A century of statistical Ecology.

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity.

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

An environmental habitat gradient and within-habitat segregation enable co-existence of ecologically similar bird species.

Niche theory predicts that ecologically similar species can coexist through multidimensional niche partitioning. However, owing to the challenges of accounting for both abiotic and biotic processes in ecological niche modelling, the underlying mechanisms that facilitate coexistence of competing species are poorly understood. In this study, we evaluated potential mechanisms underlying the coexistence of ecologically similar bird species in a biodiversity-rich transboundary montane forest in east-central Africa by computing niche overlap indices along an environmental elevation gradient, diet, forest strata, activity patterns and within-habitat segregation across horizontal space. We found strong support for abiotic environmental habitat niche partitioning, with 55% of species pairs having separate elevation niches. For the remaining species pairs that exhibited similar elevation niches, we found that within-habitat segregation across horizontal space and to a lesser extent vertical forest strata provided the most likely mechanisms of species coexistence. Coexistence of ecologically similar species within a highly diverse montane forest was determined primarily by abiotic factors (e.g. environmental elevation gradient) that characterize the Grinnellian niche and secondarily by biotic factors (e.g. vertical and horizontal segregation within habitats) that describe the Eltonian niche. Thus, partitioning across multiple levels of spatial organization is a key mechanism of coexistence in diverse communities.

Reconsidering priorities for forest conservation when considering the threats of mining and armed conflict.

Many threats to biodiversity can be predicted and are well mapped but others are uncertain in their extent, impact on biodiversity, and ability for conservation efforts to address, making them more difficult to account for in spatial conservation planning efforts, and as a result, they are often ignored. Here, we use a spatial prioritisation analysis to evaluate the consequences of considering only relatively well-mapped threats to biodiversity and compare this with planning scenarios that also account for more uncertain threats (in this case mining and armed conflict) under different management strategies. We evaluate three management strategies to address these more uncertain threats: 1. to ignore them; 2. avoid them; or 3. specifically target actions towards them, first individually and then simultaneously to assess the impact of their inclusion in spatial prioritisations. We apply our approach to the eastern Democratic Republic of the Congo (DRC) and identify priority areas for conserving biodiversity and carbon sequestration services. We found that a strategy that avoids addressing threats of mining and armed conflict more often misses important opportunities for biodiversity conservation, compared to a strategy that targets action towards areas under threat (assuming a biodiversity benefit is possible). We found that considering mining and armed conflict threats to biodiversity independently rather than simultaneously results in 13 800-14 800 km2 and 15 700-25 100 km2 of potential missed conservation opportunities when undertaking threat-avoiding and threat-targeting management strategies, respectively. Our analysis emphasises the importance of considering all threats that can be mapped in spatial conservation prioritisation.

A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity.

Community occupancy models estimate species-specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates toward the community mean can mask differences among groups. Infinite-mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species-level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP-COM with different dimensions (i.e., different numbers of model parameters with species-level DP random effects) and under varying cluster differences. Because the DP-COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP-COM model to a bird data set from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community-level covariate coefficients on occupancy probability were generally unbiased with (near-) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP-COM was similar. The DP-COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP-COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP-COM, and considering the computational burden for the DP models, we recommend using the DP-COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP-COM.

Environmental determinants influencing anthrax distribution in Queen Elizabeth Protected Area, Western Uganda.

Bacillus anthracis, the bacteria that causes anthrax, a disease that primarily affects herbivorous animals, is a soil borne endospore-forming microbe. Environmental distribution of viable spores determines risky landscapes for herbivore exposure and subsequent anthrax outbreaks. Spore survival and longevity depends on suitable conditions in its environment. Anthrax is endemic in Queen Elizabeth Protected Area in western Uganda. Periodic historical outbreaks with significant wildlife losses date to 1950s, but B. anthracis ecological niche in the ecosystem is poorly understood. This study used the Maximum Entropy modeling algorithm method to predict suitable niche and environmental conditions that may support anthrax distribution and spore survival. Model inputs comprised 471 presence-only anthrax occurrence data from park management records of 1956-2010, and 11 predictor variables derived from the World Climatic and Africa Soil Grids online resources, selected considering the ecology of anthrax. The findings revealed predicted suitable niche favoring survival and distribution of anthrax spores as a narrow-restricted corridor within the study area, defined by hot-dry climatic conditions with alkaline soils rich in potassium and calcium. A mean test AUC of 0.94 and predicted probability of 0.93 for anthrax presence were registered. The five most important predictor variables that accounted for 93.8% of model variability were annual precipitation (70.1%), exchangeable potassium (12.6%), annual mean temperature (4.3%), soil pH (3.7%) and calcium (3.1%). The predicted suitable soil properties likely originate from existing sedimentary calcareous gypsum rocks. This has implications for long-term presence of B. anthracis spores and might explain the long history of anthrax experienced in the area. However, occurrence of suitable niche as a restricted hot zone offers opportunities for targeted anthrax surveillance, response and establishment of monitoring strategies in QEPA.

Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania.

INTRODUCTION: Anthrax is caused by the spore-forming, Gram-positive bacterium Bacillus anthracis. The aim of this study was to predict the potential distribution of B. anthracis in Tanzania and produce epidemiological evidence for the management of anthrax outbreaks in the country. METHODS: The Maxent algorithm was used to predict areas at risk of anthrax outbreaks based on the occurrence and environmental data in Arusha and Kilimanjaro regions; the model was later transferred to predict the entire country. Seventy percent of the occurrence data were used to train the model, while 30% were used for model evaluation. RESULTS: Four regions of northern Tanzania are predicted to have a high risk for anthrax outbreaks, while the southern and western regions had low-risk areas. Soil type (56.5%), soil pH (23.7%), and isothermally (10.4%) were the most important variables for the model prediction, and the most significant soil types were solonetz, fluvisols, and lithosols. CONCLUSIONS: A strong risk level across districts of the Tanzania mainland was identified in this study. A total of 18 districts in Tanzania Mainland are predicted to be at very high risk of an anthrax outbreak occurrence. These findings are important for policymakers to effectively mount targeted control measures for anthrax outbreaks in Tanzania.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

INTRODUCTION: Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. METHODS: The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. RESULTS: We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. CONCLUSIONS: Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

Assessing the Threat of Amphibian Chytrid Fungus in the Albertine Rift: Past, Present and Future.

Batrachochytrium dendrobatidis (Bd), the cause of chytridiomycosis, is a pathogenic fungus that is found worldwide and is a major contributor to amphibian declines and extinctions. We report results of a comprehensive effort to assess the distribution and threat of Bd in one of the Earth's most important biodiversity hotspots, the Albertine Rift in central Africa. In herpetological surveys conducted between 2010 and 2014, 1018 skin swabs from 17 amphibian genera in 39 sites across the Albertine Rift were tested for Bd by PCR. Overall, 19.5% of amphibians tested positive from all sites combined. Skin tissue samples from 163 amphibians were examined histologically; of these two had superficial epidermal intracorneal fungal colonization and lesions consistent with the disease chytridiomycosis. One amphibian was found dead during the surveys, and all others encountered appeared healthy. We found no evidence for Bd-induced mortality events, a finding consistent with other studies. To gain a historical perspective about Bd in the Albertine Rift, skin swabs from 232 museum-archived amphibians collected as voucher specimens from 1925-1994 were tested for Bd. Of these, one sample was positive; an Itombwe River frog (Phrynobatrachus asper) collected in 1950 in the Itombwe highlands. This finding represents the earliest record of Bd in the Democratic Republic of Congo. We modeled the distribution of Bd in the Albertine Rift using MaxEnt software, and trained our model for improved predictability. Our model predicts that Bd is currently widespread across the Albertine Rift, with moderate habitat suitability extending into the lowlands. Under climatic modeling scenarios our model predicts that optimal habitat suitability of Bd will decrease causing a major range contraction of the fungus by 2080. Our baseline data and modeling predictions are important for comparative studies, especially if significant changes in amphibian health status or climactic conditions are encountered in the future.