Biology subjects

Maynard, D. S.

Publications and source records attributed to Maynard, D. S..

6 recordsLinked to original sources

bioRxiv2026

PAMalytics: a no-code application for structured validation of bioacoustic detections

1. Passive acoustic monitoring (PAM) is increasingly used for ecological research, biodiversity monitoring, assessment, and reporting. Automated species classifiers make it feasible to process large audio datasets but generate numerous detections that often need validation before use in downstream analyses or formal outputs. 2. Method development in PAM has focused on classifier building and downstream models that account for imperfect detection, yet the practical step between these - post-classification validation - remains weakly supported and is often implemented through ad hoc workflows. This increases manual handling, creates scope for transcription or consolidation errors, limits transparency and makes it difficult to document what was reviewed. 3. We introduce PAMalytics, an open-source, no-code, local browser-based application to support post-classification validation as a standardised workflow stage. PAMalytics ingests detections from any classifier, allows users to define how detections are sampled for review, and presents selected detections alongside their spectrograms with audio playback in one unified interface. Sampling strategy and review decisions are tracked alongside reviewer identity improving traceability and reproducibility across the validation workflow. 4. Case studies with Conservation International Cambodia and Imperial College London demonstrate PAMalytics in two validation settings. In Cambodia, gibbon predictions from a large, uneven dataset were sampled within sites, with likely classifier errors prioritised for validation. At Imperial, Amazon bird detections were sampled across each species classifier-confidence range before biodiversity metrics were derived. In both cases, PAMalytics reduced manual handling and validation time. By turning an ad hoc step into an accessible, structured workflow for conservation practitioners, PAMalytics fills a practical gap in the PAM bioacoustics pipeline and strengthens the link between automated detections and evidence used in biodiversity monitoring and reporting.

ecology↗

bioRxiv2026

Trait misalignment risk in North American forests under climate change

Climate change is expected to alter forest community composition and functioning, with consequences for the ecosystem services forests provide. However, most macroecological projections focus on individual species distributions and offer limited insight into whether entire communities will remain functionally compatible with future climatic conditions. Here we quantify the risk that present-day forest communities will become functionally misaligned with projected climates using a trait-based approach. We analysed forest inventory data from more than 42,000 mature plots across the United States and Canada. For each plot we estimated community-weighted means for 24 functional traits describing leaf economics, hydraulic function, wood structure, abiotic tolerances and symbiotic strategies. We modelled relationships between community functional composition and environmental conditions, and used these relationships to estimate the trait profiles most compatible with projected late-century climates (2080-2100). Trait-environment misalignment (TEM) risk was quantified as the multivariate distance between current community trait composition and the trait profile associated with the projected future climate at each location, accounting for covariance among traits and intraspecific trait variation. Projected climatic conditions favour trait combinations associated with greater hydraulic capacity and reduced cold and shade tolerance. However, the magnitude of functional misalignment varies strongly across space. The highest TEM risk occurs in high-latitude and montane conifer forests across western and central North America, whereas many mid-latitude broadleaf and mixed forests show lower risk because projected climatic changes reinforce existing drought-adapted functional strategies. Critically, high species richness was the strongest predictor of reduced risk, reinforcing the importance of biodiversity in buffering against adverse outcomes. Our results suggest that many forests are projected to experience climatic conditions associated with functional strategies that differ from those characterising the current community. By identifying where the largest functional adjustments are implied, this trait-based framework provides a scalable way to pinpoint forests most likely to experience suboptimal climate conditions and to prioritise monitoring and climate-adapted management.

ecology↗

bioRxiv2023

Enhanced local cooling effects of forests across the globe

Forests cool the land surface in warm regions and warm the land surface in cool regions. Because these local temperature buffering effects depend on background climate1,2, increasingly large areas might experience forest cooling effects as the climate warms3-5. Here, using statistical modeling, we quantified changes in the sensitivity of land surface temperatures6 to forest cover change7 from 1988 - 2016, across 15 million km2 of land area. Forests had a local cooling effect on day and night surface temperatures in 86.3% (72 million km2) and 61.6% (5,108 million km2) of the forest area, respectively. This area has increased by 0.3% (0.2 million km2) at day and 4.7% (2 million km2) at night over recent decades. Our study indicates that climate change is enhancing the cooling effect of forests in the short term, underscoring the importance of protecting natural, diverse forest ecosystems in the face of rising global temperatures that threaten ecosystems8,9, human health10,11, and food production12.

ecology↗

bioRxiv2021

Global trade-offs in tree functional traits

AO_SCPLOWBSTRACTC_SCPLOWDue to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. When considering trait relationships across the entire plant kingdom, plant trait frameworks typically must omit traits unique to large woody species, thereby limiting our understanding of how these distinct ecological pressures shape trait relationships in trees. Here, by considering 18 functional traits--reflecting leaf economics, wood structure, tree size, reproduction, and below-ground allocation--we quantify the major axes of variation governing trait expression of trees worldwide. We show that trait variation within and across angiosperms and gymnosperms is captured by two independent processes: one reflecting tree size and competition for light, the other reflecting leaf photosynthetic capacity and nutrient economies. By exploring multidimensional relationships across clusters of traits, we further identify a representative set of seven traits which captures the majority of variation in form and function in trees: maximum tree height, stem conduit diameter, specific leaf area, seed mass, bark thickness, root depth, and wood density. Collectively, this work informs future trait-based research into the functional biogeography of trees, and contributes to our fundamental understanding of the ecological and evolutionary controls on forest biodiversity and productivity worldwide.

ecology↗

bioRxiv2019

Predicting coexistence in experimental ecologicalcommunities

The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model, or analyze the community vastly exceeds what is feasible using current methods. Here, we address this challenge by presenting a statistical approach that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Using three well-studied experimental systems--encompassing plants, protists, and algae with grazers--we show that this method predicts with high accuracy the results of unobserved experiments, while making no assumptions about the dynamics of the systems. These results suggest a fundamentally different study design for building and quantifying experimental systems, requiring a small number of experiments relative to traditional approaches. By providing a scalable method for navigating large systems, this work provides an efficient way to study highly diverse experimental communities.

ecology↗

bioRxiv2017

Predicting Global Forest Reforestation Potential

Executive SummaryForests are important determinants of the carbon cycle, and they provide countless ecosystem services to support billions of people worldwide. Global-scale forest restoration is one of our most effective weapons in the fight against biodiversity loss, rural poverty and climate change. In this report, we generate a spatial map of tree density within the potential forest restoration areas delineated by the IUCN/WRIs \"Atlas of Forest Landscape Restoration Opportunities\" to estimate the potential number of trees that could be restored at a global scale. We also estimate the number of trees that might be saved by avoiding deforestation in currently forested areas.\n\nWe show that the restoration areas have the capacity to support a total of 1.33 trillion trees. However, given that a considerable proportion of these areas already contain forests, we estimate that 589 billion new trees (larger than 10 cm diameter) could be restored within these areas, which would have the potential to store 65-91 Gigatonnes of carbon after reaching forest maturation. These values will increase marginally over time, as deforestation is responsible for the removal of living trees within the restoration areas. However, if only 50% or 25% of the mosaic areas (the largest of the designated restoration types) are available for reforestation, this total number will fall to approximately 360, or 246 billion trees, respectively, with corresponding decreases in potential carbon storage. Given that anthropogenic carbon emissions are currently in the order of 9-12 Gigatonnes per year, effective global-scale restoration might potentially have a valuable impact on global-scale climate mitigation over the rest of this century.\n\nThis report was produced with funding from WWF-UK as part of the Trillion Trees programme with the Wildlife Conservation Society and BirdLife International

ecology↗