@article{1a9a838a254b43f789dc8349f3434f8b,
title = "Multi-scale biodiversity drives temporal variability in macrosystems",
abstract = "High temporal variability in environmental conditions, populations, and ecological communities can result in species extinctions and outbreaks of agricultural pests and disease vectors, as well as impact industries dependent on reliable provisioning of ecosystem services. Yet few empirical studies have focused on testing hypotheses about the drivers of ecological temporal variability at large spatial and temporal scales. Using decadal datasets that span aquatic and terrestrial macrosystems and structural equation modeling, we show that local temporal variability and spatial synchrony increase temporal variability for entire macrosystems. These mechanisms are influenced by environmental heterogeneity, habitat-level species diversity, spatial scale, and the size of the regional species pool. This analysis is among the first to provide a quantitative argument for the value of regional species diversity. Moreover, our conceptual model is generalizable and may help guide management efforts to reduce temporal variability for conservation or service provisioning in other macrosystems.",
author = "Patrick, {Christopher J.} and McCluney, {Kevin E.} and Albert Ruhi and Andrew Gregory and John Sabo and Thorp, {James H.}",
note = "Funding Information: Publication of this Special Issue was funded by the US National Science Foundation (NSF award number DEB 1928375). Thanks to funding from Bowling Green State University in support of a working group meeting at Arizona State University in 2016 for this project. Preparation of this manuscript was aided by two NSF Macrosystems Biology program grants (1442595, 1926565) to JHT, CJP, and KEM and colleagues, and an early career fellowship awarded to CJP from the National Academy of Science, Engineering, and Medicine, Gulf Research Program. AR was supported by US Department of Agriculture National Institute of Food and Agriculture NC-1189 “Understanding the Ecological and Social Constraints to Achieving Sustainable Fisheries Resource Policy and Management”, and by University of California–Berkeley new faculty start-up funds. Funding Information: Publication of this Special Issue was funded by the US National Science Foundation (NSF award number DEB 1928375). Thanks to funding from Bowling Green State University in support of a working group meeting at Arizona State University in 2016 for this project. Preparation of this manuscript was aided by two NSF Macrosystems Biology program grants (1442595, 1926565) to JHT, CJP, and KEM and colleagues, and an early career fellowship awarded to CJP from the National Academy of Science, Engineering, and Medicine, Gulf Research Program. AR was supported by US Department of Agriculture National Institute of Food and Agriculture NC‐1189 “Understanding the Ecological and Social Constraints to Achieving Sustainable Fisheries Resource Policy and Management”, and by University of California–Berkeley new faculty start‐up funds. Funding Information: As the use of big data in ecology continues to advance, there are a growing number of datasets that cover increasingly larger spatial and temporal scales. These expansive datasets offer new opportunities. For example, the proliferation of affordable remote‐sensing data at increasingly high frequencies and broad scales offers a powerful resource for evaluating patterns of variability and spatial synchrony in vegetation dynamics across a wide range of spatial scales. Existing publicly funded programs focused on boots‐on‐the‐ground research provide another source for valuable long‐term and large‐scale data. For instance, coastal monitoring programs funded by state and federal agencies (eg the National Estuarine Research Reserve Network) provide access to decades of high‐frequency data on coastal processes from dozens of sites along the US coastline. Similarly, the US National Science Foundation (NSF)‐funded Long Term Ecological Research Network and Lotic Intersite Nitrogen Experiment programs have amassed numerous macrosystem‐level datasets. Because of the size, distribution, and longevity of those projects, they offer an opportunity for examining macrosystem processes. More recently, in 2012, NSF developed the National Ecological Observatory Network (NEON) to characterize long‐term ecological changes at large scales, by integrating local‐ to continental‐scale measurements at 20 core terrestrial and 20 core aquatic sites, supplemented by 41 relocatable sites. Data generated from these efforts would be especially useful for understanding how macrosystem processes like metacoupling and teleconnections influence temporal variability (see Tromboni . [ 2021 ]). For example, knowledge of migratory patterns of waterfowl along the Atlantic, Mississippi, Central, and Pacific flyways could provide information on long distance telecommunications for both avian and invertebrate taxa (eg fairy shrimp) that can travel on the feathers or in the guts of migratory birds, or by wind. Dispersal via these pathways may influence stability relationships within ephemeral wetlands (O{\textquoteright}Neill and Thorp 2014 ). Combining multiple sources of data can improve knowledge about the relative importance of drivers of temporal variability, including dispersal and climate, from local to continental scales. The approaches we describe here could be applied to data collected on plants, animals, soil, nutrients, biogeochemistry, and atmospheric characteristics across multiple sites, identifying important controls of variability at different spatiotemporal scales for a wide range of ecosystems. et al Publisher Copyright: {\textcopyright} 2021 The Authors. Frontiers in Ecology and the Environment published by Wiley Periodicals LLC on behalf of the Ecological Society of America",
year = "2021",
month = feb,
doi = "10.1002/fee.2297",
language = "English (US)",
volume = "19",
pages = "47--56",
journal = "Frontiers in Ecology and the Environment",
issn = "1540-9295",
publisher = "Ecological Society of America",
number = "1",
}