Modelling the impact of toxic and disturbance stress on white-tailed eagle (Haliaeetus albicilla) populations

Journal of Applied Ecology

Hendriks

Kauffman

et al. 2013

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Summary 1.A major challenge in the conservation of threatened and endangered species is to predict population decline and design appropriate recovery measures. However, anthropogenic impacts on wildlife populations are notoriously difficult to predict due to potentially nonlinear responses and interactions with natural ecological processes like density dependence. 2. Here, we incorporated both density dependence and anthropogenic stressors in a stage-based matrix population model and parameterized it for a density-dependent population of peregrine falcons Falco peregrinus exposed to two anthropogenic toxicants [dichlorodiphenyldichloroethylene (DDE) and polybrominated diphenyl ethers (PBDEs)]. Log-logistic exposure-response relationships were used to translate toxicant concentrations in peregrine falcon eggs to effects on fecundity. Density dependence was modelled as the probability of a nonbreeding bird acquiring a breeding territory as a function of the current number of breeders. 3. The equilibrium size of the population, as represented by the number of breeders, responded nonlinearly to increasing toxicant concentrations, showing a gradual decrease followed by a relatively steep decline. Initially, toxicant-induced reductions in population size were mitigated by an alleviation of the density limitation, that is, an increasing probability of territory acquisition. Once population density was no longer limiting, the toxicant impacts were no longer buffered by an increasing proportion of nonbreeders shifting to the breeding stage, resulting in a strong decrease in the equilibrium number of breeders. 4. Median critical exposure concentrations, that is, median toxicant concentrations in eggs corresponding with an equilibrium population size of zero, were 33 and 46 lg g À1 fresh weight for DDE and PBDEs, respectively. 5. Synthesis and applications. Our modelling results showed that particular life stages of a density-limited population may be relatively insensitive to toxicant impacts until a critical threshold is crossed. In our study population, toxicant-induced changes were observed in the equilibrium number of nonbreeding rather than breeding birds, suggesting that monitoring efforts including both life stages are needed to timely detect population declines. Further, by combining quantitative exposure-response relationships with a wildlife demographic model, we provided a method to quantify critical toxicant thresholds for wildlife population persistence.

Section: Introductionmentioning

confidence: 99%

Modelling interactions of toxicants and density dependence in wildlife populations

Journal of Applied Ecology

Hendriks

Kauffman

et al. 2013

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“…, Korsman et al. )

r_{1} false(C false) = r_{normalmax} + ln false(\frac{1}{1 + {false[\frac{{EC}_{50}}{C} false]}^{β}} false) / {normalτ}_{g} + ln false(\frac{1}{1 + {false[\frac{{LC}_{50}}{C} false]}^{β}} false) / {normalτ}_{g}

…”

Section: Methodsmentioning

confidence: 99%

“…Population growth rate (r 1 ).-By definition, the population growth rate of species in a closed system depends on the reproduction and survival success of the population. The growth rate for a population exposed solely to toxicants can then be obtained by combining the reproduction and survival success with the intrinsic population growth rate (r max ) and generation time (s g ) of a species (Hendriks et al 2005, Korsman et al 2012)…”

Section: Population Extinction Vulnerability Indicatorsmentioning

confidence: 99%

“…Model parameterization.-To illustrate the method, we applied it to calculate the impact of DDE on the local extirpation vulnerability of three bird species: the White-tailed Eagle, the Bald Eagle, and the Osprey. Following, e.g., Korsman et al (2012), Nakamaru et al (2003), and Schipper et al (2013), we assumed that exposure to past and current prevailing environmental concentrations of DDE affects the fertility of these species (via abnormal breeding and eggshell thinning) rather than their survivorship. To parameterize Eq.…”

Section: Applicationmentioning

confidence: 99%

“…Confounding environmental factors that are correlated with the toxicant of interest may result in an overestimation of the impacts on population viability. To overcome this limitation, multiplicative or additive exposure-response relationships could be employed in the method instead (see, e.g., Korsman et al 2012). Furthermore, for toxic compounds that are not yet released or that dissipate quickly in the environment, hence for which exposure concentrations are not easily obtained from the environment, assessments can be based on laboratory data only.…”

Section: Speciesmentioning

confidence: 99%

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Using field data to quantify chemical impacts on wildlife population viability

Hilbers

Hoondert

Ecological Applications

et al. 2018

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Abstract. Environmental pollution is an important driver of biodiversity loss. Yet, to date, the effects of chemical exposure on wildlife populations have been quantified for only a few species, mainly due to a lack of appropriate laboratory data to quantify chemical impacts on vital rates. In this study, we developed a method to quantify the effects of toxicant exposure on wildlife population persistence based on field monitoring data. We established field-based vital-rate-response functions for toxicants, using quantile regression to correct for the influences of confounding factors on the vital rates observed, and combined the response curves with population viability modelling. We then applied the method to quantify the impact of DDE on three bird species: the White-tailed Eagle, Bald Eagle, and Osprey. Population viability was expressed via five population extinction vulnerability metrics: population growth rate (r 1 ), critical patch size (CPS), minimum viable population size (MVP), probability of population extirpation (PE), and median time to population extirpation (MTE). We found that past DDE exposure concentrations increased population extirpation vulnerabilities of all three bird species. For example, at DDE concentrations of 25 mg/kg wet mass of egg (the maximum historic exposure concentration reported in literature for the Osprey), r 1 became small (White-tailed Eagle and Osprey) or close to zero (Bald Eagle), the CPS increased up to almost the size of Connecticut (White-tailed Eagle and Osprey) or West Virginia (Bald Eagle), the MVP increased up to approximately 90 (White-tailed Eagle and Osprey) or 180 breeding pairs (Bald Eagle), the PE increased up to almost certain extirpation (Bald Eagle) or only slightly elevated levels (White-tailed Eagle and Osprey) and the MTE became within decades (Bald Eagle) or remained longer than a millennium (White-tailed Eagle and Osprey). Our study provides a method to derive species-specific field-based response curves of toxicant exposure, which can be used to assess population extinction vulnerabilities and obtain critical levels of toxicant exposure based on maximum permissible effect levels. This may help conservation managers to better design appropriate habitat restoration and population recovery measures, such as reducing toxicant levels, increasing the area of suitable habitat or reintroducing individuals.

Time‐varying effects of aromatic oil constituents on the survival of aquatic species: Deviations between model estimates and observations

Hoop

Viaene

Enviro Toxic and Chemistry

et al. 2016

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There is a need to study the time course of toxic chemical effects on organisms because there might be a time lag between the onset of chemical exposure and the corresponding adverse effects. For aquatic organisms, crude oil and oil constituents originating from either natural seeps or human activities can be relevant case studies. In the present study the authors tested a generic toxicokinetic model to quantify the time-varying effects of various oil constituents on the survival of aquatic organisms. The model is based on key parameters applicable to an array of species and compounds with baseline toxicity reflected by a generic, internal toxicity threshold or critical body burden (CBB). They compared model estimates with experimental data on the effects of 8 aromatic oil constituents on the survival of aquatic species including crustaceans and fish. The average model uncertainty, expressed as the root mean square error, was 0.25 (minimum-maximum, 0.04-0.67) on a scale between 0 and 1. The estimated survival was generally lower than the measured survival right after the onset of oil constituent exposure. In contrast, the model underestimated the maximum mortality for crustaceans and fish observed in the laboratory. Thus, the model based on the CBB concept failed to adequately predict the lethal effects of the oil constituents on crustaceans and fish. Possible explanations for the deviations between model estimates and observations may include incorrect assumptions regarding a constant lethal body burden, the absence of biotransformation products, and the steady state of aromatic hydrocarbon concentrations in organisms. Clearly, a more complex model approach than the generic model used in the present study is needed to predict toxicity dynamics of narcotic chemicals. Environ Toxicol Chem 2017;36:128-136. © 2016 SETAC.