturalized plants and an important driver of naturalization, a critical step in the invasion process. Historical records of cultivated plants can represent introduced species pools and propagule pressure, allowing for tests of how species' traits and environmental context affect naturalization while accounting for human influence. Ruderal traits, which generally promote naturalization, may not be universally advantageous across closed versus open landscapes (forest versus grassland/shrubland) or different agricultural land use conversion types, though such context dependence has not yet been demonstrated at a broad scale. We analyzed the naturalization of 3949 cultivated ornamental non-native plant taxa that were for sale in nursery and seed catalogs in the conterminous United States during a period over 200 years to test for context dependence between traits associated with ruderality (short lifespan, shade intolerance, and self-compatibility) and estimates of historical forest/grassland cover and agricultural land use change. We found that present-day naturalization was closely tied to longer cultivation duration and greater cultivation extent. While ruderal traits tended to promote naturalization, perennial lifespan and shade tolerance favored naturalization in US states with higher forest cover, which is consistent with an alternative invasion strategy in closed-canopy systems. Land use conversion to pasture and succession of abandoned agricultural land promoted naturalization of disturbance-adapted plants in both forest and grassland landscapes. Our results emphasize the central role of cultivation in plant invasion and provide spatially and temporally extensive evidence that, while ruderal traits are important predictors of naturalization, they are dependent on the landscape context into which plants are introduced. Our work demonstrates the importance of integrating historical cultivation and land use/cover data for a nuanced understanding of the ecological factors that drive plant naturalization.

Species distribution models (SDMs), broadly referring to both species distribution and ecological niche modelling frameworks, are widely used to predict habitat suitability. However, their performance can be biased by uneven sampling effort in occurrence data. Building on two existing approaches, we propose a novel method for sampling bias correction, consisting of the estimation of observer kernel densities for individual species and their subsequent weighting according to the relative contribution of individual observers to the total number of focus species presences. This approach, the ‘presence-weighted observer-oriented approach' (PW-OOA), aimed to provide a better estimation of sampling effort, thus further improving SDM prediction performance. Using bird occurrence data from the Czech Republic, we modelled the distributions of 109 species using four approaches to bias correction: spatial thinning of species presences (STSP), target group occurrences background (TGOB), TGOB+ (tuned up by adjusting kernel smoothing bandwidths) and the new PW-OOA method. We compared the results with simple random background sampling. Models were evaluated using independent reference (presence–absence) data. The PW-OOA method outperformed the other approaches, with the greatest improvement detected for species with higher prevalence. However, as internal validation can be misleading with biased occurrences, we recommend TGOB+ as the most robust approach without independent data; with such data, PW-OOA is superior. While no single optimal combination of bandwidth and observers' weights was identified across species, the PW-OOA method provides a flexible framework to account for observer-specific sampling biases. This study demonstrates the crucial importance of considering the behavior of individual observers and sampling intensity smoothing when correcting for sampling bias in SDMs based on unstructured opportunistic occurrence data.

Temperate deciduous forests of North America, west Eurasia, and east Eurasia share a common origin but were later separated by major geographic barriers. Here, we examine their diverging biodiversity and evolutionary patterns by analyzing floristic richness, phylogenetic turnover, and community evolutionary distinctiveness (CED). We analyzed ~ 9600 vegetation plots across the Northern Hemisphere to calculate species rarefaction curves, within- and between-region phylogenetic turnover, and CED to assess the presence of evolutionarily isolated lineages within communities. We then modeled CED as a function of present-day and last glacial maximum (LGM) macro-environmental factors using boosted regression trees (BRTs). East Eurasian forests had the highest floristic richness, especially among woody species, while west Eurasia featured the richest herbaceous component. Within-region phylogenetic turnover was lowest in east Eurasia, followed by North America and west Eurasia. Between-region phylogenetic turnover was highest between west Eurasia and east Eurasia, and lowest for North America–east Eurasia. North America ranked highest in CED, followed by east Eurasia and west Eurasia. The BRTs revealed contrasting effects of the current and LGM climate on CED across regions. Present precipitation seasonality had a striking negative impact on CED in east Eurasia, whereas temperature seasonality had a strong negative effect in west Eurasia. East Eurasia's exceptional woody, gymnosperm, and fern diversity may reflect the region's long-term climatic and geological stability, which has allowed the persistence and diversification of ancient lineages. Meanwhile, the pronounced evolutionary distinctiveness of North American forests may be linked to more intense climatic and tectonic shifts over both Quaternary and deeper timescales. These findings highlight the unique evolutionary legacies of the temperate deciduous biome and call for future research that expands geographic and climatic coverage to capture the full diversity of temperate deciduous forests worldwide.

Human impacts are forcing species towards marginal and suboptimal portions of their historical ranges. Cetaceans are now under protection, but are still threatened by fishing activities, which reduce fish stocks, alter their feeding behavior, and can cause mortality due to bycatch. Here, we investigated how different fishing activities affect cetacean population density patterns in the Mediterranean, one of the most impacted and fished seas.

We collected 366 population density estimates for eight cetacean species. We then classified species into four trophic groups (planktivorous, piscivorous, teutophagous, generalist) and modelled their density as a function of both environmental and fishing variables (artisanal, demersal destructive, demersal non-destructive with low bycatch, demersal non-destructive with high bycatch, and pelagic fishing with low bycatch). Finally, to quantify human contribution to the observed geographic pattern of population density, we predicted and compared population density patterns under a baseline fishing and a minimum fishing scenario.

The four groups of cetacean species exhibited diverse responses to environmental and fishing variables. Demersal destructive fishing consistently had a negative influence on species population density. In contrast, others, such as demersal non-destructive fishing, showed mixed effects, including a potential attraction effect on piscivorous species. Overall, we predicted a probable change in the geographic pattern of population density of cetaceans in response to fishing activities, especially along the coasts in planktivorous species and in offshore areas in generalist species.

Our study provides evidence of the negative impact of fishing activities on cetacean population density, while highlighting functional group-specific responses to different fishing practices. These findings enhance our understanding of human-induced changes in marine ecosystems, suggesting probable alterations to the natural population density patterns of cetaceans in the Mediterranean Sea.

Human activities have significantly modified habitats, resulting in a global biodiversity crisis. In this study, we leveraged the first national-scale biodiversity survey based on airborne environmental DNA, comparing the effects of three human pressure indices increasing in complexity and scope – a binary urban–rural index, an index integrating land cover and pollutant concentrations as a proxy of human activity, and the composite human footprint index – across mammals, birds, insects, plants and fungi. While most taxa exhibited higher diversity in urban areas compared to rural ones, we uncovered more complex patterns using the landscape-pollution and human footprint indices, including dual diversity maxima at both high and moderate levels of human pressure. We also show an effect of human pressure on community composition even when local species richness remained stable: regardless of the human pressure index, anthropogenic sites were mostly characterized by synanthropic and invasive species. Overall, our results underscore the complex interactions among anthropogenic pressures, taxon diversity and community composition, demonstrating the value of multi-taxon analyses and multiple indices to better understand biodiversity patterns at large scales.