skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Spider and caterpillar abundance, tree size, and canopy closure metrics in a tree diversity experiment
This data set includes spider abundances recorded on focal trees in a large-scale forest diversity manipulation at the Smithsonian Environmental Research Center in Edgewater, MD, USA. We repeatedly sampled spiders on 540 trees of 15 species planted in single or mixed species combinations (4 or 12) in June and August of 2019 and 2021. We took caterpillar abundance data, measured tree height, and took canopy closure measurements on each tree in 2021. Data associated with the paper: Positive tree diversity effects on arboreal spider abundance are tied to canopy cover in a forest experiment published in Ecology  more » « less
Award ID(s):
2044406 2044361
PAR ID:
10502602
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
figshare
Date Published:
Subject(s) / Keyword(s):
Ecology not elsewhere classified
Format(s):
Medium: X Size: 117474 Bytes
Size(s):
117474 Bytes
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Human actions are decreasing the diversity and complexity of forests, and a mechanistic understanding of how these changes affect predators is needed to maintain ecosystem services, including pest regulation. Using a large‐scale tree diversity experiment, we investigate how spiders respond to trees growing in plots of single or mixed species combinations (4 or 12) by repeatedly sampling 540 trees spanning 15 species. In 2019 (6 years post‐establishment), spider responses to tree diversity varied by tree species. By 2021, diversity had a more consistently positive effect, with trees in 4‐ or 12‐species plots supporting 23% or 50% more spiders, respectively, compared to conspecifics in monocultures. Spiders showed stronger tree species preferences in late summer, and the positive impact of plot diversity doubled. In early summer, the positive diversity effect was tied to higher canopy cover in diverse plots, leading to higher spider densities. This indirect path strengthened in late summer, with an additional direct effect of plot diversity on spiders. Prey availability was higher in diverse plots but was not tied to spider density. Overall, diverse plots supported more predators, partly by increasing available habitat. Adopting planting strategies focused on species mixtures may better maintain higher trophic levels and ecosystem functions. 
    more » « less
  2. Tanentzap, Andrew J (Ed.)
    The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. 
    more » « less
  3. Abstract Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground‐based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, many classification models only include the most abundant species, leading to biased predictions at broad scales. For example, if only common species are used to train the model, this assumes that these samples are representative across the entire landscape. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. We use a targeted sampling workflow to the Ordway Swisher Biological Station within the US National Ecological Observatory Network (NEON), where traditional forestry plots had identified six canopy tree species with more than 10 individuals at the site. Combining iterative model development with rare species sampling, we extend a training dataset to include 14 species. Using a multi‐temporal hierarchical model, we demonstrate the ability to include species predicted at <1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to 61% accuracy of a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670 000 individual trees. We find distinct patches of forest composed of rarer species at the full‐site scale, highlighting the importance of capturing species diversity in training data. We estimate the relative abundance of 14 species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. For example, we estimate that the dominant species,Pinus palustrisaccounts for c. 28% of predicted stems, with models predicting a range of counts between 160 000 and 210 000 individuals. These maps provide the first estimates of canopy tree diversity within a NEON site to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales. 
    more » « less
  4. null (Ed.)
    Cajander larch (Larix cajanderi Mayr.) forests of the Siberian Arctic are experiencing increased wildfire activity in conjunction with climate warming. These shifts could affect postfire variation in the density and arrangement of trees and understory plant communities. To better understand how understory plant composition, abundance, and diversity vary with tree density, we surveyed understory plant communities and stand characteristics (e.g., canopy cover, active layer depth, and soil organic layer depth) within 25 stands representing a density gradient of similarly-aged larch trees that established following a 1940 fire near Cherskiy, Russia. Understory plant diversity and mean total plant abundance decreased with increased canopy cover. Canopy cover was also the most important variable affecting individual species’ abundances. In general, tall shrubs (e.g., Betula nana subsp. exilis) were more abundant in low-density stands with high light availability, and mosses (e.g., Sanionia spp.) were more abundant in high-density stands with low light availability. These results provide evidence that postfire variation in tree recruitment affects understory plant community composition and diversity as stands mature. Therefore, projected increases in wildfire activity in the Siberian Arctic could have cascading impacts on forest structure and composition in both overstory and understory plant communities. 
    more » « less
  5. Abstract Urban tree canopy cover is often unequally distributed across cities such that more socially vulnerable neighborhoods often have lower tree canopy cover than less socially vulnerable neighborhoods. However, how the diversity and composition of the urban canopy affect the nature of social‐ecological benefits (and burdens), including the urban forest's vulnerability to climate change, remains underexamined. Here, we synthesize tree inventories developed by multiple organizations and present a species‐specific, geolocated database of more than 600,000 urban trees across the 7‐county Minneapolis‐St. Paul (MSP) metropolitan area in the Upper Midwest of the United States. We find that tree diversity across the MSP is variable yet dominated by a few species (e.g.,Fraxinus pennsylvanica,Acer platanoides, andGleditsia triacanthos), contributing to the vulnerability of the MSP urban forest to future climate change and disturbances. In contrast to tree canopy cover, tree diversity was not well predicted by socioeconomic or demographic factors. However, our analysis identified areas where both climate and social vulnerability are high. Our results add to a growing body of literature emphasizing the importance of considering how complex and interacting social and ecological factors drive urban forest diversity and composition when pursuing management objectives. 
    more » « less