The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias

Abstract

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5′ × 5′ grid cell, or “pentad”). The explanatory variables were distance to major road and exceptional birding locations or “sampling hubs,” percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.

The relationship between wealth and biodiversity: A test of the Luxury Effect on bird species richness in the developing world

“…However, we know that urban areas were among the habitat specifically targeted (Harebottle et al, 2008) and that there is a general bias in coverage towards urban areas (Hugo & Altwegg, 2017), so we believe that even in peri-urban pentads, urban coverage is likely to have been adequate. Although we believe our results provide important insights into potential impacts of socio-economic and physical development, there are nevertheless some caveats on the interpretation of the results.…”

“…First, in common with the vast majority of Citizen Science-based atlas data, we do not know the precise locations surveyed for any given visit within a pentad, so the extent to which systematic biases in observer behaviours may have affected the results remains unknown. However, we know that urban areas were among the habitat specifically targeted (Harebottle et al, 2008) and that there is a general bias in coverage towards urban areas (Hugo & Altwegg, 2017), so we believe that even in peri-urban pentads, urban coverage is likely to have been adequate. Furthermore, although we cannot control for spatial effort, we did control for the effort in terms of the number of cards submitted and the time spent surveying.…”

Large-scale assessment of commensalistic–mutualistic associations between African birds and herbivorous mammals using internet photos

“…On the other hand, we were not able to find any evidence for several previously described associations, e.g., between chorister robin-chat Cossypha dichroa and nyala Tragelaphus angasi and bushbuck Tragelaphus scriptus (for other cases see also Dean & MacDonald, 1981 ). This may indicate limitations of our approach which may be the result of species- and region-specific public bias towards common or “charismatic” species of birds and mammals and to regions with a good infrastructure ( Clucas, McHugh & Caro, 2008 ; Hugo & Altwegg, 2017 ; Troudet et al, 2017 ). Alternatively, the absence of some associations among the results of the internet search may be caused by our searching method because only English genus names were involved in the search, hence, overestimating associations for more common species.…”

Occupancy models for citizen‐science data

“…As a result, the main environmental gradients were well sampled, albeit sampling intensity varied a lot in geographical space ( Figure 1) and in environmental space (Hugo & Altwegg, 2017). The Southern African Bird Atlas Project challenges provide a good example of the active role of atlas organizers that we believe to be essential to maximizing utility of resulting data.…”