172 results found
Sethi SS, Bick A, Ewers RM, et al., 2023, Limits to the accurate and generalizable use of soundscapes to monitor biodiversity, Nature Ecology and Evolution, Vol: 7, Pages: 1373-1378, ISSN: 2397-334X
Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys.
de Lorm TA, Horswill C, Rabaiotti D, et al., 2023, Optimizing the automated recognition of individual animals to support population monitoring, Ecology and Evolution, Vol: 13, ISSN: 2045-7758
Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, noninvasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogs is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S-Pattern, and WildID. As a case study, we consider the African wild dog, Lycaon pictus, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). Our automated image preprocessing has immediate application for expanding monitoring based on image matching. However, the difference in accuracy between population
Sethi S, Ewers RM, Balakrishnan R, 2023, Ecology: correct the digital data divide, NATURE, Vol: 617, Pages: 35-35, ISSN: 0028-0836
Delabre I, Lyons-White J, Melot C, et al., 2023, Should I stay or should I go? Understanding stakeholder dis/engagement for deforestation-free palm oil, BUSINESS STRATEGY AND THE ENVIRONMENT, ISSN: 0964-4733
Mills MB, Malhi Y, Ewers RM, et al., 2023, Tropical forests post-logging are a persistent net carbon source to the atmosphere., Proceedings of the National Academy of Sciences of USA, Vol: 120, Pages: 1-7, ISSN: 0027-8424
Logged and structurally degraded tropical forests are fast becoming one of the most prevalent land-use types throughout the tropics and are routinely assumed to be a net carbon sink because they experience rapid rates of tree regrowth. Yet this assumption is based on forest biomass inventories that record carbon stock recovery but fail to account for the simultaneous losses of carbon from soil and necromass. Here, we used forest plots and an eddy covariance tower to quantify and partition net ecosystem CO2 exchange in Malaysian Borneo, a region that is a hot spot for deforestation and forest degradation. Our data represent the complete carbon budget for tropical forests measured throughout a logging event and subsequent recovery and found that they constitute a substantial and persistent net carbon source. Consistent with existing literature, our study showed a significantly greater woody biomass gain across moderately and heavily logged forests compared with unlogged forests, but this was counteracted by much larger carbon losses from soil organic matter and deadwood in logged forests. We estimate an average carbon source of 1.75 ± 0.94 Mg C ha-1 yr-1 within moderately logged plots and 5.23 ± 1.23 Mg C ha-1 yr-1 in unsustainably logged and severely degraded plots, with emissions continuing at these rates for at least one-decade post-logging. Our data directly contradict the default assumption that recovering logged and degraded tropical forests are net carbon sinks, implying the amount of carbon being sequestered across the world's tropical forests may be considerably lower than currently estimated.
Lewis-Brown E, Jennings N, Mills M, et al., 2023, Comparison of carbon management and emissions of universities that did and did not adopt voluntary carbon offsets, Climate Policy, ISSN: 1469-3062
The urgent need to reduce greenhouse gas emissions, remove carbon from the atmosphere and stabilize natural carbon sinks has led to the development of many carbon management measures, increasingly including voluntary carbon offsets (VCOs). We studied carbon management in universities, institutions with large carbon footprints and considerable influence in climate science and policy fora. However, concerns that VCOs may deter adopters (including universities) from adopting other carbon reduction measures and limit emissions reductions, for example, through moral hazard, have been raised but understudied. We compared the carbon management characteristics (priorities, policies, practices and emissions) of universities that did and did not adopt VCOs. We found adopters measured carbon emissions for longer, and had set targets to reach net zero earlier than had non-adopters. Adopters of VCOs also undertook more carbon management practices in both 2010 and 2020 than non-adopters. We also found that both adopters and non-adopters significantly increased their carbon management practices over the decade studied, but with no difference between groups. Gross CO2 emissions were reduced significantly over time by adopters of VCOs but not by non-adopters, whereas carbon intensity and percentage annual emissions reductions did not relate to adoption status. Consequently, our study showed no indication of mitigation deterrence due to adoption of VCOs at the universities studied. Rather, greater emissions reductions correlated with earlier net zero target dates, and a higher number of policies and carbon management practices. However, our study was constrained to universities that were affiliated with a national environmental network, so research beyond these organizations, and with individuals, would be useful. The survey was voluntary, exposing the study to potential self-selection bias so the findings may not be generalized beyond the study group. Finally, we found the carbon ac
Norman DL, Bischoff PH, Wearn OR, et al., 2023, Can CNN-based species classification generalise across variation in habitat within a camera trap survey?, Methods in Ecology and Evolution, Vol: 14, Pages: 242-251, ISSN: 2041-210X
Camera trap surveys are a popular ecological monitoring tool that produce vast numbers of images making their annotation extremely time-consuming. Advances in machine learning, in the form of convolutional neural networks, have demonstrated potential for automated image classification, reducing processing time. These networks often have a poor ability to generalise, however, which could impact assessments of species in habitats undergoing change.Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes.Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively). Generalisability to new camera locations within a disturbance level was poor (mean F1-score: 0.32). Performance across unseen habitat disturbance levels was worse (mean F1-score: 0.27). Training the network on multiple disturbance levels improved generalisability (mean F1-score on unseen disturbance levels: 0.41). Cropping images to bounding boxes improved overall performance (F1-score: 0.77 vs. 0.47) and generalisability (mean F1-score on unseen disturbance levels: 0.73), but at a cost of losing images that contained animals which the detector failed to detect.These results suggest researchers should consider using an object detector before passing images to a classifier, and an improvement in classification might be seen if labelled images from other studies are added to their training data. Composition of training data was shown to be influential, but including rarer classes did not compromise performance on common classes, providing support for the inclu
Malhi Y, Riutta T, Wearn OR, et al., 2022, Logged tropical forests have amplified and diverse ecosystem energetics, Nature, Vol: 612, Pages: 707-713, ISSN: 0028-0836
Old-growth tropical forests are widely recognized as being immensely important for their biodiversity and high biomass1. Conversely, logged tropical forests are usually characterized as degraded ecosystems2. However, whether logging results in a degradation in ecosystem functions is less clear: shifts in the strength and resilience of key ecosystem processes in large suites of species have rarely been assessed in an ecologically integrated and quantitative framework. Here we adopt an ecosystem energetics lens to gain new insight into the impacts of tropical forest disturbance on a key integrative aspect of ecological function: food pathways and community structure of birds and mammals. We focus on a gradient spanning old-growth and logged forests and oil palm plantations in Borneo. In logged forest there is a 2.5-fold increase in total resource consumption by both birds and mammals compared to that in old-growth forests, probably driven by greater resource accessibility and vegetation palatability. Most principal energetic pathways maintain high species diversity and redundancy, implying maintained resilience. Conversion of logged forest into oil palm plantation results in the collapse of most energetic pathways. Far from being degraded ecosystems, even heavily logged forests can be vibrant and diverse ecosystems with enhanced levels of ecological function.
Nainar A, Walsh RPD, Bidin K, et al., 2022, Baseflow persistence and magnitude in oil palm, logged and primary tropical rainforest catchments in Malaysian Borneo: implications for water management under climate change, Water, Vol: 14, Pages: 1-16, ISSN: 2073-4441
While timber harvesting has plateaued, repeat-logging and conversion into plantations (especially oil palm) are still active in the tropics. The associated hydrological impacts especially pertaining to enhanced runoff, flood, and erosion have been well-studied, but little attention has been given to water resource availability in the humid tropics. In the light of the increasing climate extremes, this paper compared baseflow values and baseflow recession constants (K) between headwater catchments of five differing land-uses in Sabah, Malaysian Borneo, namely primary forest (PF), old growth/virgin jungle reserve (VJR), twice-logged forest with 22 years regeneration (LF2), multiple-logged forest with 8 years regeneration (LF3), and oil palm plantation (OP). Hydrological and meteorological sensors and dataloggers were established in each catchment. Daily discharge was used for computing K via four estimation methods. Catchment ranks in terms of decreasing K were VJR (0.97841), LF3 (0.96692), LF2 (0.90347), PF (0.83886), and OP (0.86756). Catchment ranks in terms of decreasing annual baseflow were PF (1877 mm), LF3 (1265 mm), LF2 (812 mm), VJR (753 mm), and OP (367 mm), corresponding to 68%, 55%, 51%, 42%, and 38% of annual streamflow, respectively. Despite the low K, PF had the highest baseflow magnitude. OP had the fastest baseflow recession and lowest baseflow magnitude. Baseflow persistence decreased with increasing degree of disturbance. K showed strong association to catchment stem density instead of basal area. For dynamic catchments in this study, the Kb3 estimator is recommended based on its lowest combination of coefficient of variation (CoV) and root mean squared error (RMSE) of prediction. For wetter catchments with even shorter recession events, the Kb4 estimator may be considered. Regarding climate change, logging and oil palm agriculture should only be conducted after considering water resource availability. Forests (even degraded ones) should be conserve
Aguirre-Gutierrez J, Berenguer E, Menor IO, et al., 2022, Functional susceptibility of tropical forests to climate change, NATURE ECOLOGY & EVOLUTION, Vol: 6, Pages: 878-+, ISSN: 2397-334X
One of landscape ecology's main goals is to unveil how biodiversity is impacted by habitat transformation. However, the discipline suffers from significant context dependency in observed spatial and temporal trends, hindering progress towards understanding the mechanisms driving species declines and preventing the development of accurate estimates of future biodiversity change. Here, we discuss recent evidence that populations' and species' responses to habitat change at the landscape scale are modulated by factors and processes occurring at macroecological scales, such as historical disturbance rates, distance to geographic range edges, and climatic suitability. We suggest that placing landscape ecology studies in a macroecological lens will help to explain seemingly inconsistent results and will ultimately create better predictive models to help mitigate the biodiversity crisis.
Deere NJ, Bicknell JE, Mitchell SL, et al., 2022, Riparian buffers can help mitigate biodiversity declines in oil palm agriculture, Frontiers in Ecology and the Environment, Vol: 20, Pages: 459-466, ISSN: 1540-9295
Agricultural expansion is a primary driver of biodiversity decline in forested regions of the tropics. Consequently, it is important to understand the conservation value of remnant forests in production landscapes. In a tropical landscape dominated by oil palm (Elaeis guineensis), we characterized faunal communities across eight taxa occurring within riparian forest buffers, which are legally protected alongside rivers, and compared them to nearby recovering logged forest. Buffer width was the main predictor of species richness and abundance, with widths of 40–100 m on each side of the river supporting broadly equivalent levels of biodiversity as compared to logged forest. However, width responses varied markedly among taxa, and buffers often lacked forest-dependent species. Much wider buffers than are currently mandated are needed to safeguard most species. The largest biodiversity gains are achieved by increasing relatively narrow buffers. To provide optimal conservation outcomes in tropical production landscapes, we encourage policy makers to prescribe width requirements for key taxa and different landscape contexts.
Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecolog
Gregory N, Ewers RM, Chung AYC, et al., 2022, Oil palm expansion increases the vectorial capacity of dengue vectors in Malaysian Borneo, PLoS Neglected Tropical Diseases, Vol: 16, ISSN: 1935-2727
Changes in land-use and the associated shifts in environmental conditions can have large effects on the transmission and emergence of mosquito-borne disease. Mosquito-borne disease are particularly sensitive to these changes because mosquito growth, reproduction, survival and susceptibility to infection are all thermally sensitive traits, and land use change dramatically alters local microclimate. Predicting disease transmission under environmental change is increasingly critical for targeting mosquito-borne disease control and for identifying hotspots of disease emergence. Mechanistic models offer a powerful tool for improving these predictions. However, these approaches are limited by the quality and scale of temperature data and the thermal response curves that underlie predictions. Here, we used fine-scale temperature monitoring and a combination of empirical, laboratory and temperature-dependent estimates to estimate the vectorial capacity of Aedes albopictus mosquitoes across a tropical forest-oil palm plantation conversion gradient in Malaysian Borneo. We found that fine-scale differences in temperature between logged forest and oil palm plantation sites were not sufficient to produce differences in temperature-dependent demographic trait estimates using published thermal performance curves. However, when measured under field conditions a key parameter, adult abundance, differed significantly between land-use types, resulting in estimates of vectorial capacity that were 1.5 times higher in plantations than in forests. The prediction that oil palm plantations would support mosquito populations with higher vectorial capacity was robust to uncertainties in our adult survival estimates. These results provide a mechanistic basis for understanding the effects of forest conversion to agriculture on mosquito-borne disease risk, and a framework for interpreting emergent relationships between land-use and disease transmission. As the burden of Ae. albopictus-vectored d
Fornace K, Manin BO, Matthiopoulos J, et al., 2022, A protocol for a longitudinal, observational cohort study of infection and exposure to zoonotic and vector-borne diseases across a land-use gradient in Sabah, Malaysian Borneo: a socio-ecological systems approach [version 1; peer review: 1 approved, 2 approved with reservations], Wellcome Open Research, Vol: 7, ISSN: 2398-502X
Introduction. Landscape changes disrupt environmental, social and biological systems, altering pathogen spillover and transmission risks. This study aims to quantify the impact of specific land management practices on spillover and transmission rates of zoonotic and vector-borne diseases within Malaysian Borneo. This protocol describes a cohort study with integrated ecological sampling to assess how deforestation and agricultural practices impact pathogen flow from wildlife and vector populations to human infection and detection by health facilities. This will focus on malaria, dengue and emerging arboviruses (Chikungunya and Zika), vector-borne diseases with varying contributions of simian reservoirs within this setting. Methods. A prospective longitudinal observational cohort study will be established in communities residing or working within the vicinity of the Stability of Altered Forest Ecosystems (SAFE) Project, a landscape gradient within Malaysian Borneo encompassing different plantation and forest types. The primary outcome of this study will be transmission intensity of selected zoonotic and vector-borne diseases, as quantified by changes in pathogen-specific antibody levels. Exposure will be measured using paired population-based serological surveys conducted at the beginning and end of the two-year cohort study. Secondary outcomes will include the distribution and infection rates of Aedes and Anopheles mosquito vectors, human risk behaviours and clinical cases reported to health facilities. Longitudinal data on human behaviour, contact with wildlife and GPS tracking of mobility patterns will be collected throughout the study period. This will be integrated with entomological surveillance to monitor densities and pathogen infection rates of Aedes and Anopheles mosquitoes relative to land cover. Within surrounding health clinics, continuous health facility surveillance will be used to monitor reported infections and febrile illnesses. Models will be develo
Bowler E, Lefebvre VA, Pfeifer M, et al., 2022, Optimising sampling designs for habitat fragmentation studies, Methods in Ecology and Evolution, Vol: 13, Pages: 217-229, ISSN: 2041-210X
Habitat fragmentation has become one of the largest areas of research in conservation biology. Empirical studies into habitat fragmentation impacts typically measure ecological responses to metrics describing fragmentation processes, for example ‘distance to the nearest forest edge’, ‘forest fragment area’ and ‘landscape habitat amount’. However, these studies often fail to sample across representative ranges of fragmentation metrics characterising the study region. They therefore lack the data to account for correlation among multiple fragmentation metrics and for spatial autocorrelation among sample sites, which reduces the strength of derived predictive models.Here, we draw on approaches used in the mining and soil science industry to develop standardised and repeatable protocols for designing optimised sampling schemes of biodiversity in fragmented landscapes that meet three criteria: the distance between sample sites is maximised to reduce spatial autocorrelation, the full range of values of the metrics of interest are sampled and the confounding effects of correlated metrics are minimised.We show that our computational methods can optimise the placement of sample sites in fragmented landscapes to minimise, and in some cases to entirely avoid, over- or under-sampling of fragmentation metrics. Our method is flexible enough to cater for any continuous (e.g. maps of percentage tree cover) or categorical (e.g. habitat and land use types) fragmentation metric, and to simultaneously handle combinations of multiple fragmentation metrics and habitat types. We implement our methods as open-source code which includes options to mask invalid or inaccessible regions, update designs to adapt to unforeseen constraints in the field and suggest optimal numbers of sample sites for given design criteria.Using a case study landscape, we demonstrate how the approach improves on manually generated sampling designs. We also show that the methods a
Lyons-White J, Mikolo Yobo C, Ewers RM, et al., 2022, Understanding zero deforestation and the High Carbon Stock Approach in a highly forested tropical country, Land Use Policy, Vol: 112, Pages: 1-12, ISSN: 0264-8377
“Zero deforestation” commitments are pledges by companies to avoid deforestation when producing palm oil. Zero deforestation can be implemented using the High Carbon Stock Approach (HCSA), a tool that distinguishes forests from degraded land which can be developed. In highly forested countries like Gabon, zero deforestation may conflict with national economic goals involving palm oil and other agricultural commodities. We investigated perspectives of stakeholders in Gabon about zero deforestation and the HCSA using Critical Systems Heuristics, a systems thinking methodology. In 25 interviews with government, NGOs, companies, and research institutions, and two focus groups with rural communities, we identified three contrasting perspectives on forest conservation and agro industrial development: international, national, and local. Zero deforestation represents an international perspective that marginalises issues from a national perspective. This may produce unintended consequences that undermine the legitimacy of zero deforestation, including conversion of Gabon’s savannahs and disincentives for sustainable business. From a local perspective, zero deforestation is embedded in an agro-industrial vision that may marginalise value judgments concerning forests and traditional livelihoods. Gabon’s National Land Use Plan could help reconcile the three perspectives but requires recognition by international standards. Adapting the HCSA to Gabon’s context should also be considered to promote legitimacy. Research is required to ensure proposed institutional arrangements deliver equitable multi-stakeholder participation in land-use planning. Gabon’s case shows the applicability of zero deforestation to all highly forested countries cannot be assumed. Improved international understanding of national contexts, and flexibility in applying “zero deforestation”, is important for designing effective and equitable international standard
Lyons-White J, Yobo CM, Ewers RM, et al., 2022, Understanding zero deforestation and the High Carbon Stock Approach in a highly forested tropical country, LAND USE POLICY, Vol: 112, ISSN: 0264-8377
Sethi SS, Ewers RM, Jones NS, et al., 2021, Soundscapes predict species occurrence in tropical forests, OIKOS, Vol: 2022, Pages: 1-9, ISSN: 0030-1299
Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point-count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above-ground carbon density – a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large-scale assessments of habitat suitability.
Heath R, Orme DS, Sethi CSL, et al., 2021, How index selection, compression, and recording schedule impact the description of ecological soundscapes, Evolutionary Ecology, Vol: 11, Pages: 13206-13217, ISSN: 0269-7653
Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down-sampling are sometimes employed to reduce data storage and transmission costs. These parameters vary widely between experiments, with the consequences of this variation remaining mostly unknown.We analyse field recordings from North-Eastern Borneo across a gradient of historical land use. We quantify the impact of experimental parameters (MP3 compression, recording length and temporal subsetting) on soundscape descriptors (Analytical Indices and a convolutional neural net derived AudioSet Fingerprint). Both descriptor types were tested for their robustness to parameter alteration and their usability in a soundscape classification task.We find that compression and recording length both drive considerable variation in calculated index values. However, we find that the effects of this variation and temporal subsetting on the performance of classification models is minor: performance is much more strongly determined by acoustic index choice, with Audioset fingerprinting offering substantially greater (12%–16%) levels of classifier accuracy, precision and recall.We advise using the AudioSet Fingerprint in soundscape analysis, finding superior and consistent performance even on small pools of data. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies.
Milodowski DT, Coomes DA, Swinfield T, et al., 2021, The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo, JOURNAL OF APPLIED ECOLOGY, Vol: 58, Pages: 1764-1775, ISSN: 0021-8901
Blundo C, Carilla J, Grau R, et al., 2021, Taking the pulse of Earth's tropical forests using networks of highly distributed plots, BIOLOGICAL CONSERVATION, Vol: 260, ISSN: 0006-3207
Huaraca Huasco W, Riutta T, Girardin CAJ, et al., 2021, Fine root dynamics across pantropical rainforest ecosystems, Global Change Biology, Vol: 27, Pages: 3657-3680, ISSN: 1354-1013
Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old-growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi-deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water-stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting t
Ewers RM, Nathan SKSS, Lee PAK, 2021, African swine fever ravaging Borneo's wild pigs, NATURE, Vol: 593, Pages: 37-37, ISSN: 0028-0836
Boyle MJW, Bishop TR, Luke SH, et al., 2021, Localised climate change defines ant communities in human-modified tropical landscapes, Functional Ecology, Vol: 35, Pages: 1094-1108, ISSN: 0269-8463
Logging and habitat conversion create hotter microclimates in tropical forest landscapes, representing a powerful form of localised anthropogenic climate change. It is widely believed that these emergent conditions are responsible for driving changes in communities of organisms found in modified tropical forests, although the empirical evidence base for this is lacking.Here we investigated how interactions between the physiological traits of genera and the environmental temperatures they experience lead to functional and compositional changes in communities of ants, a key organism in tropical forest ecosystems.We found that the abundance and activity of ant genera along a gradient of forest disturbance in Sabah, Malaysian Borneo, was defined by an interaction between their thermal tolerance (CTmax) and environmental temperature. In more disturbed, warmer habitats, genera with high CTmax had increased relative abundance and functional activity, and those with low CTmax had decreased relative abundance and functional activity.This interaction determined abundance changes between primary and logged forest that differed in daily maximum temperature by a modest 1.1°C, and strengthened as the change in microclimate increased with disturbance. Between habitats that differed by 5.6°C (primary forest to oil palm) and 4.5°C (logged forest to oil palm), a 1°C difference in CTmax among genera led to a 23% and 16% change in relative abundance, and a 22% and 17% difference in functional activity. CTmax was negatively correlated with body size and trophic position, with ants becoming significantly smaller and less predatory as microclimate temperatures increased.Our results provide evidence to support the widely held, but never directly tested, assumption that physiological tolerances underpin the influence of disturbance‐induced microclimate change on the abundance and function of invertebrates in tropical landscapes.
Nunes MH, Jucker T, Riutta T, et al., 2021, Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Nino, Nature Communications, Vol: 12, Pages: 1-11, ISSN: 2041-1723
The past 40 years in Southeast Asia have seen about 50% of lowland rainforests converted to oil palm and other plantations, and much of the remaining forest heavily logged. Little is known about how fragmentation influences recovery and whether climate change will hamper restoration. Here, we use repeat airborne LiDAR surveys spanning the hot and dry 2015-16 El Niño Southern Oscillation event to measure canopy height growth across 3,300 ha of regenerating tropical forests spanning a logging intensity gradient in Malaysian Borneo. We show that the drought led to increased leaf shedding and branch fall. Short forest, regenerating after heavy logging, continued to grow despite higher evaporative demand, except when it was located close to oil palm plantations. Edge effects from the plantations extended over 300 metres into the forests. Forest growth on hilltops and slopes was particularly impacted by the combination of fragmentation and drought, but even riparian forests located within 40 m of oil palm plantations lost canopy height during the drought. Our results suggest that small patches of logged forest within plantation landscapes will be slow to recover, particularly as ENSO events are becoming more frequent.
Gray REJ, Ewers RM, 2021, Monitoring forest phenology in a changing world, Forests, Vol: 12, Pages: 1-24, ISSN: 1999-4907
Plant phenology is strongly interlinked with ecosystem processes and biodiversity. Like many other aspects of ecosystem functioning, it is affected by habitat and climate change, with both global change drivers altering the timings and frequency of phenological events. As such, there has been an increased focus in recent years to monitor phenology in different biomes. A range of approaches for monitoring phenology have been developed to increase our understanding on its role in ecosystems, ranging from the use of satellites and drones to collection traps, each with their own merits and limitations. Here, we outline the trade-offs between methods (spatial resolution, temporal resolution, cost, data processing), and discuss how their use can be optimised in different environments and for different goals. We also emphasise emerging technologies that will be the focus of monitoring in the years to follow and the challenges of monitoring phenology that still need to be addressed. We conclude that there is a need to integrate studies that incorporate multiple monitoring methods, allowing the strengths of one to compensate for the weaknesses of another, with a view to developing robust methods for upscaling phenological observations from point locations to biome and global scales and reconciling data from varied sources and environments. Such developments are needed if we are to accurately quantify the impacts of a changing world on plant phenology.
Aguirre-Gutierrez J, Rifal S, Shenkin A, et al., 2021, Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data, REMOTE SENSING OF ENVIRONMENT, Vol: 252, ISSN: 0034-4257
Banks-Leite C, Ewers R, Folkard-Tapp H, et al., 2020, Countering the effects of habitat loss, fragmentation, and degradation through habitat restoration, One Earth, Vol: 3, Pages: 672-676, ISSN: 2590-3322
Habitat loss, fragmentation and degradation impacts are the most direct threat to global biodiversity. In this Primer, we discuss how these three forms of habitat transformation are inextricably intertwined, and how their effects on biodiversity and ecosystems are often context-specific. We draw on recent analyses that have explored this context-dependence directly, to discuss how local-scale impacts of habitat transformation are mediated by biogeographic-scale variation in evolutionary histories and species’ geographic ranges. We also discuss how changes to ecosystem functions and services in modified habitats can be just as context-dependent – and how these changes are further obscured by high levels of ecological redundancy in species functions, which can confer resilience to habitat transformation. To avoid the impending extinction of millions of species, it is crucial that the impacts of habitat transformation are mitigated through a combination of preventing further habitat loss while simultaneously extending and repairing the habitats that remain.
Sethi S, Ewers R, Jones N, et al., 2020, SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo, Methods in Ecology and Evolution, Vol: 11, Pages: 1182-1185, ISSN: 2041-210X
1. Automated monitoring approaches offer an avenue to unlocking large‐scale insight into how ecosystems respond to human pressures. However, since data collection and data analyses are often treated independently, there are currently no open‐source examples of end‐to‐end, real‐time ecological monitoring networks. 2. Here, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real‐time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public‐facing website.3. We provide the open‐source code and design of our monitoring devices, the central web2py database, and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real‐time analyses of the eco‐acoustic data. 4. By detailing a fully functional, open source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology.
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