Evaluating mortality rates with a novel integrated framework for nonmonogamous species

Abstract

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause‐specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long‐term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female‐based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two‐sex framework that allows investigation of population dynamics and quantification of cause‐specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human‐related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human‐bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause‐specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.

Genetic and genomic monitoring with minimally invasive sampling methods

“…Long‐term MIS studies have been an effective way to estimate survival and growth rates in a range of species, by tracking individuals using their genotypes. This has been accomplished using mark–recapture models in species such as southern right whales ( Eubalaena australis ; Carroll et al., , ), the dendrobatid frogs ( Allobates femoralis ; Ringler, Mangione, & Ringler, ), Māui dolphins ( Cephalorhynchus hectori maui ; Baker et al., ), brown bears (Tenan et al., ) and imperial eagles ( Aquila heliaca ; Rudnick et al., ). The definitive DNA marks provided by genetic monitoring can provide robust population estimates in age‐structured populations that can be difficult to observe in the wild.…”

“…First high-throughput sequencers on market 1980 mtDNA from snagged hair samples (Tablerlet & Bouvet, 1992) mtDNA from faecal samples (Hoss et al, 1992) PCR-based sex ID method from hair samples (Taberlet et al, 1993) Microsatellite genotyping for individual identification from hair and faecal samples (Taberlet et al, 1997) First MIS genotype mark-recapture population size estimate for brown bears (Woods et al, 1999) Faecal DNA diet study using amplicon sequencing & high-throughput platform (Valentini et al, 2009) SNP genotyping of faecal samples (Norman & Sprong, 2015) High-throughput sequencing of microsatellite markers from faeces improves genotyping performance (De Barba et al, 2016) Stable isotope analysis of hair samples provides insight into diet (Felicetti et al, 2004) 2 | SAMPLING AND ME THODOLOG IC AL CONS IDER ATIONS…”

The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests

“…However, caution must be recommended as the uncertainty associated to these predictions may be large depending on the quality of data themselves (e.g. Tenan et al, 2016). Yet, the examples given indicate that the inference based on data from the sampling area, or sampling years, remain very informative to predict changes.…”

Investigating the potential of opportunistic sighting data to inform wildlife conservation strategies

“…Because the hair trap and rub tree data were collected according to a specific protocol, we refer to this structured data as traditional SCR data, or simply ‘SCR data’. In addition to the structured data collection, we also collected opportunistic hair and feces data (De Barba et al 2010a,b; Groff et al 2014; Tenan et al 2016). Following notification by third parties (typically members of the public), opportunistic sampling of hair and feces was carried out by agency personnel at sites where bear damage occurred, e.g.…”

“…Following notification by third parties (typically members of the public), opportunistic sampling of hair and feces was carried out by agency personnel at sites where bear damage occurred, e.g. depredation on livestock, beehives and/or crops (Tenan et al 2016). We refer to this data as ‘opportunistic data’.…”