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Abstract AimAbundance–occupancy relationships posit that more locally abundant species occupy more sites than less abundant species. Although widely supported, the occurrence and detection of abundance–occupancy relationships is sensitive to sampling and detection processes. Data from large‐scale standardized sampling efforts are key to address abundance–occupancy relationships. We aimed to use such a dataset to evaluate the occurrence of abundance–occupancy relationships across different spatial grains and over time for aquatic and terrestrial taxa. LocationUSA. Time period2014–2019. Major taxa studiedBirds, mammals, beetles, ticks, fishes, macroinvertebrates and zooplankton. MethodsSpecies abundance and occupancy data were obtained from the National Ecological Observatory Network (NEON). Species mean abundance and occupancy (fraction of sampled locations that were occupied) were estimated for three different spatial grains (i.e., plot, site and domain) for all years sampled. Linear models were used to explore the consistency of interspecific abundance–occupancy relationships. The slope coefficients of these models were related to temporal and spatial variables and to species richness while controlling for taxa in a linear mixed‐effects model (LMM) framework. ResultsWe found evidence for positive abundance–occupancy relationships across the three spatial grains and over time for all taxa we studied. However, our linear models had low explanatory power, suggesting that relationships, although general, were weak. Abundance–occupancy relationships were slightly stronger at the smallest spatial grain than at the largest spatial grain, but showed no detectable change over time for any taxa. Finally, species richness was not associated with the strength of these relationships. Main conclusionsTogether, our results suggest that positive interspecific abundance–occupancy relationships are fairly general but are not capable of explaining substantial variation in spatial patterns of abundance, and that other factors, such as species traits and niche, are also likely to influence these relationships.
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Occupancy models reveal regional differences in detectability and improve relative abundance estimations in fossil pollen assemblages.
The late Quaternary fossil record provides crucial data that demonstrate how organisms respond to climate change. These records have been used to great effect, demonstrating that no-analog communities frequently occur during periods of no-analog climate, and that taxa demonstrate individualistic responses to change. However, our efforts to reconstruct biotic responses to environmental change are frequently hampered by inconsistent sampling and differential preservation of fossil taxa. We leveraged occupancy modeling methods and the fossil pollen record across eastern North America to identify circumstances under which occupancy modeling improves our ability to estimate relative abundance in four pollen taxa (Cornus, Fagus, Picea, and Pinus) through time (15 kya to present) and to identify localities where data are unreliable reflections of the local community. We found that integrating observed pollen abundance and detectability improves model performance. Low genus richness and large basin area were consistently important determinants of low detection. Our occupancy models were most informative for taxa with high enough variation in observed pollen abundance for models to be adequately calibrated. We combined occupancy model estimates of pollen abundance and detectability with a Getis-Ord statistical approach to identify spatial clusters of high or low detectability, identifying regions where a taxon’s pollen is more (or less) reliable. This work will advance the integration of ecological and paleontological sciences by allowing us to better identify whether a pollen taxon is truly absent from a fossil site or if it has simply gone undetected, allowing us to produce more robust paleoecological models. This approach will bolster our ability to identify the responses of plant communities to past climatic and anthropogenic change so that we can improve our predictions of future responses.
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- PAR ID:
- 10212296
- Date Published:
- Journal Name:
- Quaternary science reviews
- Volume:
- 253
- ISSN:
- 0277-3791
- Page Range / eLocation ID:
- 106747
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.quascirev.2020.106747
