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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Global Ecology and Biogeography)
    Abstract Aim

    We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions.

     Location

    Contiguous USA.

     Time period

    2007–2016.

     Taxa studied

    Birds and trees.

     Methods

    We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity.

     Results

    A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with < 10% relative error. Models using geodiversity and climate performed better for local (alpha) and regional (gamma) diversity than for turnover‐based (beta) diversity. Among geodiversity predictors, variability of elevation fitted biodiversity best; interestingly, topographically diverse places tended to have higher tree diversity but lower bird diversity.

     Main conclusions

    Although climatic predictors tended to have larger individual effects than geodiversity, adding geodiversity improved climate‐only models of biodiversity. Geodiversity was correlated with biodiversity more consistently than with climate across ecoregions, but models tended to have a poor fit in ecoregions held out of the training dataset. Patterns of geodiversity could help to prioritize conservation efforts within ecoregions. However, we need to understand the underlying mechanisms more fully before we can build models transferable across ecoregions.

     
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Global Ecology and Biogeography)
    Abstract Issue

    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.

     Evidence

    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.

     Conclusion

    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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  3. ; ; ; ; ; ; ( , Global Change Biology)
    Abstract

    Northern temperate ecosystems are experiencing warmer and more variable winters, trends that are expected to continue into the foreseeable future. Despite this, most studies have focused on climate change impacts during the growing season, particularly when comparing responses across different vegetation cover types. Here we examined how a perennial grassland and adjacent mixed forest ecosystem in New Hampshire, United States, responded to a period of highly variable winters from 2014 through 2017 that included the warmest winter on record to date. In the grassland, record‐breaking temperatures in the winter of 2015/2016 led to a February onset of plant growth and the ecosystem became a sustained carbon sink well before winter ended, taking up roughly 90 g/m2more carbon during the winter to spring transition than in other recorded years. The forest was an unusually large carbon source during the same period. While forest photosynthesis was restricted by leaf‐out phenology, warm winter temperatures caused large pulses of ecosystem respiration that released nearly 230 g C/m2from February through April, more than double the carbon losses during that period in cooler years. These findings suggest that, as winters continue to warm, increases in ecosystem respiration outside the growing season could outpace increases in carbon uptake during a longer growing season, particularly in forests that depend on leaf‐out timing to initiate carbon uptake. In ecosystems with a perennial leaf habit, warming winter temperatures are more likely to increase ecosystem carbon uptake through extension of the active growing season. Our results highlight the importance of understanding relationships among antecedent winter conditions and carbon exchange across land‐cover types to understand how landscape carbon exchange will change under projected climate warming.

     
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  4. ; ; ; ; ( , Environmental Data Initiative)
    This dataset is a compilation of leaf trait measurements for tree species in the northeastern United States collected between 2017 and 2022 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains 1351 samples, including 18 chemical, physical and structural traits collected across 25 different tree species. Traits include stable isotopes for carbon (C) and nitrogen (N), percent C and N, C:N ratio, total chlorophyll (chl), chl a, chl b, chl a:b ratio, leaf mass per area, average leaf dry mass, average leaf area, length, and width, leaf water content, average petiole length and petiole dry mass, and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions have been included. Leaf physiological and optical traits have been measured concurrently on many of these samples and published separately. 
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  5. ; ; ; ; ; ( , Harvard Dataverse)
    LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during leaf-off conditions in November 2022. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 
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  6. ; ; ; ; ; ; ( , Environmental Data Initiative)
    Leaf temperature measurements were collected during the summer of 2020 within forested areas at the Thompson Farm Earth Systems Observatory in Durham, New Hampshire, USA. Located within the property is a registered Ameriflux site, Thompson Farm Forest (US-TFF), as well as experimental throughfall exclusion plots that are part of DroughtNet (experiment running since 2015). Leaf temperature measurements were made within the footprint of the eddy covariance flux tower as well as within both control and throughfall exclusion treatment plots. Upper canopy foliage was accessed using a bucket lift and in situ measurements made using a handheld thermal IR sensor. All data were paired with concurrent meteorological measurements from US-TFF or data from a co-located NOAA CRN station (NH Durham 2 SSW). Additionally, leaf chemical, physical, structure, and physiological traits have been measured at this site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. Specific to this leaf temperature dataset, leaf-level light, temperature, and vpd photosynthetic response curves were measured. 
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  7. ; ; ; ; ; ( , Harvard Dataverse)
    LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during the growing season. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 
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  8. ; ; ; ( , Environmental Data Initiative)
    Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. 
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  9. ; ; ; ; ; ( , Harvard Dataverse)
    Orthorectified flight line hyperspectral cubes retiled for publication. Collectively, the tiled hyperspectral cubes cover the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH. Data were acquired using a Headwall Photonics, Inc. Nano VNIR hyperspectral line scanning imager with 273 bands from 400-1000 nm. The sensor was flown on board a DJI M600 hexacopter at an altitude of ~80 m above the forest canopy, yielding ~6 cm GSD. Flight lines were converted from raw sensor observations to upwelling radiance a using a vendor-supplied radiometric calibration file for the sensor, then converted to reflectance using a calibration tarp with known reflectance. Finally, cubes were orthorectified using a 1m DSM in Headwall’s SpectralView software, mosaicked to individual flight line cubes, then subsequently tiled for publication. 
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  10. ; ; ; ; ( , Nature Plants)
    null (Ed.)
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