Time series sightability modeling of animal populations

ArchMiller, Althea A.; Dorazio, Robert M.; Clair, Katherine St.; Fieberg, John

doi:10.1371/journal.pone.0190706

Cited by 10 publications

(16 citation statements)

References 25 publications

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“…Estimated annual survival in all 4 years using collared and uncollared calves were similar (within 95% CIs) and closely match rates estimated by Lenarz et al () in the same study area during 2002–2008, before the declining population trend was indicated by the MNDNR's annual survey (Giudice et al , ArchMiller et al , DelGiudice ). Our observed rates approach those reported in northern New Hampshire, northeastern Alberta, and western Interior Alaska (0.29–0.45; Hauge and Keith , Musante et al , Keech et al ) but were 46% lower than rates observed in Ontario (0.64; Patterson et al ).…”

Section: Discussionsupporting

confidence: 83%

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Survival and cause‐specific mortality of moose calves in Northeastern Minnesota

et al. 2019

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Ungulate reproductive success (calf production and survival) influences population performance. The moose (Alces alces) population in northeastern Minnesota, USA, has declined 65% from 2006 to 2018 but has begun to stabilize. Because causes of this decline were largely unknown, we investigated production, survival, and cause‐specific mortality of calves of the global positioning system (GPS)‐collared females in this population. In 2013 and 2014, we GPS‐collared 74 neonates and monitored them for survival. In 2015 and 2016, we monitored 50 and 35 calving females for signs of neonatal mortality using changes in adult female velocities and assessed seasonal calf survival by aerial surveys. In 2013 and 2014 (pooled), survival to 9 months was 0.34 (95% CI = 0.23–0.52) for collared calves, and in 2015 and 2016 (pooled) survival was 0.35 (95% CI = 0.26–0.48) for uncollared calves. Mortality in all 4 years was high during the first 50 days of life. In 2013 and 2014 (pooled), calving sites were relatively safe for collared neonates; predator‐kills occurred a median 17.0 days after departure and a median 1,142 m from calving sites. Predation was the leading cause of death of collared calves (84% of mortalities), with wolves (Canis lupus) accounting for 77% of these. Other forms of mortality for collared and uncollared calves included drowning, infection, vehicle collision, and natural abandonment. We documented higher wolf predation than other recent studies with similar predator communities. Identifying specific causes of calf mortality and understanding their relations to various landscape characteristics and other extrinsic factors should yield insight into mechanisms contributing to the declining moose population in northeastern Minnesota and serve as a basis for ecologically sound management responses. © 2019 The Wildlife Society.

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Section: Discussionsupporting

confidence: 83%

“…Annual adult survival, 81−88% during the same interval, was moderately depressed and less variable (Carstensen et al ). These demographic rates likely account for the poor performance, but recent stability, of this depressed population (ArchMiller et al , DelGiudice ).…”

Section: Discussionmentioning

confidence: 99%

Survival and cause‐specific mortality of moose calves in Northeastern Minnesota

et al. 2019

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“…Several moose populations along the southern geographic extent of their range have exhibited declines in abundance and/or compromised health (Dou, Jiang, Stott, & Piao, 2013;Grøtan, Saether, Lillegård, Solberg, & Engen, 2009;Monteith et al, 2015;Murray et al, 2006;Ruprecht et al, 2016), while others appear to be healthy (Brimeyer & Thomas, 2004;Murray et al, 2012;Wattles, Zeller, & DeStefano, 2018). In northeastern Minnesota (NEMN), which is high-quality moose habitat, moose declined 50% in abundance over the last decade (2005DelGiudice, 2016;ArchMiller, Dorazio, St. Clair, & Fieberg, 2018). A growing wolf population that has benefited from legal protection and the seasonal availability of deer is a primary driver in the decline of moose in NEMN (Barber-Meyer & Mech, 2016;Mech & Fieberg, 2014;Mech, Fieberg, & Barber-Meyer, 2018).…”

Section: Introductionmentioning

confidence: 99%

Moose movement rates are altered by wolf presence in two ecosystems

Ditmer

Fieberg

Moen

et al. 2018

Ecology and Evolution

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Predators directly impact prey populations through lethal encounters, but understanding nonlethal, indirect effects is also critical because foraging animals often face trade‐offs between predator avoidance and energy intake. Quantifying these indirect effects can be difficult even when it is possible to monitor individuals that regularly interact. Our goal was to understand how movement and resource selection of a predator (wolves; Canis lupus) influence the movement behavior of a prey species (moose; Alces alces). We tested whether moose avoided areas with high predicted wolf resource use in two study areas with differing prey compositions, whether avoidance patterns varied seasonally, and whether daily activity budgets of moose and wolves aligned temporally. We deployed GPS collars on both species at two sites in northern Minnesota. We created seasonal resource selection functions (RSF) for wolves and modeled the relationship between moose first‐passage time (FPT), a method that discerns alterations in movement rates, and wolf RSF values. Larger FPT values suggest rest/foraging, whereas shorter FPT values indicate travel/fleeing. We found that the movements of moose and wolves peaked at similar times of day in both study areas. Moose FPTs were 45% lower in areas most selected for by wolves relative to those avoided. The relationship between wolf RSF and moose FPT was nonlinear and varied seasonally. Differences in FPT between low and high RSF values were greatest in winter (−82.1%) and spring (−57.6%) in northeastern Minnesota and similar for all seasons in the Voyageurs National Park ecosystem. In northeastern Minnesota, where moose comprise a larger percentage of wolf diet, the relationship between moose FPT and wolf RSF was more pronounced (ave. across seasons: −60.1%) than the Voyageurs National Park ecosystem (−30.4%). These findings highlight the role wolves can play in determining moose behavior, whereby moose spend less time in areas with higher predicted likelihood of wolf resource selection.

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“…The survey provides estimates of abundance (including 90% confidence intervals [CI]), percent calves, calf:cow and bull:cow ratios, and percent cows observed with twins. A sightability model corrects for visual obstruction and is used to adjust abundance (ArchMiller et al, ; Fieberg, ; Giudice, Fieberg, & Lenarz, ; Steinhorst & Samuel, ), but raw data, adjusted for sampling, are used to calculate other metrics using the combined ratio estimator (Cochran, ). The sightability model was based on radiocollared moose during 2004–2007 (Lenarz et al, , ); logistic regression indicated visual obstruction was the most influential covariate in moose detection, so visual obstruction (proportion obstructed by vegetation) is estimated within a ~10‐m radius around the first observed moose in a group (Giudice et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Assessing ecology and management of wildlife requires knowledge of population trends over time. Surveys can reveal trends but their effectiveness to do so depends on their precision and accuracy (ArchMiller, Dorazio, St. Clair, & Fieberg, ). Examining trend data can be used to estimate a population's demographic rates, which can otherwise be costly and invasive to obtain through tracking of a representative sample of individuals.…”

Section: Introductionmentioning

confidence: 99%

Comparing survey and multiple recruitment–mortality models to assess growth rates and population projections

Severud

DelGiudice

Bump

2019

Ecology and Evolution

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Estimation of population trends and demographic parameters is important to our understanding of fundamental ecology and species management, yet these data are often difficult to obtain without the use of data from population surveys or marking animals. The northeastern Minnesota moose (Alces alces Linnaeus, 1758) population declined 58% during 2006–2017, yet aerial surveys indicated stability during 2012–2017. In response to the decline, the Minnesota Department of Natural Resources (MNDNR) initiated studies of adult and calf survival to better understand cause‐specific mortality, calf recruitment, and factors influencing the population trajectory. We estimated population growth rate (λ) using adult survival and calf recruitment data from demographic studies and the recruitment–mortality (R‐M) Equation and compared these estimates to those calculated using data from aerial surveys. We then projected population dynamics 50 years using each resulting λ and used a stochastic model to project population dynamics 30 years using data from the MNDNR's studies. Calculations of λ derived from 2012 to 2017 survey data, and the R‐M Equation indicated growth (1.02 ± 0.16 [SE] and 1.01 ± 0.04, respectively). However, the stochastic model indicated a decline in the population over 30 years (λ = 0.91 ± 0.004; 2014–2044). The R‐M Equation has utility for estimating λ, and the supporting information from demographic collaring studies also helps to better address management questions. Furthermore, estimates of λ calculated using collaring data were more certain and reflective of current conditions. Long‐term monitoring using collars would better inform population performance predictions and demographic responses to environmental variability.

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Time series sightability modeling of animal populations

Cited by 10 publications

References 25 publications

Survival and cause‐specific mortality of moose calves in Northeastern Minnesota

Survival and cause‐specific mortality of moose calves in Northeastern Minnesota

Moose movement rates are altered by wolf presence in two ecosystems

Comparing survey and multiple recruitment–mortality models to assess growth rates and population projections

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