bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.03.09.584227

Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience

Cavender-Bares, J.·Grossman, J.·Guzman Quesada, J. A.·Hobbie, S. E.·Kaproth, M.·Kothari, S.·Lapadat, C.·Montgomery, R.·Park, M.
View original source ↗Export metadata ↓

Abstract

O_LIWe introduce a new "low-density" tree diversity experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA aimed at testing long-term ecosystem consequences of tree diversity and composition. The experiment was designed to provide guidance on forest restoration efforts that will simultaneously advance carbon sequestration goals and contribute to biodiversity conservation and sustainability. C_LIO_LIThe new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in the face of global change. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at high density (0.5 m spacing). In addition to lower density plantings (1 m spacing), FAB2 also has larger plots (100 m2 and 400 m2) appropriate for testing long-term ecosystem consequences of species composition and diversity. C_LIO_LIWithin the first six years, mortality in the 400 m2 monoculture plots was significantly higher than in the 100 m2 plots. The highest mortality among any treatments occurred in Tilia americana and Acer negundo monocultures, but mortality for both species decreased with increasing plot diversity. These results highlight the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low-diversity treatments in tree mixture experiments of large extent. C_LIO_LIThe FAB2 experiment provides a long-term platform for discerning the importance of species and lineage effects and of multiple dimensions of diversity in restoring ecosystem functions and services provided by forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral, and hyperspectral sensors, to complement ground-based monitoring of forest function and diversity. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change. C_LI

Source connections

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cavender-Bares, J., Grossman, J., Guzman Quesada, J. A., Hobbie, S. E., Kaproth, M., Kothari, S., Lapadat, C., Montgomery, R., Park, M.. 2024-03-13. Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience. https://doi.org/10.1101/2024.03.09.584227

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗
bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗
bioRxiv2026

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗