Difficulties in summing log-normal distributions for abundance and potential solutions.

The log-normal distribution, often used to model animal abundance and its uncertainty, is central to ecological modeling and conservation but its statistical properties are less intuitive than those of the normal distribution. The right skew of the log-normal distribution can be considerable for highly uncertain estimates and the median is often chosen as a point estimate. However, the use of the median can become complicated when summing across populations since the median of the sum of log-normal distributions is not the sum of the constituent medians. Such estimates become sensitive to the spatial or taxonomic scale over which abundance is being summarized and the naive estimate (the median of the distribution representing the sum across populations) can become grossly inflated. Here we review the statistical issues involved and some alternative formulations that might be considered by ecologists interested in modeling abundance. Using a recent estimate of global avian abundance as a case study (Callaghan et al. 2021), we investigate the properties of several alternative methods of summing across species' abundance, including the sorted summing used in the original study (Callaghan et al. 2021) and the use of shifted log-normal distributions, truncated normal distributions, and rectified normal distributions. The appropriate method of summing across distributions was intimately tied to the use of the mean or median as the measure of central tendency used as the point estimate. Use of the shifted log-normal distribution, however, generated scale-consistent estimates for global abundance across a spectrum of contexts. Our paper highlights how seemingly inconsequential decisions regarding the estimation of abundance yield radically different estimates of global abundance and its uncertainty, with conservation consequences that are underappreciated and require careful consideration.

Long-term monitoring of Mount St. Helens micrometeorology.

The 1980 volcanic eruption of Mount St. Helens had profound impacts on the geology, hydrology, and ecology of its surrounding landscapes. Consequently, the event provided a unique opportunity to study ecological change over time in relation to abiotic factors. To better assess the role localized environmental conditions play in these larger processes, we have monitored micrometeorological conditions across six disturbance zones on Mount St. Helens created by the eruption. We deployed 823 environmental sensors at 191 sites from 1997 to 2022 to measure the temperature and relative humidity of aquatic (temperature only) and terrestrial habitats in these areas, collecting over 4.2 million measurements in total. Measurements were typically recorded every 30 min from late spring through mid-fall, with the exception being Spirit Lake, where temperatures have been measured hourly on a year-round basis since 2002. These data have been used to address two broad research questions: (1) how small-scale environmental conditions influence patterns of survivorship and/or establishment on Mount St. Helens post-eruption for a range of organisms, including plants, small mammals, birds, amphibians, arthropods, fish, and other aquatic biota, and (2) to quantify and compare these environmental conditions across different disturbance zones, which vary in disturbance type, intensity, and history of post-eruption secondary disturbances. Due to the repeatability of these measurements over many years, these data lend themselves to exploring the relationship between forest succession and microclimate, especially with respect to forest-dwelling organisms whose spread and demography are sensitive to temperature and relative humidity. In addition, this dataset could be used to investigate additional questions related to early succession, disturbance ecology, climate change, or volcano ecology. This dataset is available in the R data package MSHMicroMetR, which also includes an R Shiny data visualization and exploration tool. There is no copyright on the data; please cite this data paper Ecology when using these data.

Antarctic Penguin Biogeography Project: Database of abundance and distribution for the Adélie, chinstrap, gentoo, emperor, macaroni and king penguin south of 60 S.

Background: The Antarctic Penguin Biogeography Project is an effort to collate all known information about the distribution and abundance of Antarctic penguins through time and to make such data available to the scientific and management community. The core data product involves a series of structured tables with information on known breeding sites and surveys conducted at those sites from the earliest days of Antarctic exploration through to the present. This database, which is continuously updated as new information becomes available, provides a unified and comprehensive repository of information on Antarctic penguin biogeography that contributes to a growing suite of applications of value to the Antarctic community. One such application is the Mapping Application for Antarctic Penguins and Projected Dynamics (MAPPPD; www.penguinmap.com), a browser-based search and visualisation tool designed primarily for policy-makers and other non-specialists, and mapppdr, an R package developed to assist the Antarctic science community. This dataset contains records of Pygoscelisadeliae, Pygoscelisantarctica, Pygoscelispapua, Eudypteschrysolophus, Aptenodytespatagonicus and Aptenodytesforsteri annual nest, adult and/or chick counts conducted during field expeditions or collected using remote sensing imagery, that were subsequently gathered by the Antarctic Penguin Biogeography Project from published and unpublished sources, at all known Antarctic penguin breeding colonies south of 60 S from 01-11-1892 to 12-02-2022-02-12. New information: This dataset collates together all publicly available breeding colony abundance data (1979-2022) for Antarctic penguins in a single database with standardised notation and format. Colony locations have been adjusted as necessary using satellite imagery and each colony has been assigned a unique four-digit alphanumeric code to avoid confusion. These data include information previously published in a variety of print and online formats as well as additional survey data not previously published. Previously unpublished data derive primarily from recent surveys collected under the auspices of the Antarctic Site Inventory, Penguin Watch or by the Lynch Lab at Stony Brook University.

Variability, skipped breeding and heavy-tailed dynamics in an Antarctic seabird.

The population dynamics of many colonially breeding seabirds are characterized by large interannual fluctuations that cannot be explained by environmental conditions alone. This variation may be particularly confounded by the use of skipped breeding by seabirds as a life-history strategy, which directly impacts the number of breeding pairs and may affect the accuracy of breeding abundance as a metric of population health. Additionally, large fluctuations in time series may suggest that the underlying population dynamics are heavy tailed, allowing for a higher likelihood of extreme events than expected under Gaussian dynamics. Here, we investigated the effect of demography on time series for abundance of the Adélie penguin Pygoscelis adeliae and explored the occurrence of heavy-tailed dynamics in observed Adélie time series. We focus this study on the Adélie penguin as it is an important bellwether species long used to track the impacts of climate change and fishing on the Southern Ocean ecosystem and shares life-history traits with many colonial seabirds. We quantified the impacts of demographic rates, including skipped breeding, on time series of Adélie abundance simulated using an age-structured model. We also used observed time series of Adélie breeding abundance at all known Antarctic colonies to classify distributions for abundance as Gaussian or non-Gaussian heavy tailed. We then identified the cause of such heavy-tailed dynamics in simulated time series and linked these to spatial patterns in Adélie food resource variability. We found that breeding propensity drives observed breeding fluctuations more than any other vital rate, with high variability in skipped breeding decoupling true abundance from observed breeding abundance. We also found several Antarctic regions characterized by heavy-tailed dynamics in abundance. These regions were often also characterized by high variability in zooplankton availability. In simulated time series, heavy-tailed dynamics were strongly linked to high variability in adult survival. Our results illustrate that stochastic variability in abundance dynamics, particularly the presence of variable rates of skipped breeding, can challenge our interpretation of fluctuations in abundance through time and obscure the relationship between key environmental drivers and population abundance.

Disease-structured N-mixture models: A practical guide to model disease dynamics using count data.

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark-recapture models can produce high-quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark-recapture techniques. Recently developed N-mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N-mixture models are a type of state-space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N-mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease-structured N-mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.

Predictability of demographic rates based on phylogeny and biological similarity.

Lack of demographic data for most of the world's threatened species is a widespread problem that precludes viability-based status assessments for species conservation. A commonly suggested solution is to use data from species that are closely related or biologically similar to the focal species. This approach assumes similar species and populations of the same species have similar demographic rates, an assumption that has yet to be thoroughly tested. We constructed a Bayesian hierarchical model with data on 425 plant species to predict demographic rates (intrinsic rate of population growth, recruit survival, juvenile survival, adult survival, and fecundity) based on biological traits and phylogenetic relatedness. Generally, we found small effects of species-level traits (except woody polycarpic species tended to have high adult survival rates that increased with plant height) and a weak phylogenetic signal for 4 of the 5 demographic parameters examined. Patterns were stronger in adult survival and fecundity than other demographic rates; however, the unexplained variances at both the species and population levels were high for all demographic rates. For species lacking demographic data, our model produced large, often inaccurate, prediction intervals that may not be useful in a management context. Our findings do not support the assumption that biologically similar or closely related species have similar demographic rates and provide further evidence that direct monitoring of focal species and populations is necessary for informing conservation status assessments.

Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise.

Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982-2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide "year effects" strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.Adélie penguins are a key Antarctic indicator species, but data patchiness has challenged efforts to link population dynamics to key drivers. Che-Castaldo et al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to more robust inference regarding dynamics.

Disassembly of a tadpole community by a multi-host fungal pathogen with limited evidence of recovery.

Emerging infectious diseases can cause host community disassembly, but the mechanisms driving the order of species declines and extirpations following a disease outbreak are unclear. We documented the community disassembly of a Neotropical tadpole community during a chytridiomycosis outbreak, triggered by the generalist fungal pathogen, Batrachochytrium dendrobatidis (Bd). Within the first 11 months of Bd arrival, tadpole density and occupancy rapidly declined. Species rarity, in terms of tadpole occupancy and adult relative abundance, did not predict the odds of tadpole occupancy declines. But species losses were taxonomically selective, with glassfrogs (Family: Centrolenidae) disappearing the fastest and tree frogs (Family: Hylidae) and dart-poison frogs (Family: Dendrobatidae) remaining the longest. We detected biotic homogenization of tadpole communities, with post-decline communities resembling one another more strongly than pre-decline communities. The entire tadpole community was extirpated within 22 months following Bd arrival, and we found limited signs of recovery within 10 years post-outbreak. Because of imperfect species detection inherent to sampling species-rich tropical communities and the difficulty of devising a single study design protocol to sample physically complex tropical habitats, we used simulations to provide recommendations for future surveys to adequately sample diverse Neotropical communities. Our unique data set on tadpole community composition before and after Bd arrival is a valuable baseline for assessing amphibian recovery. Our results are of direct relevance to conservation managers and community ecologists interested in understanding the timing, magnitude, and consequences of disease outbreaks as emerging infectious diseases spread globally.

What causes female bias in the secondary sex ratios of the dioecious woody shrub Salix sitchensis colonizing a primary successional landscape?

UNLABELLED: • PREMISE OF THE STUDY: Females often outnumber males in Salix populations, although the mechanisms behind female bias are not well understood and could be caused by both genetic and ecological factors. We investigated several ecological factors that could bias secondary sex ratios of Salix sitchensis colonizing Mount St. Helens after the 1980 eruption.• METHODS: We determined whether S. sitchensis secondary sex ratios varied across disturbance zones created by the eruption and across mesic and hydric habitats within each zone. For one population, we tracked adult mortality, whole-plant reproductive allocation, the number of stems, and plant size for 2 years. In a field experiment, we created artificial streams to test whether vegetative reproduction via stem fragments was sex-biased.• KEY RESULTS: We found a consistent 2:1 female bias in S. sitchensis secondary sex ratios across all disturbance zones and habitats. Despite female plants sometimes allocating more resources (in terms of carbon, nitrogen, and phosphorus) to reproduction than males, we found no evidence of sex-biased mortality. The establishment rate of S. sitchensis experimental stems did not differ between the sexes, indicating that vegetative reproduction was not distorting secondary sex ratios.• CONCLUSIONS: We hypothesize that S. sitchensis secondary sex ratios depend on either early-acting genetic factors affecting the seed sex ratio or sex-specific germination or survival rates before maturity, as opposed to factors associated with reproduction in adult plants.