Abstract
AbstractSurvival probability is fundamental for understanding population dynamics. Methods for estimating survival probability from field data typically require marking individuals, but marking methods are not possible for arthropod species that molt their exoskeleton between life stages. We developed a novel Bayesian state‐space model to estimate arthropod larval survival probability from stage‐structured count data. We performed simulation studies to evaluate estimation bias due to detection probability, individual variation in stage duration, and study design (sampling frequency and sample size). Estimation of cumulative survival probability from oviposition to pupation was robust to potential sources of bias. Our simulations also provide guidance for designing field studies with minimal bias. We applied the model to the monarch butterfly (Danaus plexippus), a declining species in North America for which conservation programs are being implemented. We estimated cumulative survival from egg to pupation from monarch counts conducted at 18 field sites in three landcover types in Iowa, USA, and Ontario, Canada: road right‐of‐ways, natural habitats (gardens and restored meadows), and agricultural field borders. Mean predicted survival probability across all landcover types was 0.014 (95% CI: 0.004–0.024), four times lower than previously published estimates using an ad hoc estimator. Estimated survival probability ranged from 0.002 (95% CI: 7.0E−7 to 0.034) to 0.058 (95% CI: 0.013–0.113) at individual sites. Among landcover types, agricultural field borders in Ontario had the highest estimated survival probability (0.025 with 95% CI: 0.008–0.043) and natural areas had the lowest estimated survival probability (0.008 with 95% CI: 0.009–0.024). Monarch production was estimated as adults produced per milkweed stem by multiplying survival probabilities by eggs per milkweed at these sites. Monarch production ranged from 1.0 (standard deviation [SD] = 0.68) adult in Ontario natural areas in 2016 to 29.0 (SD = 10.42) adults in Ontario agricultural borders in 2015 per 6809 milkweed stems. Survival estimates are critical to monarch population modeling and habitat restoration efforts. Our model is a significant advance in estimating survival probability for monarch butterflies and can be readily adapted to other arthropod species with stage‐structured life histories.
Highlights
Survival probability is a fundamental demographic vital rate that is an important parameter in population models used to support research, management, and conservation
We present a new Bayesian state-space model to estimate survival probabilities from field counts of stage-structured arthropod populations
The model takes advantage of known stage durations, which can be estimated with ancillary degree-day information, and does not require large sample sizes
Summary
Survival probability is a fundamental demographic vital rate that is an important parameter in population models used to support research, management, and conservation. Survival probabilities are needed to parameterize stage-based matrix population models (Caswell 2001), to identify risks to population viability (Crouse et al 1987), and to propose actions to reach management objectives (Crowder et al 1994). Survival probability estimation for arthropods presents unique field sampling and statistical challenges. Frequentist (non-Bayesian) statistical methods may have difficulty with numerical estimation of very small probabilities, especially when sample sizes are small (King et al 2009). Innovative Bayesian analytical approaches that can use field counts of unmarked individuals are urgently needed, given the number of arthropods at risk of extinction (Dirzo et al 2014)
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