Simulation Files

Sims900inds_L3Thresh250i.csv is CDPOP input file. C2C2_900Inds64PixelsIDXY.csv is file of individual locations used in CDPOP. CD3_900Inds64Pixels_R20.csv is input file of cost distances with b1=1 and b2=20. GDmatrix.csv is an output file from the 100th generation simulated by CDPOP

Father-offspring behaviours.

<p>Frequency of problem and non-problem offspring grouped by behaviour type of their father.</p

Nature vs. Nurture: Evidence for Social Learning of Conflict Behaviour in Grizzly Bears

<div><p>The propensity for a grizzly bear to develop conflict behaviours might be a result of social learning between mothers and cubs, genetic inheritance, or both learning and inheritance. Using non-invasive genetic sampling, we collected grizzly bear hair samples during 2011–2014 across southwestern Alberta, Canada. We targeted private agricultural lands for hair samples at grizzly bear incident sites, defining an incident as an occurrence in which the grizzly bear caused property damage, obtained anthropogenic food, or killed or attempted to kill livestock or pets. We genotyped 213 unique grizzly bears (118 M, 95 F) at 24 microsatellite loci, plus the amelogenin marker for sex. We used the program COLONY to assign parentage. We evaluated 76 mother-offspring relationships and 119 father-offspring relationships. We compared the frequency of problem and non-problem offspring from problem and non-problem parents, excluding dependent offspring from our analysis. Our results support the social learning hypothesis, but not the genetic inheritance hypothesis. Offspring of problem mothers are more likely to be involved in conflict behaviours, while offspring from non-problem mothers are not likely to be involved in incidents or human-bear conflicts themselves (Barnard’s test, <i>p</i> = 0.05, 62.5% of offspring from problem mothers were problem bears). There was no evidence that offspring are more likely to be involved in conflict behaviour if their fathers had been problem bears (Barnard’s test, <i>p</i> = 0.92, 29.6% of offspring from problem fathers were problem bears). For the mother-offspring relationships evaluated, 30.3% of offspring were identified as problem bears independent of their mother’s conflict status. Similarly, 28.6% of offspring were identified as problem bears independent of their father’s conflict status. Proactive mitigation to prevent female bears from becoming problem individuals likely will help prevent the perpetuation of conflicts through social learning.</p></div

Study Area.

<p>Map of the study area (BMA 6) and incident hair samples in southwestern Alberta. An incident is defined to be an occurrence in which the grizzly bear caused property damage, obtained anthropogenic food, or killed or attempted to kill livestock or pets.</p

Appendix A. Details for computing the marginal likelihood and obtaining the maximum likelihood estimators.

Details for computing the marginal likelihood and obtaining the maximum likelihood estimators

Supplement 1. R code for computing least-cost path distance and likelihood analysis of the spatial capture–recapture (SCR) model, for computing the marginal likelihood and obtaining the MLEs, and for simulation study, and a narrative walk-through in PDF format.

<h2>File List</h2><div> <p> <a href="EDmanuscript-supplements.pdf">EDmanuscript-supplements.pdf</a> (md5: 44eb27bf3d2ab768941365c6a9dc1d43) – R code in pdf form and extra explanation for supplement 1<br> <a href="leastcostpath.R">leastcostpath.R</a> (md5: f8d1a9c67e4ee10ed633937d82b68f3f) - R code for computing least-cost path distance and likelihood analysis of the SCR model<br> <a href="MLE.R">MLE.R</a> (md5: 4a4f93a6aa444cb52f458e0dbdc80db5)- R code for computing the marginal likelihood and obtaining the MLEs<br> <a href="simstudy.R">simstudy.R</a> (md5: 1b05540b8556e498242191382ba37c1b) - R code for simulation study </p> </div><h2>Description</h2><div> <p>The supplement demonstrates the computation of least-cost path and its use in obtaining maximum likelihood estimates of model parameters. The file Leastcostpath.R is an R script which can be executed directly (narrative walk-through is provided by the PDF file EDmanuscript-supplements.pdf). The likelihood function (in the form of an R function) is provided in the R script MLE.R. Finally, the various pieces are put together to carry-out a simulation study in the R script simstudy.R. </p> </div

Appendix B. MLE summary statistics for reanalysis of the systematic landscape.

MLE summary statistics for reanalysis of the systematic landscape

Supplement from Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore

Current range expansions of large terrestrial carnivores are occurring following human-induced range contraction. Contractions are often incomplete, leaving small remnant groups in refugia throughout the former range. Little is known about the underlying ecological and evolutionary processes that influence how remnant groups are affected during range expansion. We used data from a spatially explicit, long-term genetic sampling effort of grizzly bears (<i>Ursus arctos</i>) in the Northern Continental Divide Ecosystem (NCDE), USA to identify the demographic processes underlying spatial and temporal patterns of genetic diversity. We conducted parentage analysis to evaluate how reproductive success and dispersal contribute to spatio-temporal patterns of genetic diversity in remnant groups of grizzly bears existing in the southwestern (SW), southeastern (SE) and east-central (EC) regions of the NCDE. A few reproductively dominant individuals and local inbreeding caused low genetic diversity in peripheral regions that may have persisted for multiple generations before eroding rapidly ( approx. one generation) during population expansion. Our results highlight that individual-level genetic and reproductive dynamics plays critical roles during genetic assimilation, and shows that spatial patterns of genetic diversity on the leading edge of an expansion may result from historical demographic patterns that are highly ephemeral

Comparison of top models from 2 datasets for grizzly bear local abundance.

*<p>Weights are the proportion of MCMC samples with these covariates and represent support for models of the effect of human and habitat factors potentially influencing grizzly bear abundance in northwestern Montana, USA, in 2004. We report models up to cumulative weight = 0.5. Combined analyses include both hair trap and bear rub data.</p

Sampling sites.

<p>Location of (A) hair traps distributed on a 7×7 km grid and (B) bear rubs sampled to detect grizzly bears in northwestern Montana, USA, in 2004. Position of the same bear rubs within a (C) 10.3×10.3 km (median female home range size)) and (D) 19.7×19.7 km (median male home range size) grid scale used for analysis. Highlighted cells contain both hair traps and bear rubs and were used in our analyses comparing variable selection with each method.</p