Dataset Information

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.

SUBMITTER: Piccolo RL

PROVIDER: S-EPMC7298028 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Location biases in ecological research on Australian terrestrial reptiles.

Piccolo Renee Louise RL Warnken Jan J Chauvenet Alienor Louise Marie ALM Castley James Guy JG

Scientific reports 20200616 1

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 reptil ...[more]

PMID: 32546845

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Project description:The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists' abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network's structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

Dataset Information

Location biases in ecological research on Australian terrestrial reptiles.

Publications

Location biases in ecological research on Australian terrestrial reptiles.

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