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1. Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

   https://doi.org/10.1002/rse2.180  

   Havrilla, Caroline A. ; Villarreal, Miguel L. ; DiBiase, Jacob L. ; Duniway, Michael C. ; Barger, Nichole N. ; Horning, ed., Ned ; Moat, ed., Justin ( September 2020 , Remote Sensing in Ecology and Conservation)

   Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

    

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2. Identifying weather regimes for regional‐scale stochastic weather generators

   https://doi.org/10.1002/joc.6969  

   Najibi, Nasser ; Mukhopadhyay, Sudarshana ; Steinschneider, Scott ( December 2020 , International Journal of Climatology)

   Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs.K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.

    

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3. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing

   https://doi.org/10.1111/geb.12887  

   Zarnetske, Phoebe L. ; Read, Quentin D. ; Record, Sydne ; Gaddis, Keith D. ; Pau, Stephanie ; Hobi, Martina L. ; Malone, Sparkle L. ; Costanza, Jennifer ; M. Dahlin, Kyla ; Latimer, Andrew M. ; et al ( February 2019 , Global Ecology and Biogeography)

   Geodiversity (i.e., the variation in Earth's abiotic processes and features) has strong effects on biodiversity patterns. However, major gaps remain in our understanding of how relationships between biodiversity and geodiversity vary over space and time. Biodiversity data are globally sparse and concentrated in particular regions. In contrast, many forms of geodiversity can be measured continuously across the globe with satellite remote sensing. Satellite remote sensing directly measures environmental variables with grain sizes as small as tens of metres and can therefore elucidate biodiversity–geodiversity relationships across scales.

   We show how one important geodiversity variable, elevation, relates to alpha, beta and gamma taxonomic diversity of trees across spatial scales. We use elevation from NASA's Shuttle Radar Topography Mission (SRTM) andc. 16,000 Forest Inventory and Analysis plots to quantify spatial scaling relationships between biodiversity and geodiversity with generalized linear models (for alpha and gamma diversity) and beta regression (for beta diversity) across five spatial grains ranging from 5 to 100 km. We illustrate different relationships depending on the form of diversity; beta and gamma diversity show the strongest relationship with variation in elevation.

   With the onset of climate change, it is more important than ever to examine geodiversity for its potential to foster biodiversity. Widely available satellite remotely sensed geodiversity data offer an important and expanding suite of measurements for understanding and predicting changes in different forms of biodiversity across scales. Interdisciplinary research teams spanning biodiversity, geoscience and remote sensing are well poised to advance understanding of biodiversity–geodiversity relationships across scales and guide the conservation of nature.

    

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4. Addressing the problem of scale that emerges with habitat fragmentation

   https://doi.org/10.1111/geb.13658  

   Fletcher, Jr., Robert J. ; Betts, Matthew G. ; Damschen, Ellen I. ; Hefley, Trevor J. ; Hightower, Jessica ; Smith, Thomas A. H. ; Fortin, Marie‐Josée ; Haddad, Nick M. ( March 2023 , Global Ecology and Biogeography)

   Although habitat loss has well‐known impacts on biodiversity, the effects of habitat fragmentation remain intensely debated. It is often argued that the effects of habitat fragmentation, or the breaking apart of habitat for a given habitat amount, can be understood only at the scale of entire landscapes composed of multiple habitat patches. Yet, fragmentation also impacts the size, isolation and habitat edge for individual patches within landscapes. Addressing the problem of scale on fragmentation effects is crucial for resolving how fragmentation impacts biodiversity.

   We build upon scaling concepts in ecology to describe a framework that emphasizes three “dimensions” of scale in habitat fragmentation research: the scales of phenomena (or mechanisms), sampling and analysis. Using this framework, we identify ongoing challenges and provide guidance for advancing the science of fragmentation.

   We show that patch‐ and landscape‐scale patterns arising from habitat fragmentation for a given amount of habitat are fundamentally related, leading to interdependencies among expected patterns arising from different scales of phenomena. Aggregation of information when increasing the grain of sampling (e.g., from patch to landscape) creates challenges owing to biases created from the modifiable areal unit problem. Consequently, we recommend that sampling strategies use the finest grain that captures potential underlying mechanisms (e.g., plot or patch). Study designs that can capture phenomena operating at multiple spatial extents offer the most promise for understanding the effects of fragmentation and its underlying mechanisms. By embracing the interrelationships among scales, we expect more rapid advances in our understanding of habitat fragmentation.

    

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5. Counterintuitive scaling between population abundance and local density: Implications for modelling transmission of infectious diseases in bat populations

   https://doi.org/10.1111/1365-2656.13634  

   Lunn, Tamika J. ; Peel, Alison J. ; Eby, Peggy ; Brooks, Remy ; Plowright, Raina K. ; Kessler, Maureen K. ; McCallum, Hamish ( November 2021 , Journal of Animal Ecology)

   Models of host–pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density.

   Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogeneous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host–pathogen interactions is not routinely evaluated.

   We collected a 13‐month dataset of tree‐roostingPteropusspp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree‐level measures of abundance and density—the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost level, subplot level, tree level) are predictive of these local density dynamics.

   Roost‐level features were not representative of tree‐level abundance (bats per tree) or tree‐level density (bats per m²or m³), with roost‐level models explaining minimal variation in tree‐level measures. Total roost abundance itself was either not a significant predictor (tree‐level 3D density) or only weakly predictive (tree‐level abundance).

   This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.

   Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host–pathogen models, rather than simply modifying the transmission function.

    

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