Biology subjects

Edwards, D. P.

Publications and source records attributed to Edwards, D. P..

10 recordsLinked to original sources

bioRxiv2025

Beyond species means - the intraspecific contribution to global wood density variation

Wood density is central for estimating vegetation carbon storage and a plant functional trait of great ecological and evolutionary importance. However, the global extent of wood density variation is unclear, especially at the intraspecific level. We assembled the most comprehensive wood density collection to date (GWDD v.2), including 109,626 records from 16,829 plant species across woody life forms and biomes. Using the GWDD v.2, we explored the sources of variation in wood density within individuals, within species, and across environmental gradients. Intraspecific variation accounted for up to 15% of overall wood density variation (sd = 0.068 g cm-3). Sapwood densities varied 50% less than heartwood densities, and branchwood densities varied 30% less than trunkwood densities. Individuals in extreme environments (dry, hot, acidic soils) had higher wood density than conspecifics elsewhere (+0.02 g cm-3, [~]4% of the mean). Intraspecific environmental effects strongly tracked interspecific patterns (r = 0.83) but were only 20-30% as large and varied considerably among taxa. Individual plant wood density was difficult to predict (RMSE > 0.08 g cm-3; single-measurement R2 = 0.59). We recommend (i) systematic within-species sampling for local applications, and (ii) expanded taxonomic coverage combined with integrative models for robust estimates across ecological scales.

plant biology↗

bioRxiv2025

A global map of wood density

Wood density influences how quickly woody plants grow, how long they live and how much carbon they store, yet its global variation remains poorly mapped. Here we combined 109,626 wood density measurements from 16,829 species with 300,949 vegetation plots to produce a km-scale map of community-weighted wood density for every woody biome. Our model led to a prediction accuracy 32-51 % higher than previous global products, and a 1.8-3.7-fold wider wood density range (0.28-1.00 g cm-3; global mean: 0.57 g cm-3) than previously assumed. Spatial cross-validation showed low bias ({+/-}2.5 % of the mean), and uncertainties decreased from 20% in poorly sampled drylands and boreal regions to 5% in data-rich temperate forests. Mean annual temperature was the best predictor of community-weighted mean wood density, increasing by 0.01 g cm-3 for every 1{degrees}C change. We deliver a low-bias, high-resolution wood density layer for Earth system models, together with spatially explicit error maps. This study represents a major step forward for carbon accounting and trait-based forecasts of vegetation change.

plant biology↗

bioRxiv2025

Land-use change undermines the stability of avian functional diversity

Land-use change causes widespread shifts in the composition and functional diversity (FD) of species assemblages, yet the impacts on ecosystem resilience remain uncertain. Stability of ecosystem functioning may increase after land-use change because the most sensitive species are removed, leaving more resilient survivors (Balmford, 1996; Clavel et al., 2010; McKinney & Lockwood, 1999). Alternatively, ecosystems may be destabilized if land-use change reduces functional redundancy, accentuating the ecological impacts of further species loss (Fonseca & Ganade, 2001; McCann, 2000). Current evidence is inconclusive, partly because trait data have not been available to quantify functional stability at sufficient scale. Here, we use morphological measurements of 3696 bird species to estimate shifts in functional redundancy following recent anthropogenic land-use change at 1281 sites worldwide, and then assess the sensitivity of these altered assemblages to future species loss. We find that the proportion of disturbance-tolerant species increases after land-use change, but that this does not increase stability. Instead, we show that functional redundancy is reduced and that further species loss will destabilize ecosystem function because relatively few additional extinctions lead to accelerated losses of FD, particularly in trophic groups delivering important ecological services such as seed dispersal and insect predation. Our analyses reveal that land-use change may have major undetected impacts on the stability of key ecological functions, hindering the capacity of natural ecosystems to absorb further declines in functionality caused by ongoing perturbations.

ecology↗

bioRxiv2024

Structural proteomics defines a sequential priming mechanism for the progesterone receptor

The progesterone receptor (PR) is a steroid-responsive nuclear receptor with two isoforms: PR-A and PR-B. Disruption of PR-A:PR-B signaling is associated with breast cancer through interactions with oncogenic co-regulatory proteins (CoRs). However, molecular details of isoform-specific PR-CoR interactions remain poorly understood. Using structural mass spectrometry, we investigate the sequential binding mechanism of purified full-length PR and intact CoRs, steroid receptor coactivator 3 (SRC3) and p300, as complexes on target DNA. Our findings reveal selective CoR NR-box binding by PR and unique interaction surfaces between PR and CoRs during complex assembly, providing a structural basis for CoR sequential binding on PR. Antagonist-bound PR showed persistent CoR interactions, challenging the classical model of nuclear receptor activation and repression. Collectively, we offer a peptide-level perspective on the organization of the PR transcriptional complex and infer the mechanisms behind the interactions of these proteins, both in active and inactive conformations.

biochemistry↗

bioRxiv2024

Variable responses of individual species to tropical forest degradation

The functional stability of ecosystems depends greatly on interspecific differences in responses to environmental perturbation. However, responses to perturbation are not necessarily invariant among populations of the same species, so intraspecific variation in responses might also contribute. Such inter-population response diversity has recently been shown to occur spatially across species ranges, but we lack estimates of the extent to which individual populations across an entire community might have perturbation responses that vary through time. We assess this using 524 taxa that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in Sabah, Malaysia. Just 39 % of taxa - all with non-significant responses to forest degradation - had invariant responses. All other taxa (61 %) showed significantly different responses to the same forest degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of logging disturbance increased the prevalence of intra-population response diversity, while uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated variable responses in at least 44 % of taxa. Our results show that the responses of individual species to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to the stability of disturbed habitats such as logged tropical forests.

ecology↗

bioRxiv2023

Climate-smart prioritisation of tropical Key Biodiversity Areas for protection in response to widespread temperature novelty.

Key Biodiversity Areas (KBAs) are a cornerstone of 21st century area-based conservation targets. In tropical KBAs, biodiversity is potentially at high risk from climate change, because most species reside within or beneath the canopy, where small increases in temperature can lead to novel climate regimes. We quantify novelty in temperature regimes by modelling hourly temperatures below the forest canopy across tropical KBAs between 1990 and 2019. We find that up to 66% of KBAs with tropical forest are likely to have transitioned to novel temperature regimes. Nevertheless, 34% of KBAs are providing refuge from novelty, 58% of which are not protected. By conducting the first pan-tropical analyses of changes in below-canopy temperatures, we identify KBAs that are acting as climate refugia and should be prioritised as candidates for expansion of the conservation network in response to the post-2020 Global Biodiversity Framework target to conserve 30% of land area by 2030.

ecology↗

bioRxiv2022

Logging alters tropical forest structure, while conversion reduces biodiversity and functioning

The impacts of degradation and deforestation on tropical forests are poorly understood, particularly at landscape scales. We present an extensive ecosystem analysis of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 82 variables categorized into four ecological levels spanning a broad suite of ecosystem properties: 1) structure and environment, 2) species traits, 3) biodiversity, and 4) ecosystem functions. Responses were highly heterogeneous and often complex and non-linear. Variables that were directly impacted by the physical process of timber extraction, such as soil structure, were sensitive to even moderate amounts of logging, whereas measures of biodiversity and ecosystem functioning were generally resilient to logging but more affected by conversion to oil palm plantation. One-Sentence SummaryLogging tropical forest mostly impacts structure while biodiversity and functions are more vulnerable to habitat conversion.

ecology↗

bioRxiv2021

Biogeographic multi-species occupancy models for large-scale survey data

O_LIEcologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi-species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local-scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. C_LIO_LIWe show how this challenge can be overcome by incorporating pre-existing range information into MSOMs, yielding a biogeographic multi-species occupancy model (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey, and entire avian communities in forests and pastures of Colombias West Andes. C_LIO_LICompared to traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species-specific inference even for never-observed species. C_LIO_LIIncorporating pre-existing range data enables principled partial pooling of information across species in large-scale MSOMs. Our biogeographic framework for multi-species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false-absences are modeled in an occupancy framework. C_LI

ecology↗

bioRxiv2020

Detecting tropical selective logging with SAR data requires a time series approach

Selective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, advances in forest monitoring have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but no study has exclusively used SAR data to map tropical selective logging. A detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2 and PALSAR-2 for monitoring tropical selective logging. We built Random Forest models in an effort to classify pixel-based differences in logged and unlogged areas. In addition, we used the BFAST algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. Random Forest classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha-1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately 10% in unlogged forest. Overall our results suggest that SAR data can be used in time series analyses to detect tropical selective logging at high intensity logging locations within the Amazon (> 20 m3 ha-1). These results have important implications for current and future abilities to detect selective logging with freely available SAR data from SAOCOM 1A, the planned continuation missions of Sentinel-1 (C and D), ALOS PALSAR-1 archives (expected to be opened for free access in 2020), and the upcoming launch of NISAR.

ecology↗

bioRxiv2018

A Machine Learning Approach To Map Tropical Selective Logging

Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m3 ha-1). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondonia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (< 15 m3 ha-1). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Para, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes.

ecology↗