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Geology topics

Elise F. Zipkin

Publications and source records attributed to Elise F. Zipkin.

At least 19 recordsLinked to original sources
USGS2025

Three decades of declines restructure butterfly communities in the Midwestern United States

Insects are declining worldwide, yet gaps remain in our understanding of how declines are distributed across species within communities. Using three decades of butterfly monitoring data aggregated from the Midwestern United States, we found that no butterfly species increased in abundance from 1992 to 2023. 59 out of 136 species declined (annual mean trend: −1.2 to −6.9% per year) with losses distributed across all functional groups including residents, migrants, rare, and common species. Community composition changed such that abundance is now more even across species, driven by more severe losses in abundance—but not richness—of common species compared to rare species. These widespread declines are likely cascading across ecosystems. Conservation efforts that focus on entire communities could mitigate butterfly biodiversity loss.

Illinois, Indiana, Iowa, Michigan, Minnesota, Miss↗
USGS2025

Rapid butterfly declines across the United States during the 21st century

Reports of declining insect populations have received widespread media attention, but evidence for declines has been variable across regions and taxonomic groups. Edwards et al . examined trends in the most surveyed taxon: butterflies (see the Perspective by Inouye). Combining data from 35 citizen science programs across the continental US, the authors found declines in overall butterfly abundance over the past 20 years across almost all major regions. Two-thirds of studied species showed declines of more than 10%. Many insects have the potential for rapid population growth and recovery, but habitat restoration, species-specific interventions, and reducing pesticide use are all likely needed to curb population declines.

contiguous United States↗
USGS2022

Quantifying the conservation status and abundance trends of wildlife communities with detection-nondetection data

Effective conservation requires understanding species' abundance patterns and demographic rates across space and time. Ideally, such knowledge should be available for whole communities, as variation in species' dynamics can elucidate factors leading to biodiversity losses. However, collecting data to simultaneously estimate abundance and demographic rates is often prohibitively time-intensive and expensive for communities of species. We developed a “multi-species dynamic N-occupancy model” to estimate unbiased, community-wide relative abundance and demographic rates. Our model uses detection-nondetection data (e.g., repeated presence-absence surveys) to estimate both species- and community-level parameters as well as the effects of environmental factors. We conducted a simulation study that validated our modeling framework, demonstrating how and when such an approach can be valuable. Using data from a network of camera traps across tropical equatorial Africa, we then used our model to evaluate the statuses and trends of a forest-dwelling antelope community. We estimated relative abundance, rates of recruitment (i.e., reproduction and immigration), and apparent survival probabilities for each species' local population. Our analysis indicated that the antelope community was fairly stable in this region (although 17% of populations [species-park combinations] declined over the study period), with variation in apparent survival linked more closely to differences among national parks rather than individual species' life histories. The multi-species dynamic N-occupancy model requires only detection-nondetection data to evaluate the population dynamics of multiple sympatric species and can thus be a valuable tool for conservation efforts seeking to understand the reasons behind recent biodiversity loss.

Conservation Biology↗
USGS2022

A comparison of monitoring designs to assess wildlife community parameters across spatial scales

Dedicated long-term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs - stratified random, weighted effort, indicator unit, rotating panel, and split panel - to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially-varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of U.S. National Parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10 to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales, and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred, and offer guidance as to how managers can balance common tradeoffs in large-scale and long-term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad-scaled environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program.

Ecological Applications↗
USGS2022

Errors in aerial survey count data: Identifying pitfalls and solutions

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2021

Summary of available data from the monarch overwintering colonies in central Mexico, 1976–1991

Historical estimates of the area occupied by overwintering Danaus plexippus (monarchs) in central Mexico (between winters of 1976 and 1991) were published in García-Serrano and others (2004) and more recently in Mawdsley and others (2020). Our primary objectives were to identify the specific data that informed those estimates and, importantly, determine the degree to which the reported estimates reflect the total size of the overwintering monarch population during that period. Understanding how historical estimates of the overwintering area relate to total population size is necessary to ensure that inferences about population abundance and temporal trends are reliable, particularly as the U.S. Fish and Wildlife Service is in the process of determining if the species should be listed under the U.S. Endangered Species Act.

Open-File Report↗
USGS2020

Moving from decision to action in conservation science

Biodiversity loss is a major threat to the integrity of ecosystems and is projected to worsen, yet the path to successful conservation remains elusive. Decision support frameworks (DSFs) are increasingly applied by resource managers to navigate the complexity, uncertainty, and differing socio-ecological objectives inherent to conservation problems. Most published conservation research that uses DSFs focuses on analytical stages (e.g., identifying an optimal decision), making it difficult to assess and learn from previous examples in a conservation practice context. Here, we (1) evaluate the relationship between the application of decision science and the resulting conservation outcomes, and (2) identify and address existing barriers to the application of DSFs to conservation practice. To do this, we develop a framework for evaluating conservation initiatives using decision science that emphasizes setting attainable goals, building momentum, and obtaining partner buy-in. We apply this framework to a systematic review of amphibian conservation decision support projects, including a follow-up survey of the pertinent conservation practitioners, stakeholders, and scientists. We found that all projects identified optimal solutions to reach stated objectives, but positive conservation outcomes were limited when implementation challenges arose. Further, we identified multiple barriers (e.g., dynamic and hierarchical leadership, scale complexity, limited resource availability) that can inhibit the progression from decision identification to action implementation (i.e., ‘decision-implementation gap’), and to successful conservation outcomes. Based on these results, we provide potential actionable steps and avenues for future development of DSFs to facilitate the transition from decision to action and the realization of conservation successes.

Biological Conservation↗
USGS2019

A comparative analysis of common methods to identify waterbird hotspots

1. Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long-term conservation planning and implementation efforts. 2. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis-Ord Gi*, hotspot persistence, and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012-2014 using a 5 km2 grid. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods. 3. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis-Ord Gi* methods yielded the most similar results across all species analyzed. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation. 4. We recommend considering the ecological question and scale of any conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results. Combining methods using an integrative approach, either within a single analysis or post-hoc, could lead to greater consistency in the identification of waterbird hotspots.

Lake Erie, Lake Huron, Lake Michigan↗
USGS2019

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.

Ecology and Evolution↗
USGS2018

Eco‐evolutionary rescue promotes host–pathogen coexistence

Emerging infectious pathogens are responsible for some of the most severe host mass mortality events in wild populations. Yet, effective pathogen control strategies are notoriously difficult to identify, in part because quantifying and forecasting pathogen spread and disease dynamics is challenging. Following an outbreak, hosts must cope with the presence of the pathogen, leading to host–pathogen coexistence or extirpation. Despite decades of research, little is known about host–pathogen coexistence post‐outbreak when low host abundances and cryptic species make these interactions difficult to study. Using a novel disease‐structured N‐mixture model, we evaluate empirical support for three host–pathogen coexistence hypotheses (source–sink, eco‐evolutionary rescue, and spatial variation in pathogen transmission) in a Neotropical amphibian community decimated by Batrachochytrium dendrobatidis ( Bd ) in 2004. During 2010–2014, we surveyed amphibians in Parque Nacional G. D. Omar Torríjos Herrera, Coclé Province, El Copé, Panama. We found that the primary driver of host–pathogen coexistence was eco‐evolutionary rescue, as evidenced by similar amphibian survival and recruitment rates between infected and uninfected hosts. Average apparent monthly survival rates of uninfected and infected hosts were both close to 96%, and the expected number of uninfected and infected hosts recruited (via immigration/reproduction) was less than one host per disease state per 20‐m site. The secondary driver of host–pathogen coexistence was spatial variation in pathogen transmission as we found that transmission was highest in areas of low abundance but there was no support for the source–sink hypothesis. Our results indicate that changes in the host community (i.e., through genetic or species composition) can reduce the impacts of emerging infectious disease post‐outbreak. Our disease‐structured N‐mixture model represents a valuable advancement for conservation managers trying to understand underlying host–pathogen interactions and provides new opportunities to study disease dynamics in remnant host populations decimated by virulent pathogens.

Ecological Applications↗
USGS2017

Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus

Quantifying how climate and land use factors drive population dynamics at regional scales is complex because it depends on the extent of spatial and temporal synchrony among local populations, and the integration of population processes throughout a species’ annual cycle. We modeled weekly, site-specific summer abundance (1994–2013) of monarch butterflies Danaus plexippus at sites across Illinois, USA to assess relative associations of monarch abundance with climate and land use variables during the winter, spring, and summer stages of their annual cycle. We developed negative binomial regression models to estimate monarch abundance during recruitment in Illinois as a function of local climate, site-specific crop cover, and county-level herbicide (glyphosate) application. We also incorporated cross-seasonal covariates, including annual abundance of wintering monarchs in Mexico and climate conditions during spring migration and breeding in Texas, USA. We provide the first empirical evidence of a negative association between county-level glyphosate application and local abundance of adult monarchs, particularly in areas of concentrated agriculture. However, this association was only evident during the initial years of the adoption of herbicide-resistant crops (1994–2003). We also found that wetter and, to a lesser degree, cooler springs in Texas were associated with higher summer abundances in Illinois, as were relatively cool local summer temperatures in Illinois. Site-specific abundance of monarchs averaged approximately one fewer per site from 2004–2013 than during the previous decade, suggesting a recent decline in local abundance of monarch butterflies on their summer breeding grounds in Illinois. Our results demonstrate that seasonal climate and land use are associated with trends in adult monarch abundance, and our approach highlights the value of considering fine-resolution temporal fluctuations in population-level responses to environmental conditions when inferring the dynamics of migratory species.

Illinois↗
USGS2017

Integrating count and detection–nondetection data to model population dynamics

There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl ( Strix varia ) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.

Ecology↗
USGS2015

Statistical guidelines for assessing marine avian hotspots and coldspots: A case study on wind energy development in the U.S. Atlantic Ocean

Estimating patterns of habitat use is challenging for marine avian species because seabirds tend to aggregate in large groups and it can be difficult to locate both individuals and groups in vast marine environments. We developed an approach to estimate the statistical power of discrete survey events to identify species-specific hotspots and coldspots of long-term seabird abundance in marine environments. We illustrate our approach using historical seabird data from survey transects in the U.S. Atlantic Ocean Outer Continental Shelf (OCS), an area that has been divided into “lease blocks” for proposed offshore wind energy development. For our power analysis, we examined whether discrete lease blocks within the region could be defined as hotspots (3 × mean abundance in the OCS) or coldspots (1/3 ×) for individual species within a given season. For each of 74 species/season combinations, we determined which of eight candidate statistical distributions (ranging in their degree of skewedness) best fit the count data. We then used the selected distribution and estimates of regional prevalence to calculate and map statistical power to detect hotspots and coldspots, and estimate the p -value from Monte Carlo significance tests that specific lease blocks are in fact hotspots or coldspots relative to regional average abundance. The power to detect species-specific hotspots was higher than that of coldspots for most species because species-specific prevalence was relatively low (mean: 0.111; SD: 0.110). The number of surveys required for adequate power (> 0.6) was large for most species (tens to hundreds) using this hotspot definition. Regulators may need to accept higher proportional effect sizes, combine species into groups, and/or broaden the spatial scale by combining lease blocks in order to determine optimal placement of wind farms. Our power analysis approach provides a general framework for both retrospective analyses and future avian survey design and is applicable to a broad range of research and conservation problems.

U.S. Atlantic Ocean Outer Continental Shelf↗
USGS2014

Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation

Determining appropriate statistical distributions for modeling animal count data is important for accurate estimation of abundance, distribution, and trends. In the case of sea ducks along the U.S. Atlantic coast, managers want to estimate local and regional abundance to detect and track population declines, to define areas of high and low use, and to predict the impact of future habitat change on populations. In this paper, we used a modified marked point process to model survey data that recorded flock sizes of Common eiders, Long-tailed ducks, and Black, Surf, and White-winged scoters. The data come from an experimental aerial survey, conducted by the United States Fish & Wildlife Service (USFWS) Division of Migratory Bird Management, during which east-west transects were flown along the Atlantic Coast from Maine to Florida during the winters of 2009–2011. To model the number of flocks per transect (the points), we compared the fit of four statistical distributions (zero-inflated Poisson, zero-inflated geometric, zero-inflated negative binomial and negative binomial) to data on the number of species-specific sea duck flocks that were recorded for each transect flown. To model the flock sizes (the marks), we compared the fit of flock size data for each species to seven statistical distributions: positive Poisson, positive negative binomial, positive geometric, logarithmic, discretized lognormal, zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s closeness tests indicated that the negative binomial and discretized lognormal were the best distributions for all species for the points and marks, respectively. These findings have important implications for estimating sea duck abundances as the discretized lognormal is a more skewed distribution than the Poisson and negative binomial, which are frequently used to model avian counts; the lognormal is also less heavy-tailed than the power law distributions (e.g., zeta and Yule–Simon), which are becoming increasingly popular for group size modeling. Choosing appropriate statistical distributions for modeling flock size data is fundamental to accurately estimating population summaries, determining required survey effort, and assessing and propagating uncertainty through decision-making processes.

Statistical Methodology↗
USGS2013

Conservation of avian diversity in the Sierra Nevada: moving beyond a single-species management focus

Background: As a result of past practices, many of the dry coniferous forests of the western United States contain dense, even-aged stands with uncharacteristically high levels of litter and downed woody debris. These changes to the forest have received considerable attention as they elevate concerns regarding the outcome of wildland fire. However, attempts to reduce biomass through fuel reduction (i.e., thinning of trees) are often opposed by public interest groups whose objectives include maintaining habitat for species of concern such as the spotted owl, Strix occidentalis, the northern goshawk, Accipiter gentilis, and the Pacific fisher, Martes pennanti. Whether protection of these upper-trophic level species confers adequate conservation of avian forest diversity is unknown. Methodology and Principal Findings: We use a multi-species occurrence model to estimate the habitat associations of 47 avian species detected at 742 sampling locations within an 880-km 2 area in the Sierra Nevada. Our approach, which accounts for variations in detectability of species, estimates occurrence probabilities of all species in a community by linking species occurrence models into one hierarchical community model, thus improving inferences on all species, especially those that are rare or observed infrequently. We address how the avian community is influenced by covariates related to canopy cover, tree size and shrub cover while accounting for the impacts of abiotic variables known to affect species distributions. Conclusions and Significance: Environmental parameters estimated through our approach emphasize the importance of within and between stand-level heterogeneity in meeting biodiversity objectives and suggests that many avian species would increase under more open canopy habitat conditions than those favored by umbrella species of high conservation concern. Our results suggest that a more integrated approach that emphasizes maintaining a diversity of habitats across environmental gradients and minimizing urbanization may have a greater benefit to ecosystem functioning then a single-species management focus.

California;Nevada↗
USGS2013

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

PLoS ONE↗