The demography of a newly establishedOspreyPandion haliaetuspopulation inFrance

Abstract

Using long‐term mark–resighting data acquired over 27 years in continental France, we estimated demographic parameters and modelled the dynamics of a newly established population of Ospreys Pandion haliaetus using a life‐history model. We then performed prospective and retrospective analyses to estimate the sensitivity of the population growth rate to demographic parameters, and to quantify their contribution to the observed variation in abundance. The observed population growth rate was estimated at 1.150 (from one to 38 pairs in the period 1985–2011), and the stochastic population growth rate was estimated at 1.156. The number of fledglings per nest made the largest contribution to the variance of the observed population growth rate. Breeding productivity was stable across years. In contrast, the prospective analysis indicated that the sensitivity of the population growth rate was greatest for immigration and adult survival. Our results suggest that the increase of a new and recently established breeding population of Ospreys was mainly driven by local dynamics (high productivity and high proportion of breeding individuals), with no sign of density‐dependence except for juvenile survival. This probably reflects highly favourable conditions for breeding. Our results show that productivity can be a major driver in recovering raptor populations, and conservation work should aim to protect occupied nest‐sites and their surrounding habitat and to maintain highly favourable foraging areas in the vicinity of breeding sites.

The Osprey reintroduction in Central Italy: dispersal, survival and first breeding data

“…2.5 times below the survival probability of 0.50 obtained from wild-born juveniles from a population in continental France (Wahl & Barbraud 2014). However, the annual survival rate increased to 0.69 for second-year birds (immatures) but it was still lower than the survival rate of 0.87 estimated for the immatures and adults in Continental France (Wahl & Barbraud 2014). This suggests that reintroduced juveniles may be affected by the lack of parental care in the dispersal phase of their life (for example, fishing lessons from parents during the PFDP may result in higher survival of young once they are dispersed).…”

“…By pooling these values we obtained a first-year annual survival of 0.20, i.e. 2.5 times below the survival probability of 0.50 obtained from wild-born juveniles from a population in continental France (Wahl & Barbraud 2014). However, the annual survival rate increased to 0.69 for second-year birds (immatures) but it was still lower than the survival rate of 0.87 estimated for the immatures and adults in Continental France (Wahl & Barbraud 2014).…”

“…On the other hand, annual survival rate was relatively high (0.93) for adults compared to other wild populations: in Sweden 0.81 (Ryttmann 1994), the USA 0.85-0.90 (Spitzer et al 1983) and continental France 0.87 (Wahl & Barbraud 2014). This result could be related to the current low population density in the area, and suggests favourable conditions for adults and low levels of competition with conspecifics for food resources and for territories, as already postulated in other reintroductions (Martell et al 2002).…”

Using field data to quantify chemical impacts on wildlife population viability

“…, Sulawa et al. , Wahl and Barbraud ) and the current population size set equal to the carrying capacity K ( C ) at the start of the simulations. Similar to MVP, this reflects a situation in which a healthy population is exposed solely to toxicants and thus enables to explore the changes in the response of population viability due to toxicant exposure.…”

“…To account for demographic stochasticity, we sampled the population size at time t + 1 from a Poisson distribution (Bonsall andHastings 2004, Melbourne andHastings 2008). As the current population size and the carrying capacity of populations are context dependent, we used two scenarios in the estimation of PE and MTE, with the carrying capacity without toxicant exposure K(0) set equal to 500 or 5,000 individuals (around the range of values reported in the literature; Fraser et al 1996, Watts et al 2008, Kr€ uger et al 2010, Sulawa et al 2010, Wahl and Barbraud 2014 and the current population size set equal to the carrying capacity K(C) at the start of the simulations. Similar to MVP, this reflects a situation in which a healthy population is exposed solely to toxicants and thus enables to explore the changes in the response of population viability due to toxicant exposure.…”