A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

Riecke, Thomas V.; Sedinger, Benjamin S.; Arnold, Todd W.; Gibson, Daniel D.; Koons, David N.; Lohman, M.; Schaub, Michael; Williams, Perry J.; Sedinger, James S.

doi:10.1111/1365-2656.13747

Cited by 12 publications

(21 citation statements)

References 75 publications

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“…We urge development of novel, biologically plausible model structures (e.g. Cubaynes et al, 2012; Gimenez et al, 2012; Riecke, Sedinger, et al, 2022; van de Pol & Brouwer, 2021) to inform waterfowl harvest management in North America, and elsewhere.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, we modelled hunting mortality hazard rate (

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

\log ()(, h_{κ, t}) goodbreak= α_{1} goodbreak+ α_{2} goodbreak\times z ()(, n_{t}) goodbreak+ α_{3} goodbreak\times z ()(, H_{t}) goodbreak+ ϵ_{t},

where mortality hazard rates are the instantaneous intensity of mortality events integrated over the exposure interval (Ergon et al, 2018). We hypothesized that natural mortality hazard rate would increase with increases in teal abundance, while fecundity would decline, and we hypothesized that both natural mortality and fecundity would increase with pond abundance (Riecke, Sedinger, et al, 2022). Thus, we modelled natural mortality hazard rate (

h_{η, t}

) and fecundity (

ξ_{t}

) as a function of z‐standardized breeding pair abundance, z( n ), the z ‐standardized number of ponds, z ( p ), and random temporal variation,

\begin{matrix} lefttrue \\ \log (h_{η, t}) = β_{1} + β_{2} \times z (n_{t + 1}) + β_{3} \times z (p_{t}) + ω_{t}, \\ \log (ξ_{t}) = γ_{1} + γ_{2} \times z (<...) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Teal hunting primarily occurs in September and October (Devink et al, 2013), while natural mortality of adult female ducks primarily occurs during the breeding season (Arnold et al, 2012; Dufour & Clark, 2002; Hoekman et al, 2002; Riecke, Sedinger, et al, 2022). Thus, we modelled natural mortality probability as conditional on having survived previous hunting mortality,

\begin{matrix} lefttrue \\ κ_{t} = 1 - e^{- h_{κ, t}}, \\ η_{t} = (1 - κ_{t}) (1 - e^{- h_{η, t}}) . \end{matrix}

To model the band‐recovery data, we built modified band‐recovery models following Brownie and Pollock (1985), Kéry and Schaub (2012), Ergon et al (2018), Nater et al (2020) and Riecke, Sedinger, et al (2022), where recoveries of previously marked adult females occurred as a function of annual survival (

s_{t}

) and band‐recovery (

f_{t}

) probabilities. We estimated annual band‐recovery probability as a function of hunting mortality probability (

κ

), band‐reporting probability (

ρ

), and the probability that birds killed by hunting are not retrieved, that is, crippling loss probability (

c

).…”

Section: Methodsmentioning

confidence: 99%

“…We modelled changes in pond abundance from year to year as an auto‐regressive process,

p_{t + 1} \sim Normal ()(,, p_{t}, σ_{p}^{2})

. We modelled changes in teal abundance using an integrated population model (Figure 2; Schaub & Kéry, 2022), where the population in the next year (

n_{t + 1}

; Table 1) was a function of the population in the previous year (

n_{t}

), natural mortality during the previous year (

η_{t}

), hunting mortality (

κ_{t + 1}

) between the breeding seasons, and the mean number of female recruits produced per capita during the previous breeding season (

ξ_{t}

n_{t + 1} goodbreak= n_{t} ()(, 1 goodbreak- κ_{t + 1} goodbreak- η_{t}) goodbreak+ n_{t} ξ_{t} .

We hypothesized that increases in hunter numbers would increase the hunting mortality hazard rate and increases in teal abundance would reduce hunting mortality hazard rate as each individual would be less susceptible to harvest (Riecke, Sedinger, et al, 2022). Thus, we modelled hunting mortality hazard rate (

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

…”

Section: Methodsmentioning

confidence: 99%

“…The relationship between anthropogenic harvest and population growth rates of wild organisms is of major interest to population managers and resource consumers (Nichols et al, 1995 ; Péron, 2013 ; Sedinger & Herzog, 2012 ; Servanty et al, 2011 ) and has received considerable attention and debate (Anderson & Burnham, 1976 ; Burnham et al, 1984 ; Lebreton, 2005 ; Nichols et al, 1995 ; Péron, 2013 ; Riecke, Sedinger, et al, 2022 ). Overexploitation can catastrophically impact wildlife populations (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Riecke

Lohman

Sedinger

et al. 2022

Journal of Animal Ecology

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Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters. Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long‐term data (1973–2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue‐winged teal Spatula discors, an abundant North American dabbling duck with a relatively fast‐paced life history strategy. Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly (<0.02), and natural mortality probability increased substantially (>0.1) at greater population densities. We demonstrate strong density‐dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory mortality due to multicollinearity between abundance, natural mortality and hunting mortality. Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally.

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Section: Discussionmentioning

confidence: 99%

“…Thus, we modelled hunting mortality hazard rate (

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

\log ()(, h_{κ, t}) goodbreak= α_{1} goodbreak+ α_{2} goodbreak\times z ()(, n_{t}) goodbreak+ α_{3} goodbreak\times z ()(, H_{t}) goodbreak+ ϵ_{t},

h_{η, t}

) and fecundity (

ξ_{t}

) as a function of z‐standardized breeding pair abundance, z( n ), the z ‐standardized number of ponds, z ( p ), and random temporal variation,

\begin{matrix} lefttrue \\ \log (h_{η, t}) = β_{1} + β_{2} \times z (n_{t + 1}) + β_{3} \times z (p_{t}) + ω_{t}, \\ \log (ξ_{t}) = γ_{1} + γ_{2} \times z (<...) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

\begin{matrix} lefttrue \\ κ_{t} = 1 - e^{- h_{κ, t}}, \\ η_{t} = (1 - κ_{t}) (1 - e^{- h_{η, t}}) . \end{matrix}

s_{t}

) and band‐recovery (

f_{t}

) probabilities. We estimated annual band‐recovery probability as a function of hunting mortality probability (

κ

), band‐reporting probability (

ρ

), and the probability that birds killed by hunting are not retrieved, that is, crippling loss probability (

c

).…”

Section: Methodsmentioning

confidence: 99%

“…We modelled changes in pond abundance from year to year as an auto‐regressive process,

p_{t + 1} \sim Normal ()(,, p_{t}, σ_{p}^{2})

. We modelled changes in teal abundance using an integrated population model (Figure 2; Schaub & Kéry, 2022), where the population in the next year (

n_{t + 1}

; Table 1) was a function of the population in the previous year (

n_{t}

), natural mortality during the previous year (

η_{t}

), hunting mortality (

κ_{t + 1}

) between the breeding seasons, and the mean number of female recruits produced per capita during the previous breeding season (

ξ_{t}

n_{t + 1} goodbreak= n_{t} ()(, 1 goodbreak- κ_{t + 1} goodbreak- η_{t}) goodbreak+ n_{t} ξ_{t} .

h_{κ, t}

, Ergon et al, 2018, Nater et al, 2020) as a function of z ‐standardized

()(, z ()(, x_{i}) = \frac{x_{i} - \overset{x}{true}}{sd ()(, x)})

breeding pair abundance, z ( n ), the z ‐standardized number of hunters, z ( H ), and random temporal variation (

ϵ

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Riecke

Lohman

Sedinger

et al. 2022

Journal of Animal Ecology

Self Cite

View full text Add to dashboard Cite

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Human access constrains optimal foraging and habitat availability in an avian generalist

Masto,

Blake‐Bradshaw,

Highway

et al. 2024

Ecological Applications

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Animals balance costs of antipredator behaviors with resource acquisition to minimize hunting and other mortality risks and maximize their physiological condition. This inherent trade‐off between forage abundance, its quality, and mortality risk is intensified in human‐dominated landscapes because fragmentation, habitat loss, and degradation of natural vegetation communities is often coupled with artificially enhanced vegetation (i.e., food plots), creating high‐risk, high‐reward resource selection decisions. Our goal was to evaluate autumn–winter resource selection trade‐offs for an intensively hunted avian generalist. We hypothesized human access was a reliable cue for hunting predation risk. Therefore, we predicted resource selection patterns would be spatiotemporally dependent upon levels of access and associated perceived risk. Specifically, we evaluated resource selection of local‐scale flights between diel periods for 426 mallards (Anas platyrhynchos) relative to wetland type, forage quality, and differing levels of human access across hunting and nonhunting seasons. Mallards selected areas that prohibited human access and generally avoided areas that allowed access diurnally, especially during the hunting season. Mallards compensated by selecting for high‐energy and greater quality foraging patches on allowable human access areas nocturnally when they were devoid of hunters. Postseason selection across human access gradients did not return to prehunting levels immediately, perhaps suggesting a delayed response to reacclimate to nonhunted activities and thus agreeing with the assessment mismatch hypothesis. Last, wetland availability and human access constrained selection for optimal natural forage quality (i.e., seed biomass and forage productivity) diurnally during preseason and hunting season, respectively; however, mallards were freed from these constraints nocturnally during hunting season and postseason periods. Our results suggest risk‐avoidance of human accessible (i.e., hunted) areas is a primary driver of resource selection behaviors by mallards and could be a local to landscape‐level process influencing distributions, instead of forage abundance and quality, which has long‐been assumed by waterfowl conservation planners in North America. Broadly, even an avian generalist, well adapted to anthropogenic landscapes, avoids areas where hunting and human access are allowed. Future conservation planning and implementation must consider management for recreational access (i.e., people) equally important as foraging habitat management for wintering waterfowl.

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Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time

Weegman,

Devries,

Clark

et al. 2024

Ecological Applications

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Knowledge of interspecific and spatiotemporal variation in demography–environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random‐effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue‐winged teal [Spatula discors], gadwall [Mareca strepera], green‐winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross‐seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961–2019 and generally declined through time in blue‐winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green‐winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross‐seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross‐seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working‐lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

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A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

Cited by 12 publications

References 75 publications

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Density‐dependence produces spurious relationships among demographic parameters in a harvested species

Human access constrains optimal foraging and habitat availability in an avian generalist

Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time

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