THE ATTACK DYNAMICS AND ECOSYSTEM CONSEQUENCES OF STEM BORER HERBIVORY ON SITKA WILLOW AT MOUNT ST. HELENS

Variation in plant quality across space and time is considered a driving force behind the heterogeneous distribution of herbivorous insects on their host plants. At the same time, herbivory itself can mediate ecosystem processes that can cause feedbacks directly affecting plant quality. Here I examine both of these processes in a primary successional system to ask how insect herbivory can shape successional outcomes. I performed a three year observational study to determine which host plant factors - stress, vigor, and sex - were associated with insect herbivory by the poplar willow weevil (Cryptorynchus lapathi) on Sitka willow (Salix sitchensis), a dioecious pioneer shrub recolonizing Mount St. Helens after the 1980 eruption. I found that weevils prefer or perform best on vigorously growing willows that are seasonally water stressed. This result highlights the need to integrate hypotheses regarding insect response to stress and vigor into a single phenologically based framework focusing on nutrient mobilization to early insect herbivore life stages. I performed a field experiment involving leaf litter from stems attacked and not by weevils to determine whether weevils mediate nutrient cycling by altering willow leaf litter quality or resources available in its root environment. I found that although weevils do not consume leaves directly, stem herbivory is associated with a large reduction in leaf phosphorus, which in turn decelerates phosphorus cycling on Mount St. Helens. Lastly, I performed observational and experimental studies to show that the large female bias seen in willow on Mount St. Helens is not caused by weevil herbivory or other late acting ecological factors, but likely result from biased seed sex ratios. Taken together, these results suggest that weevil herbivory is retarding willow colonization in upland areas on Mount St. Helens, possibly allowing for alternative successional trajectories

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

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 8 (2017): 832, doi:10.1038/s41467-017-00890-0.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.H.J.L., C.C.-C., G.H., C.Y., and K.T.S. gratefully acknowledge funding provided by US National Aeronautics and Space Administration Award No. NNX14AC32G and U.S. National Science Foundation Office of Polar Programs Award No. NSF/OPP-1255058. S.J., L.L., M.M.H., Y.L., and R.J. gratefully acknowledge funding provided by US National Aeronautics and Space Administration Award No. NNX14AH74G. H.J.L., C.Y., S.J., Y.L., and R.J. gratefully acknowledge funding provided by U.S. National Science Foundation Office of Polar Programs Award No. NSF/PLR-1341548. S.J. gratefully acknowledges support from the Dalio Explore Fund

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.https://doi.org/10.1002/ecy.395

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

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 Pygoscelis adeliae, Pygoscelis antarctica, Pygoscelis papua, Eudyptes chrysolophus, Aptenodytes patagonicus and Aptenodytes forsteri 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.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

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

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

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 visualization tool designed primarily for policymakers and other non-specialists, and mapppdr, an R package developed to assist the Antarctic science community. This dataset contains records of Pygoscelis adeliae, Pygoscelis antarctica, Pygoscelis papua, Eudyptes chrysolophus, Aptenodytes patagonicus, and Aptenodytes forsteri 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 1892-11-01 to 2022-02-12. This dataset collates together all publicly available breeding colony abundance data (1979-2022) for Antarctic penguins in a single database with standardized 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 previous 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

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

Parameter values used in the simulation study and global bird abundance study.

Log-normal parameter values μ and σ used in both the simulation study and the re-analysis of Callaghan et al.’s global bird abundance data. (PDF)</p

Batrachochytrium dendrobatidis infection intensity data of amphibians in El Cope, Panama

This file contains Batrachochytrium dendrobatidis (i.e., chytrid) infection intensity data of amphibians in El Cope, Panama across > 30 species from 2012 to 2014 (including wet and dry seasons; and stream and trail habitats). A subset of individuals were swabbed twice in sequence. These double swabs were used to estimate the sensitivity of swabs to detect pathogen infection. Number listed under the column "First_swab" represent the chytrid infection load detected on the first swab, and the column "Second_swab" is the chytrid infection load detected on the second swab

Data from: Imperfect pathogen detection from non-invasive skin swabs biases disease inference

1. Conservation managers rely on accurate estimates of disease parameters, such as pathogen prevalence and infection intensity, to assess disease status of a host population. However, these disease metrics may be biased if low-level infection intensities are missed by sampling methods or laboratory diagnostic tests. These false negatives underestimate pathogen prevalence and overestimate mean infection intensity of infected individuals. 2. Our objectives were two-fold. First, we quantified false negative error rates of Batrachochytrium dendrobatidis on non-invasive skin swabs collected from an amphibian community in El Copé, Panama. We swabbed amphibians twice in sequence, and we used a recently developed hierarchical Bayesian estimator to assess disease status of the population. Second, we developed a novel hierarchical Bayesian model to simultaneously account for imperfect pathogen detection from field sampling and laboratory diagnostic testing. We evaluated the performance of the model using simulations and varying sampling design to quantify the magnitude of bias in estimates of pathogen prevalence and infection intensity. 3. We show that Bd detection probability from skin swabs was related to host infection intensity, where Bd infections < 10 zoospores have < 95% probability of being detected. If imperfect Bd detection was not considered, then Bd prevalence was underestimated by as much as 16%. In the Bd-amphibian system, this indicates a need to correct for imperfect pathogen detection caused by skin swabs in persisting host communities with low-level infections. More generally, our results have implications for study designs in other disease systems, particularly those with similar objectives, biology, and sampling decisions. 4. Uncertainty in pathogen detection is an inherent property of most sampling protocols and diagnostic tests, where the magnitude of bias depends on the study system, type of infection, and false negative error rates. Given that it may be difficult to know this information in advance, we advocate that the most cautious approach is to assume all errors are possible and to accommodate them by adjusting sampling designs. The modeling framework presented here improves the accuracy in estimating pathogen prevalence and infection intensity