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Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or ion of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species‐level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long‐term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long‐term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.

Caribbean reef corals have experienced unprecedented declines from climate change, anthropogenic stressors and infectious diseases in recent decades. Since 2014, a highly lethal, new disease, called stony coral tissue loss disease, has impacted many reef-coral species in Florida. During the summer of 2018, we noticed an anomalously high disease prevalence affecting different coral species in the northern portion of the Mexican Caribbean. We assessed the severity of this outbreak in 2018/2019 using the AGRRA coral protocol to survey 82 reef sites across the Mexican Caribbean. Then, using a subset of 14 sites, we detailed information from before the outbreak (2016/2017) to explore the consequences of the disease on the condition and composition of coral communities. Our findings show that the disease outbreak has already spread across the entire region by affecting similar species (with similar disease patterns) to those previously described for Florida. However, we observed a great variability in prevalence and tissue mortality that was not attributable to any geographical gradient. Using long-term data, we determined that there is no evidence of such high coral disease prevalence anywhere in the region before 2018, which suggests that the entire Mexican Caribbean was afflicted by the disease within a few months. The analysis of sites that contained pre-outbreak information showed that this event considerably increased coral mortality and severely changed the structure of coral communities in the region. Given the high prevalence and lethality of this disease, and the high number of susceptible species, we encourage reef researchers, managers and stakeholders across the Western Atlantic to accord it the highest priority for the near future.

Experiments are widely used in ecology, particularly for assessing global change impacts on ecosystem function. However, results from experiments often are inconsistent with observations made under natural conditions, suggesting the need for rigorous comparisons of experimental and observational studies. We conducted such a “reality check” for a grassland ecosystem by compiling results from nine independently conducted climate change experiments. Each experiment manipulated growing season precipitation (GSP) and measured responses in aboveground net primary production (ANPP). We compared results from experiments with long-term (33-yr) annual precipitation and ANPP records to ask if collectively (n = 44 experiment-years) experiments yielded estimates of ANPP, rain-use efficiency (RUE, grams per square meter ANPP per mm precipitation), and the relationship between GSP and ANPP comparable to observations. We found that mean ANPP and RUE from experiments did not deviate from observations. Experiments and observational data also yielded similar functional relationships between ANPP and GSP, but only within the range of historically observed GSP. Fewer experiments imposed extreme levels of GSP (outside the observed 33-yr record), but when these were included, they altered the GSP–ANPP relationship. This result underscores the need for more experiments imposing extreme precipitation levels to resolve how forecast changes in climate regimes will affect ecosystem function in the future.

Understanding how annual climate variation affects population growth rates across a species’ range may help us anticipate the effects of climate change on species distribution and abundance. We predict that populations in warmer or wetter parts of a species’ range should respond negatively to periods of above average temperature or precipitation, respectively, whereas populations in colder or drier areas should respond positively to periods of above average temperature or precipitation. To test this, we estimated the population sensitivity of a common shrub species, big sagebrush (Artemisia tridentata), to annual climate variation across its range. Our analysis includes 8,175 observations of year-to-year change in sagebrush cover or production from 131 monitoring sites in western North America. We coupled these observations with seasonal weather data for each site and analyzed the effects of spring through fall temperatures and fall through spring accumulated precipitation on annual changes in sagebrush abundance. Sensitivity to annual temperature variation supported our hypothesis: years with above average temperatures were beneficial to sagebrush in colder locations and detrimental to sagebrush in hotter locations. In contrast, sensitivity to precipitation did not change significantly across the distribution of sagebrush. This pattern of responses suggests that regional abundance of this species may be more limited by temperature than by precipitation. We also found important differences in how the ecologically distinct subspecies of sagebrush responded to the effects of precipitation and temperature. Our model predicts that a short-term temperature increase could produce an increase in sagebrush cover at the cold edge of its range and a decrease in cover at the warm edge of its range. This prediction is qualitatively consistent with predictions from species distribution models for sagebrush based on spatial occurrence data, but it provides new mechanistic insight and helps estimate how much and how fast sagebrush cover may change within its range.

Aim The long‐term survival of many mammal populations relies on how effectively we mitigate the threat from unsustainable hunting. Yet, hunting activities are often cryptic, especially in unprotected forests. Here, we investigate whether hunting signs can help understand the spatiotemporal dynamics of hunting activities in an unprotected African rainforest and examine how landscape characteristics predict various indicators of hunting. Location Ebo forest, Cameroon, Central Africa. Methods We recorded hunting signs (e.g., shotgun cartridges, wire snares, direct sightings) systematically on 23 parallel recce lines across the Ebo forest from 2008 to 2023. We assigned hunting data and spatial covariates (e.g., elevation, distance to village) to 1 × 1 km grid cells and applied generalised linear mixed models to predict the effects of these covariates on hunting. Results We found that hunting was commonplace across the entire Ebo forest. The best‐fitting models for each hunting sign differed considerably. Shotgun cartridges and all hunting signs combined increased significantly from 2016 to 2023 and varied non‐linearly along the village‐distance gradient. We found a progressive inversion of hunting trends along the anthropogenic gradient; between 2016 and 2018, wire snares declined with the distance to road but from 2021, they increased along the road‐distance gradient. Wire snares showed a similar pattern along the river‐distance gradient. Our results also revealed differences between shotgun hunting and snaring along the altitudinal gradient; the effect of elevation was positive on shotgun cartridges and negative on wire snares. Hunting signs and trails decreased significantly with increasing terrain ruggedness. Main Conclusions Using long‐term monitoring data, we show how hunting patterns change dynamically with respect to human and landscape‐related features. We also demonstrate complex hunting patterns along the gradient of human influence, therefore questioning the use of proxies such as the distance to human settlements and even topography to account for hunting pressure. Overall, we show that hunting sign data can reveal the spatiotemporal patterns of hunting, crucial in evaluating the effectiveness of conservation interventions and guiding the prioritisation of limited conservation resources.

Surveillance of adult Culicoides biting midge flight activity is used as an applied ecological method to guide the management of arbovirus incursions on livestock production in Europe and Australia. To date the impact of changes in the phenology of adult vector activity on arbovirus transmission has not been defined. We investigated this at two sites in the UK, identifying 150,000 Culicoides biting midges taken from 2867 collections over a nearly 40 year timescale. Whilst we recorded no change in seasonal activity at one site, shifts in first adult appearance and last adult appearance increased the seasonal activity period of Culicoides species at the other site by 40 days over the time period. Lengthening of the adult activity season was driven by an increase in abundance of Culicoides and correlated with local increases in temperature and precipitation. This diversity in responses poses significant challenges for predicting future transmission and overwintering risk. Policy implications. Our analysis not only shows a dramatic and consistent increase in the adult active period of Culicoides biting midges, but also that this varies significantly between sites. This suggests broad‐scale analyses alone are insufficient to understand the potential impacts of changes in climate on arbovirus vector populations. Understanding the impact of climate change on adult Culicoides seasonality and transmission of arboviruses requires the context of changes in a range of other local ecological drivers.

Aim To determine the role of flow intermittence and species origin in shaping freshwater fish beta diversity across dryland riverscapes. Location Verde and Little Colorado River basins, United States. Methods Fish beta diversity was investigated in two large rivers with marked differences in basin‐wide flow intermittence. Local site (continually flowing perennial vs. periodically flowing intermittent) and species (native vs. non‐native) contributions to beta diversity were compared within each basin and over multiple decades (1987–2013) in relation to changing hydrologic conditions. Metacommunity dynamics were quantified using changes in alpha‐ (local), beta‐ and gamma‐ (regional) diversity through time. Results Beta‐diversity patterns varied in relation to basin‐wide intermittence. Intermittent sites were most influential to beta diversity where basin‐wide intermittence was lower (Verde River), whereas perennial sites were most influential where basin‐wide intermittence was higher (Little Colorado River). In intermittent sites, native fish species contributions to beta diversity tended to be higher than non‐native species contributions. The relative contributions of perennial and intermittent sites to β‐diversity within each basin were invariant to annual flow regimes, whether atypically lower or higher than average flows, but somewhat related to intra‐annual flow variation. Native species contributions to β‐diversity increased in years with high flow conditions in the Verde River. Over time, beta diversity decreased in the lower intermittence Verde River, indicating taxonomic homogenization, but remained relatively unchanged in the Little Colorado River. Main Conclusions Investigations of beta‐diversity components over time are considered pivotal for conservation prioritization and planning. We found that both intermittent and perennial streams play complementary roles in supporting fish beta diversity, and that their relative contributions increase as basin wide availability of the habitat type decreases. Moreover, contributions of intermittent streams to overall beta diversity were relatively consistent through time and supported native fish diversity. Despite weakening policy protections of intermittent streams, these habitats are critical for supporting local species persistence and regional biodiversity.

While studies increasingly document long-term change in community composition, whether long-term change occurs gradually or via rapid reorganization events remains unclear. We used Latent Dirichlet Allocation (LDA) and a change-point model to examine the long-term dynamics of a desert rodent community undergoing compositional change over a 38-yr span. Our approach detected three rapid reorganization events, where changes in the relative abundances of dominant and rare species occurred, and a separate period of increased variance in the structure of the community. These events coincided with time periods, possibly related to climate events, where the total abundance of rodents was extremely low. There are a variety of processes that could link low abundance events with a higher probability of rapid ecological transitions, including higher importance of stochastic processes (i.e., competitive interactions or priority effects) and the removal of structuring effects of competitive dominants or incumbent species. Continued study of the dynamics of community change will provide important information not only on the processes structuring communities, but will also provide guidance for forecasting how communities will undergo change in the future.

Understanding host-symbiont networks is a major question in evolutionary ecology. Birds host a great diversity of endo- and ectosymbiotic organisms, with feather mites (Arachnida: Acariformes: Analgoidea, Pterolichoidea) being among the most diverse of avian symbionts. A global approach to the ecology and evolution of bird–feather-mite associations has been hampered because of the absence of a centralized data repository. Here we present the most extensive data set of associations between feather mites and birds. Data include 12036 records of 1887 feather mite species located on the flight feathers of 2234 bird species from 147 countries. Feather mites typically located inside quills, on the skin, or on downy body feathers are not included. Data were extracted from 493 published sources dating from 1882 to 2015. Data exploration shows that although most continents and bird families are represented, most bird species remain unexplored for feather mites. Nevertheless, this is the most comprehensive data set available for enabling global macroecological analyses of feather mites and their hosts, such as ecological network analyses. This metadata file outlines the structure of these data and provides primary references for all records used.

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.