Biology subjects

Newbold, T.

Publications and source records attributed to Newbold, T..

12 recordsLinked to original sources

bioRxiv2026

Land use and climate warming interact to reshape global avian biodiversity through ecological filtering and homogenisation

Land-use and climate change are dominant drivers of terrestrial biodiversity change, yet are usually analysed separately although they co-occur and may interact. We integrated global bird assemblage records from PREDICTS with temperature anomalies and species' ecological characteristics to test how land use modifies biodiversity responses to climate warming across three dimensions: local abundance and richness, compositional dissimilarity among assemblages, and species occurrence across ecological groups. Land use modified warming associations for both abundance and richness. Relative to primary vegetation, warming associations were more positive in low-intensity agriculture for both responses, but more negative for richness in high-intensity agriculture and urban land. Predicted richness remained below the reference, defined as primary vegetation at temperature anomaly 0, across all modified land uses, with the largest declines in high-intensity agriculture (-17.9%) and urban land (-21.8%). Within land-use classes, greater warming exposure was generally associated with higher compositional similarity, consistent with warming associated biotic homogenisation. Occurrence models revealed ecological filtering: broad-niched generalists were favoured in warmer modified habitats, whereas range-restricted habitat specialists were filtered out under intensive land use and warming. Conservation assessments should therefore integrate land-use-warming interactions with changes in assemblage composition and ecological strategies, particularly in agricultural and urban landscapes where warming further disadvantaged range-restricted specialists.

ecology↗

bioRxiv2026

DAPHNE: A global database of pest herbivores and their natural enemies

Anticipating and managing the impact of herbivorous pests requires evidence-based knowledge about the interactions between pests and their natural enemies. Knowledge of species' ecologies and biological interactions are typically disseminated in unstructured text across hundreds of thousands of scientific articles spanning a rapidly growing scientific literature, but manually screening this literature is time-consuming and impractical at scale. Taxonomic and geographic coverage thus remains sparse, and structured data on interactions between individual species are lacking, particularly for invertebrates. Large language models (LLMs) have recently emerged as a novel and powerful text-mining tool for knowledge extraction and evidence synthesis in a wide range of scientific disciplines, including ecology and conservation. In this study, we use an open-weight, pre-trained LLM to analyse abstracts of a corpus of published material on biological pest control to obtain a global Database of Pest Herbivores and their Natural Enemies (DAPHNE). This method resulted in the extraction of 175,404 species interactions from 112,830 published pest control studies, containing16,755 unique animal taxa resolved at the species level. Interactions include herbivory (granivory, frugivory and gall-formation), predation, parasitism and hyperparasitism, and comprise a range of additional information, such as species' taxonomy, pest status, pest importance, natural enemy importance, provision of biocontrol, associated plants and industries, invasiveness and the vectoring of pathogens and diseases. Validation of the results shows that most data columns were extracted with high recall and precision (>90%) and comparison of DAPHNE with other sources shows a high degree of agreement, but also highlights that the dataset contains information that may have been missed by other sources. In addition to the identification of important crop pests and their natural enemies, DAPHNE may be used for a variety of use-cases, including the identification of invasive species and pathogen vectors, trophic network analysis and literature search and discovery.

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↗

bioRxiv2025

Hard-to-sample species are more sensitive to land-use change: implications for global biodiversity metrics

Land-use change drives biodiversity loss, but some species suffer more than others. Indicators of global biodiversity change must attempt to summarise these impacts representatively and meaningfully, to guide biodiversity recovery. Yet species that are hard to detect, and thus feature less in relevant databases, might possess traits that make them particularly sensitive to anthropogenic impacts. Using global data for plant, bird, and spider species, we develop a statistical approach to analyse and correct for the impact of excluding hard-to-sample species from global biodiversity indicators. Based on over 4000 species with abundance comparisons available, we found that species with fewer global occurrence records consistently decline more as land-use intensity increases, suggesting that hard-to-sample species are particularly sensitive to land-use differences. When we extrapolate this relationship to all plant, bird and spider species with valid occurrence records (0.27 M species), we obtain a more representative global indicator of overall land-use impacts for these entire taxonomic groups. Our estimates indicate a lower average abundance in anthropogenic land uses compared to results obtained when hard-to-sample species are excluded. For example, intensive agriculture only has 18% of the biodiversity level of primary vegetation, rather than the 47% estimated without extrapolation. We recommend that other existing indicators include an extrapolation solution based on ours, to incorporate the available data as effectively as possible. Using occurrence data to predict species sensitivity unlocks many possibilities to improve global biodiversity indicators, without demanding additional data on poorly known species.

ecology↗

bioRxiv2024

Large language models help facilitate the automated synthesis of information on potential pest controllers

The body of ecological literature, which informs much of our knowledge of the global loss of biodiversity, has been experiencing rapid growth in recent decades. The increasing difficulty to synthesise this literature manually has simultaneously resulted in a growing demand for automated text mining methods. Within the domain of deep learning, large language models (LLMs) have been the subject of considerable attention in recent years by virtue of great leaps in progress and a wide range of potential applications, however, quantitative investigation into their potential in ecology has so far been lacking. In this work, we analyse the ability of GPT-4 to extract information about invertebrate pests and pest controllers from abstracts of a body of literature on biological pest control, using a bespoke, zero-shot prompt. Our results show that the performance of GPT-4 is highly competitive with other state-of-the-art tools used for taxonomic named entity recognition and geographic location extraction tasks. On a held-out test set, we show that species and geographic locations are extracted with F1-scores of 99.8% and 95.3%, respectively, and highlight that the model is able to distinguish very effectively between the primary roles of interest (predators, parasitoids and pests). Moreover, we demonstrate the ability of the model to effectively extract and predict taxonomic information across various taxonomic ranks, and to automatically correct spelling mistakes. However, we do report a small number of cases of fabricated information (hallucinations). As a result of the current lack of specialised, pre-trained ecological language models, general-purpose LLMs may provide a promising way forward in ecology. Combined with tailored prompt engineering, such models can be employed for a wide range of text mining tasks in ecology, with the potential to greatly reduce time spent on manual screening and labelling of the literature.

ecology↗

bioRxiv2023

Bumblebee occupancy responds to complex interactions between local and landscape land use, climatic niche properties and climate change

Insect biodiversity is changing rapidly, driven by a complex suite of pressures, foremost among which are human land use, land-use intensification, and increasingly climate change. Bumblebees deliver important pollination services to wild plants and human crops, but we lack large-scale empirical evidence on how land use and climate change interact to drive bumblebee biodiversity changes. We assess bumblebee occupancy responses to interactive effects of land use and climate pressures across North America and Western Europe. Occupancy increases with landscape natural habitat and decreases with the duration of human use of landscapes. Responses to historical climate warming are negative in natural habitats but positive in human land uses, while human land use reduces occupancy most in the centre of species temperature niches. We estimate that the combined pressures have reduced bumblebee occupancy by 61% across sampled natural habitats, and 65% across human land uses, suggesting that treating present-day natural habitats as an undisturbed reference is misleading. Our results can inform efforts to conserve bumblebee biodiversity in the face of ongoing land-use changes and accelerating climatic changes. One-sentence summaryLand use and climate change interact to drive large declines in bumblebee occupancy in both natural and human-modified habitats

ecology↗

bioRxiv2023

Impacts of global food supply on biodiversity via land use and climate change

Land-use change is currently the greatest driver of biodiversity change, with climate change predicted to match or surpass its impacts by mid-century. The global food system is a key driver of both these anthropogenic pressures, thus the development of sustainable food systems will be critical to halting and reversing biodiversity loss. Previous studies of the biodiversity footprint of food tend to focus on land use alone. We use the multi-regional input-output model EXIOBASE to estimate the impacts of biodiversity embedded within the global food system. We build on prior analyses, calculating the impacts of both agricultural land-use and greenhouse gas (GHG) emission footprints for the same two metrics of biodiversity: local species richness and rarity-weighted species richness. Our biodiversity models capture regional variation in the sensitivity of biodiversity both to land-use differences and to climate change. We find that the footprint of land area does not capture the biodiversity impact embedded within trade that is provided by our metric of land-driven species richness change, and that our metric of rarity-weighted richness places a greater emphasis on the biodiversity costs in Central and South America. We find that methane emissions are responsible for 70% of the overall GHG-driven biodiversity footprint and that, in several regions, emissions from a single years food production cause biodiversity loss equivalent to 2% or more of that regions total historic land use. The measures we present are simple to calculate and could be incorporated into decision making and environmental impact assessments by governments and businesses.

ecology↗

bioRxiv2022

Species Richness and Speciation Rates for all Terrestrial Animals Emerge from a Synthesis of Ecological Theories

AO_SCPLOWBSTRACTC_SCPLOWThe total number of species on earth and the rate at which new species are created are fundamental questions for ecology, evolution and conservation. These questions have typically been approached separately, despite their obvious interconnection. In this manuscript we approach both questions in conjunction, for all terrestrial animals, which enables a more holistic integration and generates novel emergent predictions. To do this, we combine two previously unconnected bodies of theory: general ecosystem modelling and individual based ecological neutral theory. General ecosystem models provide us with estimated numbers of individual organisms, separated by functional group and body size. Neutral theory, applied within a guild of functionally similar individuals, connects species richness, speciation rate and number of individual organisms. In combination, for terrestrial endotherms where species numbers are known, they provide us with estimates for speciation rates as a function of body size and diet class. Extrapolating the same rates to guilds of ectotherms enables us to estimate the species richness of those groups, including species yet to be described. We find that speciation rates per species per million years decrease with increasing body size. Rates are also higher for carnivores compared to omnivores or herbivores of the same body size. Our estimate for the total number of terrestrial species of animals is in the range 1.03 - 2.92 million species, a value consistent with estimates from previous studies, despite having used a fundamentally new approach. Perhaps what is most remarkable about these results is that they have been obtained using only limited data from larger endotherms and their speciation rates, with the rest of the predictive process being based on mechanistic theory. This work illustrates the potential of a new approach to classic eco-evolutionary questions, while also adding weight to existing predictions. As we now face an era of dramatic biological change, new methods will be needed to mechanistically model global biodiversity at the species and individual organism level. This will be a huge challenge but the combination of general ecosystem models and neutral theory that we introduce here could be the only way to tractably achieve it.

ecology↗

bioRxiv2019

Land-use and related pressures have reduced biotic integrity more on islands than on mainlands

Tracking progress towards biodiversity targets requires indicators that are sensitive to changes at policy-relevant scales, can easily be aggregated to any spatial scale and are simple to understand. The Biodiversity Intactness Index (BII), which estimates the average abundance of a diverse set of organisms in a given area relative to their reference populations, was proposed in 2005 in response to this need. A new implementation of BII was developed as part of the PREDICTS project in 2016 and has been adopted by GEO BON, IPBES and CBD. The previous global models for BII estimation could not account for pressures having different effects in different settings. Islands are a setting of particular interest: many are home to a disproportionate number of endemic species; oceanic islands may have relatively low overall species diversity because of their isolation; and the pattern and timing of human pressures can be very different from that seen on mainlands. Here, we test whether biotic integrity - as estimated by BII - has decreased more severely on islands than mainlands. We update methods previously used to estimate BII globally (Newbold et al., 2016) to allow pressure effects to differ between islands and mainlands, while also implementing some other recent improvements in modelling. We estimate BII for islands and mainlands by combining global models of how two aspects of biodiversity - overall abundance, and compositional similarity to minimally-impacted sites - have been affected by human pressures. We use these models to project high-resolution ([~]1km2) global maps of BII for the year 2005. We calculate average BII for island and mainland biomes, countries, IPBES regions and biodiversity hotspots; and repeat our analyses using a richness-based version of BII. BII on both islands and mainlands has fallen below the values proposed as safe limits across most regions, biomes and biodiversity hotspots. Our BII estimates are lower than those published in 2016, globally, within all biodiversity hotspots and within most biomes, and show greater spatial heterogeneity; detailed analysis of these differences shows that they arise mostly from a combination of improvements to the modelling framework. Average BII does not strongly differ between islands and mainlands, but richness-based BII has fallen by more on islands. It seems native species are more negatively affected by rising human population density and road development on islands than mainlands, and islands have seen more land conversion. Our results highlight the parlous state of biodiversity native to islands.

ecology↗

bioRxiv2018

Non-linear changes in modelled terrestrial ecosystems subjected to perturbations

When perturbed ecosystems undergo rapid and non-linear changes, this can result in regime shifts to an entirely different ecological state. The need to understand the extent, nature, magnitude and reversibility of these changes is urgent given the profound effects that humans are having on the natural world. It remains very challenging to empirically document non-linear changes and regime shifts within complex, real ecological communities, or even to demonstrate such shifts in simplified experimental systems. General ecosystem models, which simulate the dynamics of entire ecological communities based on a mechanistic representation of ecological processes, provide an alternative and novel way to project ecosystem changes across all scales and trophic levels and to forecast impact thresholds beyond which dramatic or irreversible changes may occur. We model non-linear changes in four terrestrial biomes subjected to human removal of plant biomass, such as occurs through agricultural land-use change. We find that irreversible and non-linear responses are predicted to be common where removal of vegetation exceeds 80% (a level that occurs across nearly 10% of the terrestrial surface), especially for organisms at higher trophic levels and in less productive ecosystems such as drylands. Very large, irreversible changes to the entire ecosystem structure are expected at levels of vegetation removal akin to those in the most intensively used real-world ecosystems. Our results suggest that the projected 21st century rapid increases in agricultural land conversion to feed an expanding human population, may lead to widespread trophic cascades and in some cases irreversible changes to the structure of ecological communities.

ecology↗

bioRxiv2018

Worldwide impacts of past and projected future land-use change on local species richness and the Biodiversity Intactness Index

Although people have modified the world around us throughout human history, the Great Acceleration has seen drivers such as land conversion, exploitation of natural populations, species introductions, pollution and human-induced climate change placing biodiversity under increasing pressure. In this paper we examine 1) how terrestrial species communities have been impacted over the last thousand years of human development and 2) how plausible futures defined by alternative socio-economic scenarios are expected to impact species communities in the future. We use the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) database to model impacts of land-use change and human population on local species richness, community abundance, and biodiversity intactness using a mixed-effects modelling structure. Historical impacts are inferred through projection of model results onto maps of historical land use, provided by the land-use harmonization project, and gridded human population density (HYDE 3.1). Future impacts are explored using the Shared Socio-economic Pathway (SSP) scenarios. These scenarios detail five plausible global futures based upon socio-economic factors such as wealth, population, education, technology, and reliance on fossil fuels, and can be combined with Representative Concentration Pathway (RCP) scenarios to consider climate mitigation strategies. We project model results onto the gridded outputs of six SSP/RCP scenario combinations: SSP1/RCP2.6, SSP2/RCP4.5, SSP3/RCP7.0, SSP4/RCP3.4, SSP4/RCP6.0, and SSP5/RCP8.5. Historical trend lines show that most losses in local biodiversity are relatively recent, with 75% of all loss in both abundance-based Biodiversity Intactness Index and species richness occurring post-1800. Stark regional differences emerge in all future scenarios, with biodiversity in African regions undergoing greater losses than Oceania, North America and the European regions. Although climate change is expected to have severe detrimental impacts to biodiversity - which are not quantified in these results - it is important to consider how the climate change mitigation itself may also impact biodiversity. Our results suggest that strong climate change mitigation through biofuel production will detrimentally impact biodiversity: SSP4/RCP3.4 (with high biofuel mitigation) is predicted to see two times the decrease in abundance-based biodiversity intactness and three times the decrease in local species richness between 2015-2100 as is predicted for SSP4/RCP6.0 (with lower levels of mitigation). SSP4/RCP3.4 forecasts the greatest impact to average local species richness of all the SSP/RCP combinations with an average loss of 13% of local species richness projected to have occurred by 2100. SSP3/RCP7.0 - a scenario describing a globally segregated, and economically protectionist future with low climate change mitigation - has the worst impacts on abundance-based biodiversity intactness with an average loss of 26% of intactness by 2100. However, a brighter future is possible; SSP1/RCP2.6 describes a more sustainable future, where human populations are provided for without further jeopardising environmental integrity - in this scenario we project that biodiversity will recover somewhat, with gains in biodiversity intactness and species richness in many regions of the world by 2100.

ecology↗

bioRxiv2018

Changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001-2012.

Few biodiversity indicators are available that reflect broad-sense biodiversity - rather than particular taxa - at fine spatial and temporal resolution. The Biodiversity Intactness Index (BII) reports how the average abundance of native terrestrial species in a region compares with their abundances before pronounced human impacts. BII is designed for use with data from a wide range of taxa and functional groups and for estimation at any resolution for which data on land use and related pressures are available. For each year from 2001 to 2012 we combined models of how land use and related pressures in tropical and subtropical forested biomes affect overall abundance and compositional similarity with data on anthropogenic pressures. We used these data to produce annual maps of modelled BII at a spatial resolution of 30 arc seconds (roughly 1km at the equator) across tropical and subtropical forested biomes. This is the first time temporal change in BII has been estimated across such a large region. The modelling approach used for compositional similarity is an improvement over that used previously when estimating BII, using data more efficiently. Overall, BII fell by an average of 2.4 percentage points between 2001 and 2012, with 83 countries seeing an average reduction and 45 an average increase, and the extent of primary forest fell by 3.9% over the same period. Changes are not strongly related to countries rates of economic growth over the same period.

ecology↗