Incorporating topographic variables into the models allowed us to make more reliable predictions for squares that were never actually surveyed by atlassers. We included five topographic variables in our predictive models of PObs that are closely associated with these ecological factors: latitude, longitude, elevation, slope, and aspect. Mapping and Interpreting Probability of ObservationĮcological factors such as temperature, moisture, and soils shape the distribution and quantity of habitat, and therefore also birds. The predicted values from the model were what we defined as the Probability of Observation. We used data collected by atlassers during 10-km square searches and during five minute point counts to build models that made predictions about the likelihood of detecting each species in all areas of the province, within a fixed amount of time (20 hours). The likelihood estimate afforded by the PObs measure is therefore useful for identifying the core areas of a species’ range, after accounting for differences in survey and geographic effort. This approach is useful, because it provides estimates that are comparable among squares in situations where search effort is not equal among squares, not all squares were surveyed, there were a large number of observers, and it may have been difficult to accurately assess the total number of individuals present at all surveyed point count locations due to variations in habitat structure or breeding behaviours. In a project of this size, for each species there are many detections and corresponding estimates of search effort, which allows us to obtain reasonable estimates of the ease by which each species can be found. The concept is simple: when we look across all species, squares, and atlassers, on average abundant species are likely to be found more quickly, whereas rarer species take longer to detect. This metric incorporates information on bird abundance, as well as the amount of time it takes an atlasser to locate a particular species ( i.e., search effort). Probability of Observation is a measure of detectability that can be used to indicate bird distribution and abundance in bird atlases. PObs relates to abundance, and for some species PObs can accurately indicate abundance, but for others it does not, hence the need to combine PObs with actual abundance to identify the main centres of population, which are described in the species account texts by biogeographic regions, broad climate-vegetation types and elevation ranges. Darker colours on the PObs maps indicate areas where a bird is more likely to be found. A species was predicted to occur in an unsurveyed square if that square lies within the species’ range, shares biogeoclimatic features with areas where the species is known to occur, and includes the elevation range the species uses in British Columbia. The PObs maps show how likely a species is to be found anywhere in the province. Second, using only point count data, we calculated observed abundance in the 10-km squares where the species was detected on point counts. First, we used all Atlas data, including point count data and information on search effort, to model and map the predicted chance of finding the species after 20 hours of searching a 10-km square – the “Probability of Observation”, or PObs (pronounced “Pee-Obs”). We used two approaches to try to define centres of occurrence and abundance for about 240 species for which we had sufficient data.
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