Measuring the additive effects of predation on prey survival across spatial scales

Abstract

The degree to which predation is an additive vs. compensatory source of mortality is fundamental to understanding the effects of predation on prey populations and evaluating the efficacy of predator management actions. In the Columbia River basin, USA, predation by Caspian Terns (Hydroprogne caspia) on U.S. Endangered Species Act (ESA)listed juvenile salmonids (smolts; Oncorhynchus spp.) has led to predator management actions to reduce predation; however, the assumption that reduced predation translates into greater salmonid survival, either within the life stage where predation occurs or across their lifetime, has remained untested. To address this critical uncertainty, we analyzed a long-term (2008-2018) mark-recapture-recovery data set of ESA-listed steelhead trout (O. mykiss) that were tagged (n = 78,409) and subsequently exposed to predation during smolt out-migration through multiple river reaches (spatial scales), jointly estimating weekly probabilities of steelhead survival, mortality due to bird predation, and mortality due to other causes. This concurrent estimation across time-stratified cohorts allowed for the direct measurement of the strength, magnitude, and direction of relationships between survival and Caspian Tern predation. Estimates of Tern predation on steelhead were substantial in most years, with cumulative annual estimates ranging from 0.075 (95% creditable interval = 0.058-0.099) to 0.375 (0.290-0.461). Increases in Tern predation probabilities were associated with statistically significant decreases in steelhead survival probabilities in all evaluated years and salmonid life stages (smolt out-migration and smolt-to-adult returns). Results provide novel evidence that predation by Caspian Terns may have been a super-additive source of mortality during the smolt life stage and a partially additive source of mortality to the adult life stage. Annual estimates of the difference between observed survival and baseline survival (i.e., in the absence of Tern predation) ranged from 0.052 (0.017-0.103) to 0.314 (0.172-0.459) during the steelhead smolt life stage and from 0.011 (0.001-0.029) to 0.049 (0.025-0.078) to the adult life stage. The estimated levels of compensation have important implications for predator management actions aimed at increasing the survival of endangered salmonids, and the modeling approach developed herein provides a framework to directly quantify the impacts of source-specific mortality factors on prey populations.

Predation probabilities and functional responses: How piscivorous waterbirds respond to pulses in fish abundance

“…Understanding predator‐specific responses can guide system‐specific management actions focused on local (e.g., areas of prey concentrations) and landscape‐level (e.g., inland vs. estuary) factors. Quantifying how predators respond to pulses in prey availability has direct implications for predator–prey dynamics and foraging ecology, while also providing critical information to identify predator‐ and location‐specific predation impacts (Osterback et al, 2013; Payton et al, 2020; Roby et al, 2002), which can in turn guide targeted‐ and system‐specific actions aimed at prey populations of conservation concern. Together, these results illustrate the complexity of predator–prey interactions in natural systems and highlight the need to jointly investigate predator and prey abundance, predation probabilities, per capita predation probabilities, and predator foraging strategies to understand processes affecting complex communities of predators and their prey.…”

“…Previous studies also quantified bird species‐specific tag deposition probabilities at tern, cormorant, and gull colonies in the Columbia River basin (Hostetter et al, 2015). To account for detection and deposition probabilities herein, we used informative priors derived from these previous studies (Hostetter et al, 2015; Payton et al, 2020) to inform detection probabilities ($$$ {p}_{jk} $$$) and predator‐specific deposition probabilities ($$$ \uppsi $$$; see Appendices S1–S3).…”

“…Predation probabilities ($$$ {\uptheta}_{jk} $$$) were modeled as a weekly random walk on the logit‐scale reflecting the tendency of predation probabilities to vary in a correlated pattern across weeks within a year (Payton et al, 2020). Specifically, $$$$ {\uptheta}_{1k}\sim \mathrm{beta}\left(1,1\right)\kern13.4em \mathrm{for}\ \mathrm{Week}\ 1, $$$$ $$$$ \mathrm{logit}\left({\uptheta}_{jk}\right)\sim \mathrm{Normal}\left(\mathrm{logit}\left({\uptheta}_{\left(j-1\right)k}\right),{\upsigma}_k^{\uptheta}\right)\kern2.5em \mathrm{for}\ \mathrm{Week}\ 2,3,\dots, J, $$$$ where $$$ {\upsigma}_k^{\uptheta} $$$ describes the year‐specific standard deviation (SD) of the random walk.…”

The benefits of merging passive and active tracking approaches: New insights into riverine migration by salmonid smolts

“…The most commonly observed avian piscine species on the River Endrick during this study were goosanders ( Mergus merganser ), gray heron ( Ardea cinerea ), and osprey ( Pandion haliaetus ), whereas the most common aquatic piscine predators in this area will likely be pike ( Esox lucius ) and brown trout (Adams, unpublished data). Predation can be a major driver of population dynamics and demography (Jonsson et al, 2017; Payton et al, 2020) by exerting a strong selective pressure (Ward & Hvidsten, 2011), but other factors can act concomitantly (e.g., environmental factors, hydropower; Lothian et al, 2018; Thorstad et al, 2012). Our finding that avian predation rates were particularly high in the immediate vicinity of the release site suggests that there was a concomitant interaction between the effects of trapping/tagging and vulnerability to predation for smolts.…”

The benefits of merging passive and active tracking approaches: new insights into riverine migration by salmonid smolts

“…Predation can be a major driver of population dynamics and demography (Jonsson et al 2017, Payton et al 2020) by exerting a strong selective pressure (Ward and Hvidsten 2011), but other factors can act concomitantly (e.g., environmental factors, hydropower, etc. ; Lothian, et al 2018, Thorstad, et al 2012.…”