Ecography最新文献

In response to the striking effects of environmental change, conservation strategies often include the identification of conservation areas that can effectively maintain vulnerable species. Consequently, identifying system-specific conditions that maintain the demographic and genetic viability of species of conservation concern is essential. Connectivity plays a critical role in the persistence of populations. Islands have been model systems to understand connectivity and metapopulation processes and have emerged as particularly favorable targets for conservation. While islands can be isolated from mainland disturbances, it is unknown what degree of isolation is necessary to avoid unfavorable changes but remain sufficiently connected to maintain population viability. To test this question, we explored connectivity within the Apostle Islands, an archipelago of 22 islands within Lake Superior, by comparing historical and contemporary trends in ice bridge connectivity and by simulating the effect of reduced connectivity within this system. We developed a demographically informed individual-based model to explicitly test the role of connectivity to influence the persistence and genetic diversity of American marten Martes americana, a forest carnivore at risk across its southern range boundary. We found that genetic diversity was resilient to moderate changes in ice cover, but a complete loss of connectivity resulted in rapid genetic erosion. Despite genetic erosion, populations persisted as long as nominal connectivity occurred between islands. Our work suggests that connectivity will decline, but martens would be resilient to moderate changes and, in the short term, the Apostle Islands can act as a refuge along this species' southern range boundary. Identifying thresholds in connectivity that maintain populations but allow for isolation from disturbance will be necessary to identify suitable areas for species conservation across space and time.

Basic ecological theory suggests that a tradeoff between competitiveness and stress tolerance dictates species range limits at regional extents. However, empirical support for this key theory remains deficient because the necessary spatial and temporal coverage and scalability of field observations has rarely been achieved. We harnessed an extensive dendroecological network (> 22 000 tree-ring samples from 816 forest inventory plots) to disentangle competition-limited from climate-limited growth in both overstory and understory trees. Growth synchrony among trees thereby served as an integral metric of climate sensitivity, an approach that we justify in supplementary analyses of growth responses to temperature, precipitation, and the standardized precipitation-evapotranspiration index. Sampling plots were arranged along elevational climate and vegetation gradients throughout the Carpathian Mountains, ranging from mixed-species lowland forests to coniferous forests at high elevations. With mixed-effect modelling, we also identified non-climatic factors (stand characteristics, species diversity, and disturbance history) that modulate spatial patterns in the growth rate and synchrony of European beech Fagus sylvatica and Norway spruce Picea abies. Beech exhibited reduced growth and increased climate sensitivity towards higher elevations but performed better when species diversity was higher. The growth of spruce increased towards its lower range boundary, but understory cohorts grew poorly under interspecific competition. Overall, climate sensitivity was lower in more productive stands with benign climatic conditions and in recently disturbed sites with reduced stand density. These contrasting performances at mid-elevations where the two species overlap (900–1300 m a.s.l.) reflect their evolutionary history, which enables them to be competitive (beech) or cold-stress tolerant (spruce). This history will affect interactions between the two species under climate warming and shape macroecological patterns in the Carpathian ecoregion and likely other parts of Europe. Our findings point to a growing advantage of competitively stronger species in montane and subalpine vegetation zones.

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we use remotely sensed burnt area products and a common fire weather index calculated from global meteorological reanalysis data to identify and describe geographic variation in fuel moisture as a flammability threshold. This geospatial analysis identified a wide range of flammability thresholds associated with fire activity across 772 ecoregions, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can ignite and sustain wildfires with higher estimated fuel moisture than previously identified; Mediterranean forests, in contrast, tend to sustain fires with consistently low estimated fuel moisture. Statistical modelling showed that flammability thresholds derived from burnt area are primarily driven by climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation–climate–fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatial and temporal fire activity.

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present-day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio-geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid-21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast-changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.

The future trajectory of global forests is closely intertwined with tree demography, and a major fundamental goal in ecology is to understand the key mechanisms governing spatio-temporal patterns in tree population dynamics. While previous research has made substantial progress in identifying the mechanisms individually, their relative importance among forests remains unclear mainly due to practical limitations. One approach to overcome these limitations is to group mechanisms according to their shared effects on the variability of tree vital rates and quantify patterns therein. We developed a conceptual and statistical framework (variance partitioning of Bayesian multilevel models) that attributes the variability in tree growth, mortality, and recruitment to variation in species, space, and time, and their interactions – categories we refer to as organising principles (OPs). We applied the framework to data from 21 forest plots covering more than 2.9 million trees of approximately 6500 species. We found that differences among species, the species OP, proved a major source of variability in tree vital rates, explaining 28–33% of demographic variance alone, and 14–17% in interaction with space, totalling 40–43%. Our results support the hypothesis that the range of vital rates is similar across global forests. However, the average variability among species declined with species richness, indicating that diverse forests featured smaller interspecific differences in vital rates. Moreover, decomposing the variance in vital rates into the proposed OPs showed the importance of unexplained variability, which includes individual variation, in tree demography. A focus on how demographic variance is organized in forests can facilitate the construction of more targeted models with clearer expectations of which covariates might drive a vital rate. This study therefore highlights the most promising avenues for future research, both in terms of understanding the relative contributions of groups of mechanisms to forest demography and diversity, and for improving projections of forest ecosystems.

Deep neural networks (DNN) have become a central method in ecology. To build and train DNNs in deep learning (DL) applications, most users rely on one of the major deep learning frameworks, in particular PyTorch or TensorFlow. Using these frameworks, however, requires substantial experience and time. Here, we present ‘cito', a user-friendly R package for DL that allows specifying DNNs in the familiar formula syntax used by many R packages. To fit the models, ‘cito' takes advantage of the numerically optimized ‘torch' library, including the ability to switch between training models on the CPU or the graphics processing unit (GPU) which allows the efficient training of large DNNs. Moreover, ‘cito' includes many user-friendly functions for model plotting and analysis, including explainable AI (xAI) metrics for effect sizes and variable importance. All xAI metrics as well as predictions can optionally be bootstrapped to generate confidence intervals, including p-values. To showcase a typical analysis pipeline using ‘cito', with its built-in xAI features, we built a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret DNNs, ‘cito' will make this interesting class of models more accessible to ecological data analysis. A stable version of ‘cito' can be installed from the comprehensive R archive network (CRAN).

Edaphic and vegetation conditions can render climatically suitable sites inadequate for a species to persist, constraining the amount of suitable habitat and the possibilities of tracking preferred climatic conditions as they shift in response to climate change. We combined climatic and remotely sensed data to model current and future distributions of nine extant taxa of ateline primates across the Amazon basin. We used the models to identify and quantify potential range changes and refugia of suitable habitat from the present to the latter half of the 21st century. We applied an ensemble forecasting approach for species distribution models using 596 spatially rarefied occurrences. We parameterised these models combining reflectance data from a basin-wide Landsat TM/ETM+ image composite, and three sets of bioclimatic layers containing data for the current time period, and two different (moderate and worst-case) climate change scenarios for 2041–2070. Eight out of nine taxa are likely to experience pronounced range losses, with seven of them predicted to lose over 50% of their currently suitable habitats irrespective of climate change scenarios. Modelled ateline richness exhibited a broad decrease in high-richness areas, and a possible redistribution along the northernmost parts of western Amazonia. Refugia from 21st century climate change for the whole complex were mostly concentrated in western Amazonia, especially in its southern part. We identified hotspots of vulnerability to climate change and 21st century refugia for all Amazonian atelines while accounting for habitat characteristics that are important to guarantee the continued existence of suitable habitats for these strictly arboreal taxa. Increasing the understanding of climate change impacts on Amazonia's largest primates can help to inform spatial conservation planning decisions and management to sustain forest-dwelling biodiversity over large areas such as Amazonia.