Addressing challenges in non invasive capture-recapture based estimates of small populations: a pilot study on the Apennine brown bear

Abstract

It is often difficult to determine optimal sampling design for non-invasive genetic sampling, especially when dealing with rare or elusive species depleted of genetic diversity. To address this problem, we ran a hair-snag pilot study on the remnant Apennine brown bear population. We used occupancy models to estimate the performance of an improved field protocol, a meta-analysis approach to indirectly model capture probability, and simulations to evaluate the effect of genotyping errors on the accuracy of capture-recapture population estimates. In spring 2007 we collected 70 bear hair samples in 15 5 x 5 km cells, using 5 10-day trapping sessions. Bear detectability was higher in 2007 than in a previous attempt on the same population in 2004, reflecting improved field protocols and sampling design. However, individual capture probability was 0.136 (95% CI = 0.120-0.152), still below the minimum requirements of capture-mark-recapture closed population models. We genotyped hair samples (n = 63) at 9 microsatellite loci, obtaining 94% Polymerase Chain Reaction success, and 13 bear genotypes. Estimated P(IDsib) was 0.00594, and per-genotype error rate was 0.13, corresponding to a 99% probability of correct individual identification. Simulation studies showed that the effect of non-corrected or filtered genetic errors on the accuracy of population estimates was negligible only when individual capture probability was > 0.2. Our results underline how the interaction among field protocols, sampling strategies and genotyping errors may affect the accuracy of DNA-based estimates of small and genetically depleted populations, and warned us about the feasibility of a survey using only traditional hair-snag sampling. In this and similar cases, indications from pilot studies can provide cost-effective means to evaluate the efficiency of designed sampling and modelling procedures

First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach

“…What emerges from the literature review is that the individual brown bears genotyping was performed by three different labs: until 2002 by the Experimental Zooprophylactic Institute of Abruzzo and Molise “G. Caporale” (IZSAM - Lab1, Method 1 described in Lorenzini et al [ 49 ]); in 2011 and 2014 by the Wildlife Genetics International lab (WGI - Lab2, Method 2 described in Ciucci et al [ 52 ]); and since 2002 until now, except for the core area in 2011 and 2014, by the Unit for Conservation Genetics of the Italian Institute for Environmental Protection and Research (ISPRA BIO-CGE - Lab3, Method 3 described in Gervasi et al [ 14 , 41 , 50 ] and Forconi et al [ 54 ]). The most recent study [ 55 ] begins to address the problem of how to merge datasets between Lab2/Method 2 and Lab3/Method 3.…”

Estimating survival in the Apennine brown bear accounting for uncertainty in age classification

“…Additionally, incidental samples were collected along the whole study period, usually by park wardens during their patrolling activity of the park area, including veriication of property damage caused by bears. Details on the individual sampling projects and techniques are provided in Gervasi et al (2008Gervasi et al ( , 2010Gervasi et al ( , 2012 and Ciucci et al (2015a).…”

“…By comparing the observed number of 1 and 2MM in the two datasets prior to merging, and that observed for the inal merged dataset, we assessed to what extent the increased number of individuals afected the power of the microsatellites panel to correctly distinguish individual bears. In addition, to allow comparison with previous studies conducted on the Apennine brown bear (Gervasi et al 2008(Gervasi et al , 2010(Gervasi et al , 2012 and with other non-invasive genetic sampling experiences on brown bears in Europe (Bellemain et al 2005;De Barba et al 2010), we also estimated the probability of identity (P id ) and the probability of identity of siblings (P idsib ) for the two genetic datasets and for the merged database (Waits et al 2001). However, as both the P id and the P idsib assume a certain level of genetic relatedness among individuals in the population, the exact interpretation of the latter statistics in terms of shadow efect occurrence was especially diicult (see Ciucci et al 2015a for details).…”

Counts of unique females with cubs in the Apennine brown bear population, 2006–2014

“…To estimate this probability we used the formula: where is the probability for an individual of age a and gender g to remain invisible to sampling until 2014. Sex specific capture probabilities in the 2004,2007,2008 and 2011 surveys were derived from Gervasi et al (2008Gervasi et al ( , 2010Gervasi et al ( , 2012 and Ciucci et al (2014), whereas and are yearly survival probabilities for cubs and adults, respectively (P. Ciucci unpublished data), assuming these remain constant across survey years. The complementary of (% corr ; Table S1) provides an estimate of the proportion of bears correctly classified as ≤4 years, suggesting that a great majority of bears with no previous hair-snag history are expected to be ≤4 years old.…”