USGS Science⌕ Search

Geology topics

Holly H. Edwards

Publications and source records attributed to Holly H. Edwards.

7 recordsLinked to original sources
USGS2023

2021–2022 Statewide Abundance Estimates for the Florida Manatee

Knowing the population size of Florida manatees ( Trichechus manatus latirostris ) is critical for conservation and management of this threatened species. Martin et al. (2015) and Hostetler et al. (2018) applied statistical models that incorporated multiple data sources to estimate the statewide abundance of manatees from aerial surveys f lown in 2011–2012 and 2015–2016. We conducted additional aerial surveys in 2021–2022 and applied similar models to provide an updated abundance estimate. This report serves as an update to Hostetler et al. (2018), with most of the text and methodology adapted from the previous report, and provides updated population estimates based on the newly available data. We estimate that the number of manatees in Florida in 2021–2022 was 9,790 (95% Bayesian credible interval 8,350–11,730), of which 4,630 (3,960–5,420) were on the west coast of Florida and 5,160 (3,940–6,980) were on the east coast. These estimates and the associated uncertainty, in addition to being of immediate value to wildlife managers, are essential new data for incorporation into integrated population models and population viability analyses. We also provide context for interpreting the new estimates and perspectives for future modeling improvements.

Florida↗
USGS2018

Integrating encounter theory with decision analysis to evaluate collision risk and determine optimal protection zones for wildlife

1.Better understanding human‐wildlife interactions and their links with management can help improve the design of wildlife protection zones. One example is the problem of wildlife collisions with vehicles or human‐built structures (e.g. power lines, wind farms). In fact, collisions between marine wildlife and watercraft are among the major threats faced by several endangered species of marine mammals. Natural resource managers are therefore interested in finding cost‐effective solutions to mitigate these threats. 2.We combined abundance estimators with encounter rate theory to estimate relative lethal collision risk of the Florida manatee ( Trichechus manatus latirostris ) from watercraft. We first modeled seasonal abundance of watercraft and manatees using a Bayesian analysis of aerial survey count data. We then modeled relative lethal collision risk in space and across seasons. Finally, we applied decision analysis and Linear Integer Programming to determine the optimal design of speed zones in terms of relative risk to manatees and costs to waterway users. We used a Pareto efficient frontier approach to evaluate the performance of alternative zones, which included additional practical considerations (e.g. spatial aggregation of speed zones) in relation to the optimal zone configurations. 3.Under the various relationships for probability of death given strike speed that we considered, the current speed zones reduced the relative lethal collision risk by an average of 51.5% to 70% compared to the scenario in which all speed regulations were removed (i.e. the no‐protection scenario). We identified optimal zones and near‐optimal zones with additional management considerations that improved upon the current zones in terms of cost or relative risk. 4.Policy Implications : Our analytical framework combines encounter rate theory and decision analysis to quantify the effectiveness of speed zones protecting manatees while accounting for uncertainty. Our approach can be used to optimize the design of protection zones intended to reduce conflicts between human waterborne activity and marine mammals. This framework could be extended to address many other problems of human‐wildlife interactions, such as the optimal placement of wind farms to minimize collisions with wildlife or the optimal allocation of ranger effort to mitigate poaching threats.

Journal of Applied Ecology↗
USGS2018

Updated statewide abundance estimates for the Florida manatee

Knowing how many manatees live in Florida is critical for conservation and management of this threatened species. Martin et al. (2015) flew aerial surveys in 2011–2012 and estimated abundance in those years using advanced techniques that incorporated multiple data sources. We flew additional aerial surveys in 2015–2016 to count manatees and again applied advanced statistical techniques to estimate their abundance. We also made several methodological advances over the earlier work, including accounting for how sea state (water surface conditions) and synchronous surfacing behavior affect the availability of manatees to be detected and incorporating all parts of Florida in the area of inference. We estimate that the number of manatees in Florida in 2015–2016 was 8,810 (95% Bayesian credible interval 7,520–10,280), of which 4,810 (3,820–6,010) were on the west coast of Florida and 4,000 (3,240–4,910) were on the east coast. These estimates and associated uncertainty, in addition to being of immediate value to wildlife managers, are essential new data for incorporation into integrated population models and population viability analyses.

Florida↗
USGS2016

Influence of manatees' diving on their risk of collision with watercraft

Watercraft pose a threat to endangered Florida manatees ( Trichechus manatus latirostris ). Mortality from watercraft collisions has adversely impacted the manatee population&rsquo;s growth rate, therefore reducing this threat is an important management goal. To assess factors that contribute to the risk of watercraft strikes to manatees, we studied the diving behavior of nine manatees carrying GPS tags and time&ndash;depth recorders in Tampa Bay, Florida, during winters 2002&ndash;2006. We applied a Bayesian formulation of generalized linear mixed models to depth data to model the probability ( P t ) that manatees would be no deeper than 1.25 m from the water&rsquo;s surface as a function of behavioral and habitat covariates. Manatees above this threshold were considered to be within striking depth of a watercraft. Seventy-eight percent of depth records (individual range 62&ndash;86%) were within striking depth (mean = 1.09 m, max = 16.20 m), illustrating how vulnerable manatees are to strikes. In some circumstances manatees made consecutive dives to the bottom while traveling, even in areas >14 m, possibly to conserve energy. This is the first documentation of potential cost-efficient diving behavior in manatees. Manatees were at higher risk of being within striking depth in shallow water (<0.91 m), over seagrass, at night, and while stationary or moving slowly; they were less likely to be within striking depth when &le;50 m from a charted waterway. In shallow water the probability of a manatee being within striking depth was 0.96 (CI = 0.93&ndash;0.98) and decreased as water depth increased. The probability was greater over seagrass ( P t = 0.96, CI = 0.93&ndash;0.98) than over other substrates ( P t = 0.73, CI = 0.58&ndash;0.84). Quantitative approaches to assessing risk can improve the effectiveness of manatee conservation measures by helping identify areas for protection.

Florida↗
USGS2013

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

Ecology↗
USGS2011

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost‐effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees ( Trichechus manatus latirostris ) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non‐independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non‐independent detection of individuals. We simulated correlated behaviour using beta‐binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta‐binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta‐binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness‐of‐fit procedure to evaluate the fit of beta‐binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta‐binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta‐binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness‐of‐fit when analysing ecological data with N‐mixture models. Indeed, both the simulations and the goodness‐of‐fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non‐independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N‐mixture models to ecological data.

Methods in Ecology and Evolution↗