Jean Polfus
I'm a Liber Ero Postdoctoral Fellow. I am interested in an approach to conservation that generates a permeable interface between research and society by respecting the lives and experiences of people that live and depend on the land.
Supervisors: Dr. Micheline Manseau
Supervisors: Dr. Micheline Manseau
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Papers by Jean Polfus
first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data
from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰for δ13C and 3.04‰for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information
on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It maybe difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.
2. We present a method and software for identifying spatial neighbourhoods in genetic distance data that adopts a regression framework where the predictors are generated using Moran’s eigenvectors maps (MEM), a multivariate technique developed for spatial ecological analyses and recommended for genetic applications.
3. Using simulated genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We also apply MEMGENE to genetic data from a highly vagile ungulate population and demonstrate spatial genetic neighbourhoods aligned with a river likely to reduce, but not
eliminate, gene flow.
4. We developed the MEMGENE package for R in order to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to modify the analysis. Detailed supplementary documentation and tutorials are provided.
first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data
from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰for δ13C and 3.04‰for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information
on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It maybe difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.
2. We present a method and software for identifying spatial neighbourhoods in genetic distance data that adopts a regression framework where the predictors are generated using Moran’s eigenvectors maps (MEM), a multivariate technique developed for spatial ecological analyses and recommended for genetic applications.
3. Using simulated genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We also apply MEMGENE to genetic data from a highly vagile ungulate population and demonstrate spatial genetic neighbourhoods aligned with a river likely to reduce, but not
eliminate, gene flow.
4. We developed the MEMGENE package for R in order to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to modify the analysis. Detailed supplementary documentation and tutorials are provided.