# There is no abstract for a Brevia article Endemism is a measure of geographic range restriction which is used to highlight regions with unique biota, found nowhere else on earth. Here, we develop a trait-based metric for a functional approach to endemism studies - functional endemism - and explore global patterns in birds. We find that the world’s islands and mountain ranges are hotspots of functional endemism in birds, highlighting the importance of these ecosystems for conservation.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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