Exploring the ecological processes driving geographical patterns of breeding bird richness in British Columbia, Canada

Abstract

British Columbia (BC), Canada, has a diverse landscape that provides breeding habitat for > 300 avian species, and the recent development of the BC Breeding Bird Atlas data set presents key information for exploring the landscape conditions which lead to biological richness. We used the volunteer-collected raw breeding bird evidence data set to analyze the effects of sampling biases on spatial distribution of observed breeding bird species and implemented regression tree analysis (Random Forests) to examine the influence of productivity, ambient energy, and habitat heterogeneity on independently measured breeding bird richness. Results indicated that total breeding species richness is correlated with total survey effort (alpha < 0.001). By stratifying species richness by survey effort, we observed that ambient energy is the top-ranked environmental predictor of breeding bird richness across BC, which, when used in combination with a number of other environmental variables, explains -40% of the variation in richness. Using our modeled relationships, we predicted breeding bird species richness in the areas of BC not presently surveyed between three and six hours. The majority of the productive Boreal Plains, the southern portion of the Taiga Plains region, the lowlands of the Southern and Central Interior, along the Rocky Mountain Trench, and the coastal areas of the Georgia Depression are predicted to have the highest categories of breeding richness (35-57 unique species). Our results support ongoing species diversity gradient research, which identifies ambient energy as an important factor influencing species diversity distributions in the Northern Hemisphere. By linking breeding bird richness to environmental data derived from remotely sensed data and systematically collected climate data, we demonstrate the potential to monitor shifts in ambient energy as a surrogate for vertebrate habitat condition affecting population levels. By analyzing the influence of survey effort on species richness metrics, we also highlight the need to consider adding attributes to the raw breeding bird data set to describe observer experience, such as hours or seasons spent surveying, and provide survey dates to allow greater flexibility for removing survey bias. These additions can increase the utility of atlas data for species richness studies useful for conservation planning.

The grazing impacts of four barren ground caribou herds (Rangifer tarandus groenlandicus) on their summer ranges: An application of archived remotely sensed vegetation productivity data

“…While originally applied as an index to describe plant communities (Coops et al, 2009a;Fitterer, Nelson, Coops, & Wulder, 2012) and animal diversity (Coops, Wulder, & Iwanicka, 2009b;Andrew, Wulder, Coops, & Baillargeon, 2012;Fitterer, Nelson, Coops, Wulder, & Mahony, 2013;Rickbeil, Coops, Drever, & Nelson, 2014b), DHI is also a useful predictor of individual coastal bird species distributions (Rickbeil et al, 2014a) and for describing forage conditions for moose (Alces alces) in Ontario, Canada (Michaud et al, 2014). The DHI estimates three components of landscape productivity -the yearly sum or overall productivity, the seasonality (the change between the maximum and minimum productivity throughout the year), and the minimum annual productivity (not considered here as all arctic vegetation goes to 0 in terms of fPAR values owing to the short growing season).…”

Biodiversity Indicators Show Climate Change Will Alter Vegetation in Parks and Protected Areas

“…To date, satellite measures of productivity have been correlated with plant canopy cover [29,30] and overall productivity [23] in historical [31][32][33] and current contexts [34,35]. Relationships between productivity indicators and biodiversity have also been used to assess future spatial distributions of species [36] and biodiversity [26,37]. Time series of remotely sensed productivity components have been linked with climate data and applied to forecast future indicators of biodiversity [37][38][39][40][41].…”

Predicting Climate Change Impacts to the Canadian Boreal Forest

“…Research has previously documented that wide-ranging and sensitive species, such as birds and butterflies, follow vegetation productivity trends modelled by remotely sensed imagery [20][21][22][23]27,28,81]. Notably, seasonal trends captured by the fPAR index have been able to distinguish spatial variation in bird species richness, including species' responses to seasonal and minimum annual productivity levels [23] and associations between composite DHI heterogeneity [81].…”