2 resultados para DISTRIBUTION MODELS

em Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux:


Relevância:

60.00% 60.00%

Publicador:

Resumo:

Grassland bird species continue to decline steeply across North America. Road-based surveys such as the North American Breeding Bird Survey (BBS) are often used to estimate trends and population sizes and to build species distribution models for grassland birds, although roadside survey counts may introduce bias in estimates because of differences in habitats along roadsides and in off-road surveys. We tested for differences in land cover composition and in the avian community on 21 roadside-based survey routes and in an equal number of adjacent off-road walking routes in the grasslands of southern Alberta, Canada. Off-road routes (n = 225 point counts) had more native grassland and short shrubs and less fallow land and road area than the roadside routes (n = 225 point counts). Consequently, 17 of the 39 bird species differed between the two route types in frequency of occurrence and relative abundance, measured using an indicator species analysis. Six species, including five obligate grassland species, were more prevalent at off-road sites; they included four species listed under the Canadian federal Species At Risk Act or listed by the Committee on the Status of Endangered Wildlife in Canada: Sprague’s Pipit (Anthus spragueii), Baird’s Sparrow (Ammodramus bairdii), the Chestnut-collared Longspur (Calcarius ornatus), and McCown’s Longspur (Rhynchophanes mccownii). The six species were as much as four times more abundant on off-road sites. Species more prevalent along roadside routes included common species and those typical of farmland and other human-modified habitats, e.g., the European Starling (Sturnus vulgaris), the Black-billed Magpie (Pica hudsonia), and the House Sparrow (Passer domesticus). Differences in avian community composition between roadside and off-road surveys suggest that the use of BBS data when generating population estimates or distribution models may overestimate certain common species and underestimate others of conservation concern. Our results highlight the need to develop appropriate corrections for bias in estimates derived from roadside sampling, and the need to design surveys that sample bird communities across a more representative cross-section of the landscape, both near and far from roads. 

Relevância:

40.00% 40.00%

Publicador:

Resumo:

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.