Geology topics

Nathan J. Crum

Publications and source records attributed to Nathan J. Crum.

3 recordsLinked to original sources

USGS2025

Unobserved individual and population level impacts of fishing gear entanglements on North Atlantic right whales

Fishing gear entanglements can compromise health and lower survival and reproductive output of wildlife, which can slow population growth or cause population declines. However, entanglements may go unobserved, making it difficult to quantify their effects on individuals' vital rates and a population's trajectory. Fishing gear entanglements are a leading cause of death for North Atlantic right whales, Eubalaena glacialis , an endangered species whose population has declined substantially over the last decade. Key sources of observation error have not been addressed when quantifying the effects of entanglement on this species. We formulated a hidden Markov model that integrated 28 years of photo identification records, visual health assessments, entanglement assessments, and hormone assays to estimate body condition dynamics, and entanglement, survival, and reproductive rates of female North Atlantic right whales. The model also accounted for observation errors, including entanglements and reproductive events that were unobserved. Whales entangled in fishing gear were drastically more likely to experience declines in body condition and had lower survival rates than whales that were not entangled. Additionally, only whales in good body condition became pregnant. Between 1994 and 2021 entanglements reduced the expected number of calves born by 12.9% (95% credible interval: 8.2%–19.6%) and the expected number of females alive at the end of 2021 by 18.5% (95% credible interval: 8.6%–29.4%). Fishing gear entanglements have reduced the North Atlantic right whale population's capacity to recover and have increased the importance of the health and survival of the remaining living whales. Therefore, reducing the risk of fishing gear entanglements is likely necessary for the population to recover.

Animal Conservation↗

USGS2019

Quantifying risk of whale–vessel collisions across space, time, and management policies

Transportation industries can negatively impact wildlife populations, including through increased risk of mortality. To mitigate this risk successfully, managers and conservationists must estimate risk across space, time, and alternative management policies. Evaluating this risk at fine spatial and temporal scales can be challenging, especially in systems where wildlife–vehicle collisions are rare or imperfectly detected. The sizes and behaviors of wildlife and vehicles influence collision risk, as well as how much they co‐occur in space and time. We applied a modeling framework based on encounter theory to quantify the risk of lethal collisions between endangered North Atlantic right whales and vessels. Using Automatic Identification System vessel traffic data and spatially explicit estimates of right whale abundance that account for imperfect detection, we modeled risk at fine spatiotemporal scales before and after implementation of a vessel speed rule in the southeastern United States. The expected seasonal mortality rates of right whales decreased by 22% on average after the speed rule was implemented, indicating that the rule is effective at reducing lethal collisions. The rule's effect on risk was greatest where right whales were abundant and vessel traffic was heavy, and its effect varied considerably across time and space. Our framework is spatiotemporally flexible, process‐oriented, computationally efficient and accounts for uncertainty, making it an ideal approach for evaluating many wildlife management policies, including those regarding collisions between wildlife and vehicles and cases in which wildlife may encounter other dangerous features such as wind farms, seismic surveys, or fishing gear.

Georgia, Florida↗

USGS2017

Estimating occupancy probability of moose using hunter survey data

Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose ( Alces alces ) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer ( Odocoileus virginianus ) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.

New York↗