Woody plant encroachment drives population declines in 20% of common open ecosystem bird species

Grassy ecosystems cover more than 40% of the world's terrestrial surface, supporting crucial ecosystem services and unique biodiversity. These ecosystems have experienced major losses from conversion to agriculture with the remaining fragments threatened by global change. Woody plant encroachment, the increase in woody cover threatening grassy ecosystems, is a major global change symptom, shifting the composition, structure, and function of plant communities with concomitant effects on all biodiversity. To identify generalisable impacts of encroachment on biodiversity, we urgently need broad-scale studies on how species respond to woody cover change. Here, we make use of bird atlas, woody cover change data (between 2007 and 2016) and species traits, to assess: (1) population trends and woody cover responses using dynamic occupancy models; (2) how outcomes relate to habitat, diet and nesting traits; and (3) predictions of future occupancy trends, for 191 abundant, southern African bird species. We found that: (1) 63% (121) of species showed a decline in occupancy, with 18% (34) of species' declines correlated with increasing woody cover (i.e. losers). Only 2% (4) of species showed increasing population trends linked with increased woody cover (i.e. winners); (2) Open habitat specialist, invertivorous, ground nesting birds were the most frequent losers, however, we found no definitive evidence that the selected traits could predict outcomes; and (3) We predict open habitat loser species will take on average 52 years to experience 50% population declines with current rates of encroachment. Our results bring attention to concerning region-wide declining bird population trends and highlight woody plant encroachment as an important driver of bird population dynamics. Importantly, these findings should encourage improved management and restoration of our remaining grassy ecosystems. Furthermore, our findings show the importance of lands beyond protected areas for biodiversity, and the urgent need to mitigate the impacts of woody plant encroachment on bird biodiversity.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Fast Bayesian inference for large occupancy datasets

In recent years, the study of species' occurrence has benefited from the increased availability of large-scale citizen-science data. While abundance data from standardized monitoring schemes are biased toward well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species' changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya-Gamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: subset of regressors and nearest neighbor GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species' occurrence, which are used to assess biodiversity change.

Occurrence dynamics of mammals in protected tropical forests respond to human presence and activities

Protected areas (PAs) play a vital role in wildlife conservation. Nonetheless there is concern and uncertainty regarding how and at what spatial scales anthropogenic stressors influence the occurrence dynamics of wildlife populations inside PAs. Here we assessed how anthropogenic stressors influence occurrence dynamics of 159 mammal species in 16 tropical PAs from three biogeographic regions. We quantified these relationships for species groups (habitat specialists and generalists) and individual species. We used long-term camera-trap data (1,002 sites) and fitted Bayesian dynamic multispecies occupancy models to estimate local colonization (the probability that a previously empty site is colonized) and local survival (the probability that an occupied site remains occupied). Multiple covariates at both the local scale and landscape scale influenced mammal occurrence dynamics, although responses differed among species groups. Colonization by specialists increased with local-scale forest cover when landscape-scale fragmentation was low. Survival probability of generalists was higher near the edge than in the core of the PA when landscape-scale human population density was low but the opposite occurred when population density was high. We conclude that mammal occurrence dynamics are impacted by anthropogenic stressors acting at multiple scales including outside the PA itself.

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

BACKGROUND

Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters (Enhydra lutris) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000.

METHODS

Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery.

RESULTS

Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1-8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested.

CONCLUSIONS

This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.

The Role of Climate Changes in the Spread of Freshwater Fishes: Implications for Alien Cool and Warm-Water Species in a Mediterranean Basin

In running waters, under climate change conditions, the combined effect of water warming and decreasing flow rates may encourage colonisation by invasive cool and warm-water fish species. The aim of the study was to analyze the potential climate change effects on the spread of four invasive alien fishes in the Tiber River basin, taking into account the effects of river fragmentation. Fish and environmental data collected in 91 sites over the years 1998–2018, were used to analyze temporal changes in their habitat requirements. A multivariate analysis was conducted, and the hypothesis of a range expansion towards the upstream reaches has been tested. For Barbus barbus, Gobio gobio, Padogobius bonelli and Pseudorasbora parva population abundances and body condition were analyzed. Detectability, occupancy, local extinction and colonization probabilities were estimated. We showed that B. barbus and P. bonelli have significantly extended their range toward upstream. P. parva did not move toward higher altitudes significantly, suggesting that, at this stage, the species has probably reached an equilibrium. River fragmentation, elevation, water temperature and average current speed seem to be major determinants in colonization processes, affecting the dispersal ability of the species. Not surprisingly for species introduced in relatively recent times, the colonization probabilities were much higher than extinction probabilities. Our results provided evidence for some synergistic effects between climate changes and alien fish species invasions, in terms of species range shifts mediated by rising water temperatures, although they should be interpreted cautiously, taking into account that these species most likely were not yet stabilized.

Using environmental DNA and occupancy modelling to estimate rangewide metapopulation dynamics

We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multiseason rangewide site occupancy histories. Second, we developed a novel dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analysed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from 2 years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the 2 years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighbouring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range and, when these surveys are paired with occupancy modelling, can uncover metapopulation dynamics and their drivers.

A statistical forecasting approach to metapopulation viability analysis

Conservation of at-risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two-step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog (Lithobates chiricahuensis) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50-yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0-37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0-2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.

Drought-mediated extinction of an arid-land amphibian: insights from a spatially explicit dynamic occupancy model

Understanding how natural and anthropogenic processes affect population dynamics of species with patchy distributions is critical to predicting their responses to environmental changes. Despite considerable evidence that demographic rates and dispersal patterns vary temporally in response to an array of biotic and abiotic processes, few applications of metapopulation theory have sought to explore factors that explain spatiotemporal variation in extinction or colonization rates. To facilitate exploring these factors, we extended a spatially explicit model of metapopulation dynamics to create a framework that requires only binary presence-absence data, makes few assumptions about the dispersal process, and accounts for imperfect detection. We apply this framework to 22 yr of biannual survey data for lowland leopard frogs, Lithobates yavapaiensis, an amphibian that inhabits arid stream systems in the southwestern United States and northern Mexico. Our results highlight the importance of accounting for factors that govern temporal variation in transition probabilities, as both extinction and colonization rates varied with hydrologic conditions. Specifically, local extinctions were more frequent during drought periods, particularly at sites without reliable surface water. Colonization rates increased when larval and dispersal periods were wetter than normal, which increased the probability that potential emigrants metamorphosed and reached neighboring sites. Extirpation of frogs from all sites in one watershed during a period of severe drought demonstrated the influence of site-level features, as frogs persisted only in areas where most sites held water consistently and where the amount of sediment deposited from high-elevation wildfires was low. Application of our model provided novel insights into how climate-related processes affected the distribution and population dynamics of an arid-land amphibian. The approach we describe has application to a wide array of species that inhabit patchy environments, can improve our understanding of factors that govern metapopulation dynamics, and can inform strategies for conservation of imperiled species.

Insights from distribution dynamics inform strategies to conserve a dhole Cuon alpinus metapopulation in India

Most large carnivore populations currently occur in heterogeneous landscapes, with source populations embedded in a matrix of human-dominated habitats. Understanding changes in distribution of endangered carnivores is critical for prioritizing and implementing conservation strategies. We examined distribution and dynamics of a dhole Cuon alpinus metapopulation, first in 2007 and subsequently in 2015, based on indirect sign surveys across 37, 000sq. km of India's Western Ghats. Predicted dhole occupancy declined from 0.62 (95% CI: 0.58-0.66) in 2007 to 0.54 (95% CI: 0.50-0.58) in 2015. Occupancy was associated with abundance of primary prey species and anthropogenic disturbance. Local extinction appeared to be influenced by forest cover loss, and offset by protected reserves; colonization was influenced by occupancy in neighbouring sites. Perturbation analysis indicated that occupancy was more sensitive to local extinction within reserves and to colonization in sites abutting reserves. The Western Ghats could serve as a stronghold for the endangered dhole, provided future colonizations are facilitated through habitat consolidation beyond reserve boundaries, and local extinctions are prevented by increasing protection efforts within select reserves. We advocate for wildlife managers to adopt a landscape-based approach and periodic monitoring to ensure persistence of the dhole metapopulation in Western Ghats, and in other critical conservation regions across the species' geographic range.

Quantifying spatio-temporal variation of invasion spread

- The spread of invasive species can have far-reaching environmental and ecological consequences. Understanding invasion spread patterns and the underlying process driving invasions are key to predicting and managing invasions. - We combine a set of statistical methods in a novel way to characterize local spread properties and demonstrate their application using simulated and historical data on invasive insects. Our method uses a Gaussian process fit to the surface of waiting times to invasion in order to characterize the vector field of spread. - Using this method, we estimate with statistical uncertainties the speed and direction of spread at each location. Simulations from a stratified diffusion model verify the accuracy of our method. - We show how we may link local rates of spread to environmental covariates for two case studies: the spread of the gypsy moth ( Lymantria dispar), and hemlock woolly adelgid ( Adelges tsugae) in North America. We provide an R-package that automates the calculations for any spatially referenced waiting time data.

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5' × 5' grid cell, or "pentad"). The explanatory variables were distance to major road and exceptional birding locations or "sampling hubs," percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters (Enhydra lutris) in Glacier Bay, in southeastern Alaska.

Multilevel Models for the Distribution of Hosts and Symbionts

Symbiont occurrence is influenced by host occurrence and vice versa, which leads to correlations in host-symbiont distributions at multiple levels. Interactions between co-infecting symbionts within host individuals can cause correlations in the abundance of two symbiont species across individual hosts. Similarly, interactions between symbiont transmission and host population dynamics can drive correlations between symbiont and host abundance across habitat patches. If ignored, these interactions can confound estimated responses of hosts and symbionts to other factors. Here, we present a general hierarchical modeling framework for distributions of hosts and symbionts, estimating correlations in host-symbiont distributions at the among-site, within-site, among-species, and among-individual levels. We present an empirical example from a multi-host multi-parasite system involving amphibians and their micro- and macroparasites. Amphibian hosts and their parasites were correlated at multiple levels of organization. Macroparasites often co-infected individual hosts, but rarely co-infected with the amphibian chytrid fungus. Such correlations may result from interactions among parasites and hosts, joint responses to environmental factors, or sampling bias. Joint host-symbiont models account for environmental constraints and species interactions while partitioning variance and dependence in abundance at multiple levels. This framework can be adapted to a wide variety of study systems and sampling designs.

Land abandonment and changes in snow cover period accelerate range expansions of sika deer

Ongoing climate change and land‐use change have the potential to substantially alter the distribution of large herbivores. This may result in drastic changes in ecosystems by changing plant–herbivore interactions. Here, we developed a model explaining sika deer persistence and colonization between 25 years in terms of neighborhood occupancy and habitat suitability. We used climatic, land‐use, and topographic variables to calculate the habitat suitability and evaluated the contributions of the variables to past range changes of sika deer. We used this model to predict the changes in the range of sika deer over the next 100 years under four scenario groups with the combination of land‐use change and climate change. Our results showed that both climate change and land‐use change had affected the range of sika deer in the past 25 years. Habitat suitability increased in northern or mountainous regions, which account for 71.6% of Japan, in line with a decrease in the snow cover period. Habitat suitability decreased in suburban areas, which account for 28.4% of Japan, corresponding to land‐use changes related to urbanization. In the next 100 years, the decrease in snow cover period and the increase in land abandonment were predicted to accelerate the range expansion of sika deer. Comparison of these two driving factors revealed that climate change will contribute more to range expansion, particularly from the 2070s onward. In scenarios that assumed the influence of both climate change and land‐use change, the total sika deer range increased by between +4.6% and +11.9% from the baseline scenario. Climate change and land‐use change will require additional efforts for future management of sika deer, particularly in the long term.