Beyond Natural History: Some Thoughts About Research Priorities in the Study of Xenarthrans

Loughry, W. J.; McDonough, Colleen M.

doi:10.5537/020.014.0102

Cited by 8 publications

(3 citation statements)

References 32 publications

(21 reference statements)

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“…However, natural history information for most of the Xenarthra species is scarce and, when available, often limited to a few short‐term studies or based on captive animals (Chiarello, 2008; McDonough & Loughry, 2008; Superina et al, 2014). Even for widespread taxa, available data are mostly derived from studies from few localities or a particular biome and may not reflect how the species respond under distinct environmental conditions (Diniz & Brito, 2012; Loughry & McDonough, 2013). Therefore, the effectiveness of actions for conserving xenarthrans may be hindered by extensive knowledge shortfalls.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the effectiveness of actions for conserving xenarthrans may be hindered by extensive knowledge shortfalls. To help fill these gaps and guide scientific projects, previous studies have highlighted the most needed topics and defined priority species for research (Diniz & Brito, 2012; Loughry & McDonough, 2013; Superina et al, 2014). However, no attempt has been made to define priority areas and spatially orient fieldwork across the Neotropics in a cost‐effective manner.…”

Section: Introductionmentioning

confidence: 99%

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Identifying hotspots and priority areas for xenarthran research and conservation

Feijó

Wen

et al. 2022

Diversity and Distributions

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Aim Limited funds for conservation and research require the development of prioritization schemes. Traditionally, biodiversity metrics were used to delineate priority areas; however, a growing realization emphasizes that logistic factors should be also considered. Here, we combine species richness, past collection efforts, degree of habitat loss and accessibility to define priority areas and spatially orient fieldwork in a cost‐effective manner for xenarthrans. Location Neotropics. Methods We assessed spatial patterns of species richness in Xenarthra and identified diversity hotspots based on species distribution models. Spatial patterns and biases in the Xenarthra past collection efforts were analysed using a comprehensive database including 33,464 individual records for 34 species of Xenarthra. Finally, we produced priority area indices relating species richness and collection efforts with levels of habitat loss and accessibility (roads and rivers network) to highlight important but neglected areas. Results Collection efforts were concentrated to a small portion of the Neotropics (central‐western Brazil, eastern Bolivia and north‐western Argentina) and were biased towards access routes. Only 4% of the Neotropics can be considered well sampled. Major xenarthran diversity hotspots include the Amazonian lowlands of Bolivia and the dry Chaco of Paraguay and Argentina. Priority areas for research varied depending on the metric analysed. Amazon holds a high diversity that remains poorly explored. Central Argentina and eastern Brazil are priority areas for research and conservation given the low sampling efforts, high diversity and endemic species, high levels of habitat loss and a dense road network. Main conclusions Most areas of the Neotropics lack a proper assessment of the xenarthran assemblage, reflecting extensive knowledge shortfalls. Sites close to roads tend to be better sampled, but many areas with a dense road network are undersampled, being good candidates for low‐cost studies. Xenarthra diversity hotspots facing the highest levels of habitat alteration are the least protected and are priority for both research and conservation. Simultaneous efforts of data collection and conservation actions across most of the xenarthran range should be stimulated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying hotspots and priority areas for xenarthran research and conservation

Feijó

Wen

et al. 2022

Diversity and Distributions

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“…While the armadillo has primarily been reported to be nocturnal, there appears to be a substantial degree of plasticity to this behavior (McDonough & Loughry, 1997 ). Armadillos can switch between nocturnal and diurnal activity seasonally, geographically, ontogenetically, or in response to local weather conditions (Harmsen et al, 2011 ; Loughry & McDonough, 2013 ; McDonough & Loughry, 1997 ). However, the aforementioned explanations did not appear to drive the patterns we were observing.…”

Section: Introductionmentioning

confidence: 99%

Nine‐banded armadillo (Dasypus novemcinctus) activity patterns are influenced by human activity

DeGregorio

Gale

Lassiter

et al. 2021

Ecology and Evolution

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As the human footprint upon the landscape expands, wildlife seeking to avoid human contact are losing the option of altering their spatial distribution and instead are shifting their daily activity patterns to be active at different times than humans. In this study, we used game cameras to evaluate how human development and activity were related to the daily activity patterns of the nine-banded armadillo (Dasypus novemcinctus) along an urban to rural gradient in Arkansas, USA during the winter of 2020-2021. We found that armadillos had substantial behavioral plasticity in regard to the timing of their activity patterns; >95% of armadillo activity was nocturnal at six of the study sites, whereas between 30% and 60% of activity occurred during the day at three other sites. The likelihood of diurnal armadillo activity was best explained by the distance to downtown Fayetteville (the nearest population center) and estimated ambient sound level (both indices of human activity) with armadillos being most active during the day at quiet sites far from Fayetteville. Furthermore, armadillo activity occurred later during the night period (minutes after sunset) at sites near downtown and with higher anthropogenic sound. Anecdotal evidence suggests that the observed activity shift may be in response to not only human activity but also the presence of domestic dogs. Our results provide further evidence that human activity has subtle nonlethal impacts on even common, widespread wildlife species. Because armadillos have low body temperatures and basal metabolism, being active during cold winter nights likely has measurable fitness costs. Nature reserves near human population centers may not serve as safe harbors for wildlife as we intend, and managers could benefit from considering these nonlethal responses in how they manage recreation and visitation in these natural areas.

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Noninvasive Low‐cost Method to Identify Armadillos' Burrows: A Machine Learning Approach

Rodrigues

Nogueira

Chiarello

2021

Wildlife Society Bulletin

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Having accurate information about population parameters of armadillos (Mammalia, Cingulata) is essential for the conservation and management of the taxon, most species of which remain poorly studied. We investigated whether we could accurately identify 4 armadillo species (Euphractus sexcinctus, Dasypus novemcinctus, Cabassous tatouay, and Cabassous unicinctus) based on burrow morphometry. We first selected published studies that reported measurements of width, height, and angle of the burrows used by the 4 species of armadillos. Then, using such data we simulated burrow measurements for each of the 4 species of armadillos and we created predictive models through supervised machine learning that were capable of correctly identifying the species of armadillos based on their burrows' morphometry. By using classification algorithms such as Random Forest, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, and Decision Tree C5.0, we achieved the overall accuracy for the classification task by about 71%, including an overall Kappa index by about 61%. Euphractus sexcinctus was the most difficult species to discriminate and classify (approximately 68% of accuracy), whereas C. unicinctus was the easiest to discriminate (approximately 93% of accuracy). We found that it was possible to identify similar-sized armadillos based on the measurements of their burrows described in the literature. Finally, we developed an R function (armadilloID) that automatically identified the 4 species of armadillos using burrow morphology. As the data we used represented all studies that reported the morphometry of burrows for the 4 species of armadillos, we can generalize that our function can predict armadillo species beyond our data.

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Beyond Natural History: Some Thoughts About Research Priorities in the Study of Xenarthrans

Cited by 8 publications

References 32 publications

Identifying hotspots and priority areas for xenarthran research and conservation

Identifying hotspots and priority areas for xenarthran research and conservation

Nine‐banded armadillo (Dasypus novemcinctus) activity patterns are influenced by human activity

Noninvasive Low‐cost Method to Identify Armadillos' Burrows: A Machine Learning Approach

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