Do Citizen Science Methods Identify Regions of High Avian Biodiversity?

Abstract

Citizen science may offer a way to improve our knowledge of the spatial distribution of biodiversity and endemism, as the data collected by this method can be integrated into existing data sources to provide a more robust understanding of broad scale patterns of species richness. We explored whether data collected by citizen scientists agree on identifying regions of high avian species richness in a well-studied state. We compiled and examined the number of bird species detected in each of the 77 counties of Oklahoma based on published range maps, museum collections, and by five citizen science methods: the USGS Breeding Bird Survey, the Oklahoma Breeding Bird Atlas, eBird, the Oklahoma Winter Bird Atlas, and National Audubon Society Christmas Bird Counts. We also quantified the number of species of conservation concern recorded by each method in each county. A total of 460 species were reported across the state, with the total number of species detected by each method ranging from 40% of this total (Winter Bird Atlas) to 94% of this total (eBird). In general, species totals were poorly correlated across methods, with only six of 21 combinations (28.6%) showing significant correlations. Total species numbers recorded in each county were correlated with human population density and county area, but not with mean annual temperature or precipitation. The total number of species of conservation concern was correlated with the total number of species detected, county area, and precipitation. Most of the citizen science methods examined in this study were not explicitly designed to identify regions of high biodiversity and so efforts to use these methods for this purpose should be employed only cautiously and with a thorough understanding of potential biases.

Long-term Christmas Bird Counts describe neotropical urban bird diversity

“…The analysis presented here showed how long-term CBC data can be analyzed over time to recognize with more precision the composition and structure of bird communities within and around urban areas [ 8 ]. Avian monitoring involves systematically collecting and analyzing long-term data under a question or premise of analysis that consolidates complete inventories over time and recognizes their changes and/or patterns on a spatial and temporal scale, as well as using the results to guide the formulation and evaluation of conservation policies [ 44 ]. Thus, CBC long-term community characterization made it possible to identify patterns of attributes such as richness, abundance, similarity, or complementarity in bird communities and how these might vary at a spatial level, making it possible to obtain an increasingly complete picture of the diversity and distribution of bird communities that persists in urban and peri-urban environments in cities of the Neotropics [ 18 , 45 ].…”

Annual Censuses and Citizen Science Data Show Rapid Population Increases and Range Expansion of Invasive Rose-Ringed and Monk Parakeets in Seville, Spain

“…Citizen science data have several biases that may explain these results. Although occurrence records from volunteers can be temporally and spatially biased when the detectability of the species is difficult or survey efforts are inadequate [66,[83][84][85], these biases are unlikely in this case, given that parakeets are loud and highly conspicuous [59,86], thus facilitating their detection by birdwatchers. However, volunteers can accidentally introduce sampling bias such as a misidentification between both parakeet species (which is rather unlikely due to their different morphologies and behaviors) or under-reporting, especially when these species become abundant, or simply because they are not interesting to observers [87,88].…”

Long-term Christmas Bird Counts describe Neotropical urban bird diversity

“…The analysis presented here showed how long-term CBC data can be analyzed over time to recognize with more precision the composition and structure of bird communities within and around urban areas [8]. Avian monitoring involves systematically collecting and analyzing long-term data under a question or premise of analysis that consolidates complete inventories over time and recognizes their changes and/or patterns on a spatial and temporal scale, as well as using the results to guide the formulation and evaluation of conservation policies [44]. Thus, CBC long-term community characterization made it possible to identify patterns of attributes such as richness, abundance, similarity, or complementarity in bird communities and how these might vary at a spatial level, making it possible to obtain an increasingly complete picture of the diversity and distribution of bird communities that persists in urban and peri-urban environments in cities of the Neotropics [45,18].…”