Location biases in ecological research on Australian terrestrial reptiles

Abstract

Understanding geographical biases in ecological research is important for conservation, planning, prioritisation and management. However, conservation efforts may be limited by data availability and poor understanding of the nature of potential spatial bias. We conduct the first continent-wide analysis of spatial bias associated with Australian terrestrial reptile ecological research. to evaluate potential research deficiencies, we used Maxent modelling to predict the distributions of 646 reptile studies published from 1972 to 2017. Based on existing distributions of 1631 individual reptile study locations, reptile species richness, proximity to universities, human footprint and location of protected areas, we found the strongest predictor of reptile research locations was proximity to universities (40.8%). This was followed by species richness (22.9%) and human footprint (20.1%), while protected areas were the weakest predictor (16.2%). These results highlight that research effort is driven largely by accessibility and we consequently identify potential target areas for future research that can be optimised to ensure adequate representation of reptile communities.

Estimating alpha, beta, and gamma diversity through deep learning

“…Sampling biases pose a severe challenge for biodiversity reconstruction in countries of uneven spatial sampling, such as Australia (Piccolo et al, 2020). In our approach, we account for geographic bias in the training data by quantifying the uncertainty in the diversity predictions, which largely reflect those areas with little or no training instances.…”

Technical note: Conservative storage of water vapour: a key to practical measurements of water stable isotopes in tree stems and soils

“…These factors limit the utility of in-situ measurement systems to field sites in vicinity to civil infrastructure, which potentially leads to research sites chosen because of proximity to power rather than suitability as research location, and therefore, location biases (e.g. monitoring wildlife in vicinity to universities (Piccolo et al, 2020), or the location of protected areas worldwide (Joppa and Pfaff, 2009)). Additionally, remote areas tend to lie in regions with less wealth, leading to an underrepresentation of research requiring cost-intensive equipment.…”

Ecological impact assessment of climate change and habitat loss on wetland vertebrate assemblages of the Great Barrier Reef catchment and the influence of survey bias

“…In the absence of rigorous ecological assessments, consortium databases that combine surveys from many small studies and citizen science observations are often used (Fletcher et al, 2019; Zuckerberg et al., 2011). While the data may still be the best available, citizen science observations and ad hoc surveys can be often biased toward easy to access areas (e.g., roads or walking tracks), lack systematically derived absence records, and may fail to adequately represent remote or inaccessible ecosystems (Boakes et al, 2010; Fletcher et al, 2019; Piccolo et al, 2020). The extent to which this bias blurs knowledge on species distribution patterns largely depends on the spread of urban centers and the proximity of ecosystems to those urban centers.…”

“…Consortium databases have been used in Australia to predict species distributions with climate change (Booth et al., 2012; González‐Orozco et al., 2014; Graham et al., 2019), including in freshwater environments (Bond et al., 2011; Graham et al., 2019; James et al., 2017). However, the underlying extent to which spatial survey effort bias exists in records, and the relative influence of this on species predictability has been given little attention (Boakes et al., 2010; Fithian et al., 2015; Piccolo et al., 2020). If survey bias means species distribution models fail to encapsulate a reasonable approximation of distribution patterns, then conservation and restoration planning will be misinformed, potentially leading to inappropriate management (Dormann, 2007; Guillera‐Arroita et al., 2015).…”