Tagging piglets at the farrowing nest in the wild: Some preliminary guidelines

Neonate ungulate often show high rates of mortality due to predation, starvation, orexposure to bad weather, leading to losses frequently exceeding 50%. Wild boar piglets are known tosuffer from thermoregulation insufficiency, which probably explain the nest construction behaviour insows. We thus tried to develop a method for tagging piglets inside their farrowing (or birth) nest toassess piglet survival from few days after their birth onwards. Sows fitted-out with VHF collars wereradio-tracked to determine parturition time, and to get a rough idea of the possible birth nest location.Then, with a handled antenna we approached on foot the birth nest, and piglets were caught, taggedand fitted-out with a backpack transmitter and released inside the nest. Temporal movements ofmother and litter association were monitored, as long as possible. Results on sow behaviour and tacticagainst human approach, piglets body mass, piglet reaction, and survival in their early lifetime weredescribed

Influence of harvesting pressure on demographic tactics: implications for wildlife management

1. Demographic tactics within animal populations are shaped by selective pressures. Exploitation exerts additional pressures so that differing demographic tactics might be expected among populations with differences in levels of exploitation. Yet little has been done so far to assess the possible consequences of exploitation on the demographic tactics of mammals, even though such information could influence the choice of effective management strategies.2. Compared with similar-sized ungulate species, wild boar Sus scrofa has high reproductive capabilities, which complicates population management. Using a perturbation analysis, we investigated how population growth rates (lambda) and critical life-history stages differed between two wild boar populations monitored for several years, one of which was heavily harvested and the other lightly harvested.3. Asymptotic lambda was 1 242 in the lightly hunted population and 1 115 in the heavily hunted population, while the ratio between the elasticity of adult survival and juvenile survival was 2 63 and 1 27, respectively. A comparative analysis including 21 other ungulate species showed that the elasticity ratio in the heavily hunted population was the lowest ever observed.4. Compared with expected generation times of similar-sized ungulates (more than 6 years), wild boar has a fast life-history speed, especially when facing high hunting pressure. This is well illustrated by our results, where generation times were 3 6 years in the lightly hunted population and only 2 3 years in the heavily hunted population. High human-induced mortality combined with non-limiting food resources accounted for the accelerated life history of the hunted population because of earlier reproduction.5. Synthesis and applications. For wild boar, we show that when a population is facing a high hunting pressure, increasing the mortality in only one age-class (e. g. adults or juveniles) may not allow managers to limit population growth. We suggest that simulations of management strategies based on context-specific demographic models are useful for selecting interventions for population control. This type of approach allows the assessment of population response to exploitation by considering a range of plausible scenarios, improving the chance of selecting appropriate management actions

Effects of spatial aggregation of nests on population recruitment: the case of a small population of Atlantic salmon

RésuméInternational audienc

How can quantitative ecology be attractive to young scientists? Balancing computer/desk work with fieldwork

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Assessing whether mortality is additive using marked animals: a Bayesian state?space modeling approach

International audienceWhether different sources of mortality are additive, compensatory, or depensatory is a key question in population biology. A way to test for additivity is to calculate the correlation between cause-specific mortality rates obtained from marked animals. However, existing methods to estimate this correlation raise several methodological issues. One difficulty is the existence of an intrinsic bias in the correlation parameter. Although this bias can be formally expressed, it requires knowledge about natural survival without any competing mortality source, which is difficult to assess in most cases. Another difficulty lies in estimating the true process correlation while properly accounting for sampling variation. Using a Bayesian approach, we developed a state–space model to assess the correlation between two competing sources of mortality. By distinguishing the mortality process from its observation through dead recoveries and live recaptures, we estimated the process correlation. To correct for the intrinsic bias, we incorporated experts’ opinions on natural survival. We illustrated our approach using data on a hunted population of wild boars. Mortalities were not additive and natural mortality increased with hunting mortality likely as a consequence of non-controlled mortality by crippling loss. Our method opens perspectives for wildlife management and for the conservation of endangered species