Biology subjects

Bullock, J. M.

Publications and source records attributed to Bullock, J. M..

7 recordsLinked to original sources

bioRxiv2026

A global assessment of how biodiversity enhances agroecosystem function

1. Agriculture is the most significant driver of global land-use change, placing increasing pressure on biodiversity. Balancing rising food demand with the need to conserve biodiversity is therefore an urgent challenge, especially as biodiversity can underpin key ecosystem functions that support agricultural systems. A greater general understanding of how biodiversity influences agroecosystem function is critical for developing agricultural practices that benefit both agricultural production and wider ecosystem processes. 2. Using a global dataset of 184 biodiversity-agroecosystem function relationships covering agricultural and silvicultural systems, we developed a classification system for different biodiversity-enhancing approaches, ranging from the planting of multiple crop species to creating tree islands or flower strips, providing the first comprehensive assessment of how these approaches affect agroecosystem function. 3. We found biodiversity enhancement generally had a positive, or at least neutral, association with multiple agroecosystem functions, indicating that it contributes to agricultural sustainability. Crop diversification had the greatest impact on key agroecosystem functions compared to other practices, likely due to the low heterogeneity already present in agricultural systems and the scale at which crop diversification is applied and measured. Overall, biodiversity's largest effect was on multifunctionality, highlighting its role in supporting the simultaneous delivery of multiple agroecosystem functions. 4. Variability in relationships among systems and agroecosystem function categories suggests that no single biodiversity-enhancing approach will universally benefit all functions simultaneously. Instead, a combination of approaches, assessed through their contribution to overall multifunctionality, is likely to be most effective, ensuring that agricultural sustainability claims reflect benefits across systems. 5. Policies should incentivise biodiversity-enhancing management approaches as effective strategies to improve agroecosystem multifunctionality, benefiting both agricultural productivity and the wider ecosystem.

ecology↗

bioRxiv2026

Systematic mapping shows monitoring, evaluation, and engagement are needed to build an evidence base for rewilding

Rewilding is a landscape recovery approach that seeks to enhance functioning of degraded ecosystems by reinstating natural processes and is increasingly being adopted by organisations and landowners. Despite its popularity, there is no accepted way to assess rewildings impacts. This is due in part to inconsistent monitoring and evaluation. Here we report the results from a systematic map of the academic and grey literature on monitoring and evaluation in rewilding case studies. We find that researchers and practitioners are focused on different aspects of rewilding, and that limited recording of case study details and lack of robust experimental design make it difficult to pool and compare data. We identify challenges facing researchers and rewilding practitioners seeking to build a robust evidence base from which to evaluate rewilding outcomes. These challenges include resources as well as spatial and time constraints, which all limit the consistency of data gathering among projects. More comparisons across rewilding projects, better standardisation of data collection and data recording methods, and increased collaboration between researchers and practitioners could go some way to remedy this. We provide recommendations for planning and implementing studies of rewilded landscapes to aid monitoring and evaluation. These include the broad adoption of low-cost and scalable survey designs with greater focus on comparisons across sites, and continued effort towards open-source data storage.

ecology↗

bioRxiv2025

Optimising biodiversity offsetting to account for habitat succession and species colonisation dynamics

Biodiversity offsetting schemes aim to balance habitat loss from development through conservation and restoration, yet their long-term ecological outcomes under repeated impact-offset cycles remain uncertain. Using a conceptual modelling framework based on forest succession, we show that offsetting success hinges on two critical factors: the scale of habitat compensation and the duration of conservation commitments. We show that effective offsetting requires not only restoring areas larger than those impacted, but also sustaining conservation over timeframes that align with the ecological timescales of habitat development. Rapid habitat conversion consistently undermines offset success, particularly since the persistence and recovery of habitat-dependent, colonising species are highly sensitive to how quickly habitats are lost, how long restoration sites are protected, and how much time they need to reach sufficient habitat quality. Moreover, we show that short-term improvements do not necessarily meet long-term biodiversity targets. Given that habitat succession unfolds over decades or more, offset policies must move beyond short-term metrics. We emphasise that offsetting schemes must be scrutinized to ensure sufficient spatial compensation and time for habitat succession. Offsetting schemes should also account for different ecological indicators to avoid the ecological fallacy of short-term restoration success resulting in long-term biodiversity and ecosystem integrity loss.

systems biology↗

bioRxiv2025

The roles of space and food web complexity in mediating ecological recovery

Landscape-scale ecological restoration is a key strategy for halting and reversing biodiversity decline. However, ensuring the long-term sustainability of restoration efforts requires guiding the recovery of complex ecological systems with many interdependent species at a landscape scale. Due to these challenges, our understanding of recovery trajectories remains limited. Using experiments and metapopulation models, we explore how the spatial configuration of communities and food web complexity jointly influence species recovery at different spatial scales. We find that the number and spatial placement of communities affects the colonisation of empty habitat patches, but does not influence population recovery in patches where communities are introduced. Food web complexity reduces the recovery of lower trophic levels, both at population and metapopulation scales. However, this negative effect may be partially mitigated at higher levels of food web complexity. Our results demonstrate that the joint consideration of spatial configuration and species interactions could enhance the effectiveness of restoration actions.

ecology↗

bioRxiv2024

Quantifying relative sponginess: a high-resolution model of landscape water retention as an ecosystem service

Unprecedented climate and land use changes are having major impacts on water-based ecosystem services (ES). It is crucial, therefore, to get an in-depth understanding of current levels of such ES provision, and how they could be impacted by changed environments or management. However, applying existing models for water-related ES pose substantial challenges, which include the need for in-depth specialist hydrological knowledge, the requirement for numerous datasets and parameters that may not be consistently available, and high computational costs. Additionally, there is often a mismatch between the resolution of the model output and parcel-level land management, at which ES information is often most valuable for supporting decision-making. Here we detail a rapid method for assessing water retention potential by estimating an areas sponginess - its capacity to absorb and retain precipitation. Our approach builds upon a topography-adjusted Curve Number methodology, a widely recognised and straightforward tool for estimating water run-off. We calculated run-off across 1 km grid cells (representing field- to farm-scale land parcels) in Great Britain based on the lands sponginess under storm conditions. The primary objective was to investigate the applicability of the method in estimating point-values -- i.e., the independent contribution of each grid square -- within the context of known limitations. The results enable the identification of areas with higher potential for the ES of water retention. Our model output illustrates the spectrum of sponginess across Great Britain, ranging from less than 30% of precipitation retained in city regions to as high as 99% in some rural, agricultural areas. Importantly, we demonstrate that the model is easy to run, can be used with freely-available data, and produces outputs compatible with grid-based models for other ES. Overall, the model provides an accessible approach to estimating the ES of water retention to researchers worldwide, even in data-scarce areas. Highlights- To inform land management decisions it is important to determine parcel-level ecosystem services, but there is a gap for services related to water. - Parcel-level assessments of a lands sponginess - the capacity to retain precipitation - are described. - Values are calculated using a topography-adjusted Curve Number methodology, which requires little data input or hydrological expertise. - Sponginess and run-off estimates are provided for Great Britain at 1 km resolution.

ecology↗

bioRxiv2024

Sensitivities in associating land-system archetypes with sustainability metrics: Insights from simulations

Archetypes of land- and socio-ecological systems, generated using unsupervised classification methods, enable the assimilation of complex environmental and socio-economic information. Such simplification has considerable potential to feed into decision support systems for sustainability planning. But, the usefulness of archetypes depends on how well they relate to sustainability criteria, such as ecosystem service (ES) delivery, that are external to the input datasets employed for archetype generation. Sensitivities in such post-hoc association analyses, and the associated utility of the archetype framework in a decision support context, remain unexplored. Here we emulated post-hoc association analysis procedures using simulated socio-ecological datasets and ES response variables. Our simulations revealed a substantial influence on analysis performance from (1) the number of variables used as inputs in archetype generation, (2) the correlation structure of input datasets, (3) the type and distribution of input variables, and (4) the functional form (linear or non-linear) characterising the relationship between ES variables and their predictors. We observed near-identical performance when archetypes were generated using K-means clustering and Self-Organising Maps (SOMs) - two commonly used archetype classification methods. Further, better archetype classifier performance did not guarantee better discrimination of ES value distributions between archetypes. Our results suggest that designing a framework to generate archetypes for sustainability planning, and the selected methodological choices therein, should place greater emphasis on what the archetypes will be used for in downstream analyses, and not focus solely on archetype classifier performance. This would better ensure the identification of archetypes adaptable to a diverse array of sustainability indicators and sufficiently robust for monitoring decision outcomes over time.

ecology↗

bioRxiv2017

HistMapR: Rapid digitization of historical land-use maps in R

1. Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision.\n\n2. Comparing land use between maps from different time periods allows estimation of the magnitude of habitat change in an area. However, digitizing historical maps manually is time-consuming and analyses of change are usually carried out at small spatial extents or at low resolutions.\n\n3. We developed a method to semi-automatically digitize historical land-use maps using the R environment. We created a number of functions that use the existing raster package to classify land use according to a maps colours, as defined by the RGB channels of the raster image. The method was tested on three different types of historical land-use map and results were compared to manual digitisations.\n\n4. Our method is fast, and agreement with manually-digitised maps of around 80-92% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land-use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.

ecology↗