Geology topics

Dana J. Morin

Publications and source records attributed to Dana J. Morin.

10 recordsLinked to original sources

USGS2025

Spatial close-kin mark-recapture models applied to terrestrial species with continuous natal dispersal

Close-kin mark–recapture (CKMR) methods use information on genetic relatedness among individuals to estimate demographic parameters. An individual's genotype can be considered a ‘recapture’ of each of its parent's genotype, and the frequency of kin-pair matches detected in a population sample can directly inform estimates of abundance. CKMR inference procedures require analysts to define kinship probabilities in functional forms, which inevitably involve simplifying assumptions. Among others, population structure can have a strong influence on how kinship probabilities are formulated. Many terrestrial species are philopatric or face barriers to dispersal, and not accounting for dispersal limitation in kinship probabilities, can create substantial bias if sampling is also spatially structured (e.g. via harvest). We present a spatially explicit formulation of CKMR that corrects for incomplete mixing by incorporating natal dispersal distances and spatial distribution of individuals into the kinship probabilities. We used individual-based simulations to evaluate the accuracy of abundance estimates obtained with one spatially naïve and two spatially explicit CKMR models across six scenarios with distinct spatial patterns of relative abundance and sampling probability. Estimates of abundance obtained with a CKMR model naïve to spatial structure were negatively biased when sampling was spatially biased. Incorporating patterns of natal dispersal in the kinship probabilities helped address this bias, but estimates were not always accurate depending on the model used and the scenario considered. Incorporating natal dispersal into spatially structured CKMR models can address the bias created by population structure and heterogeneous sampling but will often require additional assumptions and auxiliary data (e.g. relative abundance indices). The models shown here were designed for terrestrial species with continuous patterns of natal dispersal and high year-to-year site fidelity but could be extended to other species.

Methods in Ecology and Evolution↗

USGS2024

No evidence hunting bait increases American black bear population growth in Maine, USA

American black bear ( Ursus americanus ) populations are increasing throughout much of North America. Use of multiple harvest methods, including hunting over bait, is intended to increase harvest success rates to meet harvest and population management objectives. However, black bear population growth can be influenced by food availability, and some speculate the use of bait may inadvertently increase bear abundance through food resource supplementation. We collected hair from black bears captured from 2005–2019 and a selection of potential food items, including those used as bait from 3 study areas in Maine, USA, with different levels of human influence. We used stable isotope analysis of the black bear hair and food item samples to evaluate the contribution of different identified food groups to the diet of individual bears. We found no evidence that greater contribution of human food resources, including bait, increased the number of cubs per litter for reproductive females or that human foods comprised a greater part of the diet in years when natural food resources were less abundant. Approximately 69% of black bear harvest occurred over bait. We found the probability of reproductive-age females to be harvested increased with average representation of human foods with high carbon enrichment (including bait) in their diet, but the relationship was weak, likely because of sampling constraints. Additionally, the probability of being harvested was greater in years when natural food resources were scarce. We conclude that bait is not available on the landscape in large enough amounts or for a sufficient amount of time in Maine to substantially influence female reproduction. Our results indicate hunting over bait is an effective tool in a state that is attempting to maintain a stable bear population via harvest, and dispels conjecture that hunting over bait may increase cub production and offset the intention of harvest.

Maine↗

USGS2024

Sources of bias in applying close-kin mark–recapture to terrestrial game species with different life histories

Close-kin mark–recapture (CKMR) is a method analogous to traditional mark–recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species’ and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent–offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Ecology↗

USGS2022

Comparison of methods for estimating density and population trends for low-density Asian bears

Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.

Global Ecology and Conservation↗

USGS2022

The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears

Designing a population monitoring program for Asian bears presents challenges associated with their low densities and detectability, generally large home ranges, and logistical or resource constraints. The use of an occupancy-based method to monitor bear populations can be appropriate under certain conditions given the mechanistic relationship between occupancy and abundance. The form of the occupancy–abundance relationship is dependent on species-specific characteristics such as home range size and population density, as well as study area size. To assess the statistical power of tests to detect population change of Asian bears, we conducted a study using a range of scenarios by simulating spatially explicit individual-based capture-recapture data from a demographically open model. Simulations assessed the power to detect changes in population density via changes in site-level occupancy or abundance through time, estimated using a standard occupancy model or a Royle-Nichols model, both with point detectors (representing camera traps). We used IUCN Red List criteria as a guide in selection of two population decline scenarios (20% and 50%), but we chose a shorter time horizon (10 years = 1 bear generation), meaning that declines were steeper than used for IUCN criteria (3 generations). Our simulations detected population declines of 50% with high power (>0.80) and low false positive rates (FPR: incorrectly detecting a decline) (<0.10) when detectors were spaced at > 0.67 times the home range diameter (home-range spacing ratio: HRSR, a measure of spatial correlation), such that bears would tend to overlap no more than two detectors. There was high (0.85) correlation between realized occupancy and N in these scenarios. The FPR increased as the HRSR decreased because of spatial correlation in the occupancy process induced when individual home ranges overlap multiple detectors. The mean statistical power to detect more gradual population declines (20% in 10 years) with HRSR > 0.67 was low for occupancy models 0.22 (maximum power 0.67) and Royle-Nichols models (0.24; maximum power 0.67), suggesting that declines of this magnitude may not be described reliably with 10 years of monitoring. Our results demonstrated that under many realistic scenarios that we explored, false positive rates were unacceptably high. We highlight that when designing occupancy studies, the spacing between point detectors be at least 0.67 times the diameter of the home range size of the larger sex (e.g., males) when the assumptions of the spatial capture-recapture model used for simulation are met.

Global Ecology and Conservation↗

USGS2022

The need to step-up monitoring of Asian bears

Many wildlife species are threatened in Asia, including the five species of terrestrial bears (Asiatic black, Ursus thibetanus ; brown, U. arctos ; sloth, Melursus ursinus ; sun, Helarctos malayanus ; giant panda, Ailuropoda melanoleuca ): many populations of these bears are thought to be declining or imperiled by small population size. Here our aim is to document how population assessments have been conducted for bears in Asia. We searched the literature and identified 102 studies published during 1999–2021 that investigated the status of an Asian bear population; these occurred in 24 of the 32 bear range countries in Asia. At the most basic level, 11% of studies verified presence of bears in places where they were not known to exist. The most common objective (53% of papers) was a distribution map, often derived from presence locations in a habitat-based model. Occupancy studies (15%) used temporal (time stamps on images from cameras) or spatial (transect segments) replicates, but tended to focus on “use”, so detector spacing was sometimes not appropriate for occupancy. Purported population indices, such as sign density or camera trap encounter rates, were reported in 16% of studies. One third of studies provided a population estimate, but only 10 studies in two decades used a rigorous method (e.g., mark–recapture). Sign surveys and interviews were the most common methods for determining bear presence, and local interviews were heavily relied upon for assessing population trend. Camera trapping has become increasingly prevalent, but only one study obtained a population estimate using photographs to distinguish natural individual markings. Only three studies used hair traps to obtain DNA-based population estimates, and three other studies obtained population estimates from DNA in scats. Just three studies quantitatively measured change in population size or occupancy over time, and none of these showed a decline. Unique rangewide sign surveys of giant pandas showed significant geographic expansion. The opinions of experts and local people, now heavily relied upon for population assessments, are not reliable or sensitive enough for monitoring. Quantitative population assessments are desirable to direct conservation actions toward the most perilous situations, and provide a means to gauge the effectiveness of conservation actions. This paper demonstrates the paucity of rigorous monitoring of Asian bears, and leads off a series of papers that propose improved methods for assessing distribution, occupancy, and density

Global Ecology and Conservation↗

USGS2020

Is your ad hoc model selection strategy affecting your multimodel inference?

(Yackulic) 1. Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. 2. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models. 3. We tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies. 4. Sequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages. The structure of non-target sub-models was less important. 5. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

Ecosphere↗

USGS2017

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Ecosphere↗

USGS2017

Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador

The Andean bear ( Tremarctos ornatus ) is the only extant species of bear in South America and is considered threatened across its range and endangered in Ecuador. Habitat loss and fragmentation is considered a critical threat to the species, and there is a lack of knowledge regarding its distribution and abundance. The species is thought to occur at low densities, making field studies designed to estimate abundance or density challenging. We conducted a pilot camera-trap study to estimate Andean bear density in a recently identified population of Andean bears northwest of Quito, Ecuador, during 2012. We compared 12 candidate spatial capture–recapture models including covariates on encounter probability and density and estimated a density of 7.45 bears/100 km 2 within the region. In addition, we estimated that approximately 40 bears used a recently named Andean bear corridor established by the Secretary of Environment, and we produced a density map for this area. Use of a rub-post with vanilla scent attractant allowed us to capture numerous photographs for each event, improving our ability to identify individual bears by unique facial markings. This study provides the first empirically derived density estimate for Andean bears in Ecuador and should provide direction for future landscape-scale studies interested in conservation initiatives requiring spatially explicit estimates of density.

Ursus↗

USGS2017

Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to realworld size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.

Conference Paper↗