About: Models of Eucalypt phenology predict bat population flux

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Models of Eucalypt phenology predict bat population flux
Creator	Eby, Peggy Plowright, Raina Mccallum, Hamish Peel, Alison Giles, John
Source	Medline; PMC
abstract	Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt‐focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86–93% accuracy. Negative binomial models explained 43–53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt‐based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar‐based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape‐scale population movement and spatiotemporal disease dynamics.
has issue date	2016-09-21(xsd:dateTime)
bibo:doi	10.1002/ece3.2382
bibo:pmid	27891217
has license	cc-by
sha1sum (hex)	2ec6274ec2b20f8434d9f24438b76c60f3719a81
schema:url	https://doi.org/10.1002/ece3.2382
resource representing a document's title	Models of Eucalypt phenology predict bat population flux
has PubMed Central identifier	PMC5115174
has PubMed identifier	27891217
schema:publication	Ecol Evol
resource representing a document's body	covid:2ec6274ec2b20f8434d9f24438b76c60f3719a81#body_text
is schema:about of	named entity 'models' named entity 'foraging' named entity 'disease' named entity 'resident' named entity 'spatiotemporal' named entity 'landscape' named entity 'statistical learning' named entity 'fruit' named entity 'predict' named entity 'INDICES' named entity 'SPILLOVER' named entity 'RESIDENT' named entity 'SOUTHEAST' named entity '283' named entity 'PREDICTED' named entity 'RECENT' named entity 'LARGE' named entity 'FOUNDATION' named entity 'modeling' named entity 'population' named entity 'algorithm' named entity 'Fruit' named entity 'emergence' named entity 'data collection' named entity 'Melaleuca' named entity 'reservoir host' named entity 'flying fox' named entity 'nectar' named entity 'overfitting' named entity 'fruit' named entity 'phenology' named entity 'Eucalypt' named entity 'Hendra virus' named entity 'bat' named entity 'Eucalypt' named entity 'fruit bat' named entity 'nectar' named entity 'heterogeneity' named entity 'NDVI' named entity 'vegetation indices' named entity 'Fruit bat' named entity 'MODIS' named entity 'Eucalypt' named entity 'Pteropus poliocephalus' named entity 'central place foragers' named entity 'evolutionary timescales' named entity 'viral dynamics' named entity 'Lowood' named entity 'Australia' named entity 'phenological' named entity 'population dynamics' named entity 'disease ecology' named entity 'viral pathogens' named entity 'data set' named entity 'population distribution' named entity 'bat' named entity 'foraging' named entity 'generalist' named entity 'Durian' named entity 'population structure' named entity 'bat' named entity 'precipitation' named entity 'Pearson's correlation coefficient' named entity 'foraging' named entity 'Eucalypt' named entity 'Fig' named entity 'Africa' named entity 'Viral infections' named entity 'satellite imagery' named entity 'proxy variables' named entity 'Eucalypts' named entity 'viral dynamics' named entity 'Australia' named entity 'foraging' named entity 'nectar' named entity 'eastern Australia' named entity 'bat'

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