Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are currently implemented, including 1) Standardized Local Anomalies; 2) Changes in Probabilities of Local Climate Extremes; 3) Changes in Areas of Analogous Climates; 4) Novel Climates; 5) Changes in Distances to Analogous Climates; and 6) Climate Change Velocity. For climate change velocity, three different algorithms are implemented and available within the package including; a) Distanced-based Velocity (“dVe”); b) Threshold-based Velocity (“ve”); and c) Gradient-based Velocity (“gVe”). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The climetrics R package is seamlessly integrated with the rts package for efficient handling of raster time-series data. The functions in climetrics are designed to be user-friendly, making them suitable for less-experienced R users. Detailed comments and descriptions in their help pages and vignettes of the package facilitate further customization by advanced users. In summary, the climetrics R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential universality has led to the development of a suite of computational approaches aimed at improving the reliability of these methods. Classic methods based on univariate data have a long history of use, but recent theoretical advances have expanded EWSs to multivariate datasets, particularly relevant given advancements in remote sensing. More recently, novel machine learning approaches have been developed but have not been made accessible in the R environment. Here, we present EWSmethods – an R package that provides a unified syntax and interpretation of the most popular and cutting edge EWSs methods applicable to both univariate and multivariate time series. EWSmethods provides two primary functions for univariate and multivariate systems respectively, with two forms of calculation available for each: classical rolling window time series analysis, and the more robust expanding window. It also provides an interface to the Python machine learning model EWSNet which predicts the probability of a sudden tipping point or a smooth transition, the first of its form available to R users. This note details the rationale for this open-source package and delivers an introduction to its functionality for assessing resilience. We have also provided vignettes and an external website to act as further tutorials and FAQs.

Species distribution models (SDMs) have been widely used to project terrestrial species’ responses to climate change and are increasingly being used for similar objectives in the marine realm. These projections are critically needed to develop strategies for resource management and the conservation of marine ecosystems. SDMs are a powerful and necessary tool; however, they are subject to many sources of uncertainty. To ensure that SDM projections are informative for management and conservation decisions, sources of uncertainty must be considered and properly addressed. Here we provide ten overarching guidelines that will aid researchers to identify, minimize, and account for uncertainty through the entire model development process, from the formation of a study question to the presentation of results. These guidelines were developed at an international workshop attended by over 50 researchers and practitioners. Although our guidelines are broadly applicable across biological realms, we provide particular focus to the challenges and uncertainties associated with projecting the impacts of climate change on marine species and ecosystems.

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.

A document by Douglas Sponsler. Click on the document to view its contents.

Spatial isolation is a key driver of population-level variability in traits and genotypes worldwide. Geographical distance between populations typically increases isolation, but organisms face additional environmental barriers when dispersing between suitable habitat patches. Despite the predicted universal nature of the causes of isolation, global comparisons of isolation effects across taxa and geographic systems are few. We assessed the strength of isolation due to geographic and macroclimatic distance for paired marine island and paired mainland populations within the same species. Our meta-analysis included published measurements of phenotypic traits and neutral genetic diversity from 1832 populations of 112 plant and animal species at a global scale. As expected, phenotypic differentiation was higher between marine islands than between populations on the mainland, but spatial patterns of neutral genetic diversity did not vary between the two systems. Geographic distance had comparatively weak effects on the spatial patterns of phenotypes and neutral genetic diversity, but only phenotypic trait variability showed signal of system-dependence. These results suggest that spatial patterns of phenotypic variation are determined by system-dependent eco-evolutionary pressures, while the spatial variability of neutral genetic diversity might be universal. Our approach demonstrates that global biodiversity models that include island biology studies may progress our understanding of the interacting effects of spatial habitat structure, geographic- and environmental distances on biological processes underlying spatial population variability. We formulate future research directions for empirical tests and global syntheses in the field.

The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; Myotis spp., Eptesicus nilssonii or Pipistrellus nathusii and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for E. nilssonii and Myotis spp. For E. nilssonii and Myotis spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of P. nathusii increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring network to be an effective and cost-efficient method for gathering bat activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of climate change.

Habitat loss and fragmentation are growing global threats to natural habitats and their networks, posing significant challenges to biodiversity conservation. Among the most vulnerable ecosystems, ponds stand out due to their small sizes exhibiting global declines in numbers and extent. While it is generally agreed that connectivity in habitat networks is crucial for sustaining biodiversity, the effect of connectivity on biodiversity patterns over small-scaled habitat networks has so far received less attention given the general assumption that metacommunities lack spatial structuring on small scales. In this study, we tested whether this holds for multiple passively and actively dispersing organism groups in a well-delineated pond metacommunity of 54 bomb crater ponds situated within 1 km. We investigated the influence of space and environment on species richness and metacommunity structure in these ponds, which share similar age, size, and shape and are subject to strong environmental gradients, making it an ideal study system. We specifically examined the impact of network centrality on species richness and eigenvector-based spatial arrangement on metacommunity structure across different organism groups, including prokaryotes, microeukaryotes, zooplankton, macroinvertebrates, and amphibians. We found that while environmental filtering is the primary driver of community dynamics, there is also a significant spatial signal, particularly for passively dispersing groups, demonstrating the role of the central-peripheral connectivity gradient. These findings highlight the importance of studying and protecting ponds as parts of a network rather than focusing on individual ponds.

Introduced alien species have direct and indirect effects on native communities, leading to lower taxonomic diversity and negative impacts on ecosystem functioning. Moreover, other aspects of diversity could be negatively affected, through alteration of functional and phylogenetic diversity of a community. This is particularly evident in habitats where human disturbance may favour alien species, posing an additional stressor on native communities. Following the community resistance hypothesis (higher diversity, higher resistance to invasion), we hypothesized: i) higher taxonomic, functional and phylogenetic diversity (TD, FD and PD respectively) in non-invaded bird communities (i.e. no alien bird species); and, ii) lower alien species impact on all diversity metrics in less human-disturbed areas. We surveyed bird communities in a modified Mediterranean landscape subject to varying levels of human disturbance. We tested whether TD, FD and PD indices were significantly different between non-invaded and invaded bird communities, and assessed the effect of landscape composition and configuration on these indices. We found that non-invaded communities retained higher TD and FD than invaded communities. Alien birds occupied novel parts of the functional space in invaded communities, but that they did not fully compensate for the taxonomic and functional diversity loss caused by the absence of native species. These results were consistent across different habitats, suggesting weak environmental filtering of communities. Generally, both communities were negatively affected by more human-disturbed areas (e.g. agriculture and urban areas) and enhanced by forest areas and by landscape heterogeneity. Our results suggest that the occurrence of alien birds negatively affects TD and FD (but not PD) of bird community assemblages, but that this impact is stronger in human-modified landscapes. Therefore, since the conservation of biodiversity in anthropogenic habitats is a worldwide challenge, researchers should prioritize efforts to assess the effects of alien species on communities inhabiting those habitats.

Due to climate warming, forests are expanding to higher elevations and latitudes at the expense of tundra vegetation. While the subsequent increase in aboveground biomass is well-documented, there is much speculation regarding the effects on soil organic carbon (SOC) stocks. To provide insight into the consequences of tree encroachment into treeless tundra, we sampled SOC stocks across 36 forest-tundra ecotones along a 1100 km latitudinal gradient in Norway. Our results show that SOC stocks vary greatly within, as well as among treeline ecotones, and that tree biomass and tree species are not correlated with this variability. Instead, SOC stocks increase with temperature, and vary with slope steepness, slope aspect, and soil parent material. Applying a ‘space-for-time substitution’ perspective, our findings suggest that tree encroachment into tundra is unlikely to have immediate consequences for SOC stocks.

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst “perfect” analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species - referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data in Norway. To assess the differences between these datasets, we quantified a Community Temperature Index (CTI) from the co-occurrence data set and compared this with CTI obtained from presence-only data. We also examined the temporal trend in CTI (i.e., thermophilization) in both datasets. To do this, we first established a species-temperature relationship based on data before climate warming. In a preliminary analysis, we assessed the performance of this relationship using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing more species niches), and 3) a combined dataset merging 1) and 2). The transfer function including both datasets performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values in comparison to co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses, our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.

Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been rigorously scrutinized. We propose three procedural steps that should be consistently undertaken and reported on in connectivity modeling studies in order to improve rigour and utility: (1) describe the type of connectivity being modeled, (2) assess the uncertainty and sensitivity of model parameters, and (3) validate the model outputs, ideally with independent data. We reviewed the literature to determine the extent to which studies included these three steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled and 18% conducted some form of sensitivity or uncertainty analysis (or both). Only 19% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data. Our findings highlight a clear need and opportunity to improve the rigour, reliability, and utility of connectivity modeling research. At a minimum, researchers should be transparent about which, if any, of these three steps were undertaken. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately.

Species distribution and habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (Antilocapra americana), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional RSF models - variability in habitat selection - into a tool to improve understanding and predicting animal distribution across space and time.

Long-term ecological studies have consistently reported slower than expected changes in biodiversity over time. One explanation for this phenomenon is that commonly used diversity measurements such as species richness are too coarse to detect mechanisms shaping community assembly. It remains unclear whether abundance based diversity measurements are susceptible to the same problem. To test this, we study temporal changes in abundance based diversity indices across 3341 observations from 880 plots from 15 long-term vegetation plot studies. We then partition diversity change into mechanisms of interest to ecologists: selection, drift, and immigration. We show that these mechanisms are an imperfect predictor of temporal diversity change, creating a mismatch between changes in species abundances and changes in diversity, particularly when shifts in diversity are rapid. To resolve this mismatch, we quantify a less studied mechanism “rarity shifts”, which measure how an individual’s contributions to diversity changes over time. We found rarity shifts are an important component of diversity change across many studies. Furthermore, rarity shifts tend to oppose other mechanisms, particularly selection. Therefore, rarity shifts obscure changes in relative abundance from abundance based diversity measurements, revealing why diversity changes appear slower than expected. Ultimately, understanding rarity shifts can lead to a more accurate understanding of the rate and nature of temporal diversity change in ecology and conservation.

Forest litter decomposition is considered as an essential ecosystem process affecting carbon and nutrient cycling in mountains. However, there exists high uncertainty in accurately estimating the contribution of litter decomposition to terrestrial ecosystems, largely due to the incomparability of different studies and the data limitation in microclimate and non-climatic factors at spatially matched scales. Here we used the tea bag index (TBI) as a standardized protocol to evaluate spatial variations in forest litter decomposition rate (k) and stabilization factor (S) across 10 mountains spanning a wide range of subtropical and tropical forests. Based on the coordinated experiment of 6,864 teabags in 568 sampling sites along elevations, we evaluated the importance of 10 environmental factors covering soil microclimate, edaphic properties, plant diversity, and topography on k and S by using model averaging and linear-mixed effects models. Of the 10 mountains, we found a consistently decreasing pattern of k and an increasing pattern for S along elevations. And the significant effect of k with elevation was mainly found in the western and northmost mountains, while the effect of S occurred in the western and southernmost mountains. For microclimate, there was a general importance of soil temperature (coef. = 0.48) and temperature variation in the growing season (coef. = 0.36) in k, and soil temperature (coef. = -0.46) and moisture variation on S (coef. = -0.36). The dominant role of soil microclimate was mainly found in western mountains with relatively cold environments. For non-climatic drivers, a significant effect of tree diversity on k and a negative correlation of edaphic and topography with S in the western and southern mountains were detected. These findings provide a general understanding of spatial variations of driving factors in forest litter decomposition and highlight a dominant control of soil microclimate in cold forests in high elevations and latitudes.

Anthropogenic impacts are typically detrimental to tropical coral reefs, but the effect of increasing environmental stress and variability on the size structure of coral communities remains poorly understood. This limits our ability to effectively conserve coral reef ecosystems because size specific dynamics are rarely incorporated. Our aim is to quantify variation in the size structure of coral populations across 20 sites along a tropical-to-subtropical environmental gradient on the east coast of Australia (~23°S to 30°S), to determine how size structure changes with a gradient of sea surface temperature, turbidity, productivity and light levels. We use two approaches: 1) linear regression with summary statistics (such as median size) as response variables, a method frequently favoured by ecologists; and 2) compositional functional regression, a novel method using entire size-frequency distributions as response variables. We then predict coral population size structure with increasing environmental stress and variability. Together, we find fewer but larger coral colonies in marginal reefs than in tropical reefs, where environmental conditions are more variable and stressful for tropical corals. Our model predicts that coral populations may become gradually dominated by larger colonies (> 148 cm2) with increasing environmental stress. Fewer but bigger corals suggest low survival of smaller corals, slow growth, and / or poor recruitment. This finding is concerning for the future of coral reefs as it implies populations may have low recovery potential from disturbances. We highlight the importance of continuously monitoring changes to population structure over biogeographic scales.

Site-occupancy modeling is widely used in ecology but its application is still limited in paleoecology, where incomplete detection is routine. Here, we make extensive expansions to an earlier multispecies occupancy model used to estimate the dynamics of relative species abundance in fossil communities. These expansions include incorporating counts of individuals at sites, explicitly allowing for the inclusion of specimens assignable to genus- but not species-level, a situation common in paleontology, and modelling regional presence/absence. We provide simulations to check the performance of this new model, as well as simulations to quantify the benefits of using individual count data versus subsample occupancy data and model estimates versus face-value (raw) estimates, respectively. We also provide an empirical case study using occupancy data from a community of marine benthic colonial animals preserved in the Pleistocene of New Zealand. We find that the new model performs well, especially when it comes to recovering relative abundance dynamics and that it is well worth the effort to both collect individual count data and to include individuals unidentified to species-level in the site-occupancy modelling framework. This extended model can be widely applied in paleoecological settings and is necessary when both the average and uncertainty values of relative abundance dynamics need to be robustly estimated.