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Drones are unoccupied aerial systems (UAS) whose technology has evolved rapidly over the past 15 years. Increasingly used in conservation to manage and monitor biodiversity, drones offer rich capabilities to observe in difficult terrain, have relatively affordable hardware costs and are likely to continue to proliferate rapidly in the years ahead. Drones are useful for tasks as diverse as monitoring wildlife poaching and illegal timber extraction, managing ecotourism and disaster responses, and tracking the regeneration or degradation of forests, and offer potential for more specialised tasks as their sensory payloads are developed. However, although associated technical issues and applications have been explored in wide-ranging ways within conservation science, there has been relatively little social-scientific engagement with drones to date. This leaves a gap surrounding the potential social benefits and risks of drones, as well as in interdisciplinary conversations. This introduction is the first of four papers under the heading ‘Drone ecologies’, building on an interdisciplinary workshop held under the same name at the University of Bristol in July 2021. Expanding from the plenary dialogues that opened this workshop, this introduction explores what interdisciplinary perspectives on drones can offer in addressing global social and ecological challenges, drawing on expertise from the fields of conservation biology, human and physical geography, rainforest ecology and environmental systems. Setting out the aims of the overall special collection, we review here the ways that drones are being used, and might be used, in biodiversity conservation, setting out important considerations to minimise risks of inadvertent harms.

In this policy intervention, we recount the process of producing a policy briefing targeting researchers and practitioners who use drones in biodiversity conservation. We use the writing process as a springboard to think through the ways that interdisciplinary exchange has and might further inform the ethical use of new technologies, such as drones. This approach is vital, we argue, because while drones may be deployed as tools that enable or empower forest, wildlife or habitat monitoring practices, so too can they be variously disruptive, repurposed and/or exceed these applications in significant ways. From questions of surveillance and capture, data ownership and security, to noise disruption, drone use requires careful and critical reflection, particularly in sensitive contexts. Yet, interdisciplinary exchange attentive to the ethical, social and experiential dimensions of drone use remains patchy and thin. To this end, this intervention reflects on the process of a group of scholars from ecological, environmental and social science backgrounds coming together in an interdisciplinary project grappling with diverse issues around responsible conservation drone use. After recounting our methodology, including the surprises and learning that emerged in practice, we contextualise the key themes we chose to foreground in our published policy briefing. We conclude by connecting our collaboration with wider actions and energies in the context of existing (conservation) drone policy and practice, while underscoring our contributions to existing work.

Regenerative agriculture (RA) practices have been promoted as a critical climate change resilience strategy and adaptation solution for smallholder farmers in Sub-Saharan Africa. However, most RA programmes struggle with securing and sustaining high adoption rates with many facing dis-adoption. We used Lenton et al.'s positive tipping points framework to assess the potential for fast and lasting adoption of Regenerative Agriculture (RA) in Sub-Saharan Africa. This involved reviewing literature and combining evidence from the successful expansion of the International Small Group and Tree Planting Program (TIST) in East Africa to examine the conditions and feedback processes that drive RA adoption. We found that the key leverage points for TIST wide and rapid adoption were: (1) the cultivation of reinforcing feedback processes that strengthened the social capital around adoption and (2) elimination of barriers to carbon accreditation. Integrating carbon accreditation protocols as standard in design or review of RA interventions could provide an essential leverage to boost adoption rates. Future studies could explore what drives variations in scaling rates and patterns between the sites to inform more site specific interventions. [ABSTRACT FROM AUTHOR]

Tundra soils are one of the world's largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. The landscape‐scale controls of litter decomposition are poorly understood in tundra ecosystems, which hinders our understanding of the global carbon cycle. We examined the extent to which the thermal sum of surface air temperature, soil moisture and permafrost thaw depth influenced litter mass loss and decomposition rates (k), and at which spatial thresholds an environmental variable becomes a reliable predictor of decomposition, using the Tea Bag Index protocol across a heterogeneous tundra landscape on Qikiqtaruk–Herschel Island, Yukon, Canada. We found greater green tea litter mass loss and faster decomposition rates (k) in wetter areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness and higher surface thermal sums. We also found higher decomposition rates (k) on north‐facing relative to south‐facing aspects at microsites that were wetter rather than warmer. Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at local scales (< 50 m2) better than thermal sum. Surprisingly, there was no strong control of elevation or slope on litter decomposition. Our results reveal that there is considerable scale dependency in the environmental controls of tundra litter decomposition, with moisture playing a greater role than the thermal sum at < 50 m2 scales. Our findings highlight the importance and complexity of microenvironmental controls on litter decomposition in estimates of carbon cycling in a rapidly warming tundra biome. [ABSTRACT FROM AUTHOR]

Pinus edulis Engelm. is a short-stature, drought-tolerant tree species that is abundant in piñon-juniper woodlands throughout semiarid ecosystems of the American Southwest. P. edulis is a model species among ecophysiological disciplines, with considerable research focus given to hydraulic functioning and carbon partitioning relating to mechanisms of tree mortality. Many ecological studies require robust estimates of tree structural traits such as biomass, active sapwood area, and leaf area. We harvested twenty trees from Central New Mexico ranging in size from 1.3 to 22.7 cm root crown diameter (RCD) to derive allometric relationships from measurements of RCD, maximum height, canopy area (CA), aboveground biomass (AGB), sapwood area (AS), and leaf area (AL). Total foliar mass was measured from a subset of individuals and scaled to AL from estimates of leaf mass per area. We report a strong nonlinear relationship to AGB as a function of both RCD and height, whereas CA scaled linearly. Total AS expressed a power relationship with RCD. Both AS and CA exhibited strong linear relationships with AL (R2 = 0.99), whereas RCD increased nonlinearly with AL. We improve on current models by expanding the size range of sampled trees and supplement the existing literature for this species. Study Implications: Land managers need to better understand carbon and water dynamics in changing ecosystems to understand how those ecosystems can be sustainably used now and in the future. This study of two-needle pinon (Pinus edulis Engelm.) trees in New Mexico, USA, uses observations from unoccupied aerial vehicles, field measurements, and harvesting followed by laboratory analysis to develop allometric models for this widespread species. These models can be used to understand plant traits such biomass partitioning and sap flow, which in turn will help scientists and land managers better understand the ecosystem services provided by pinon pine across North America. [ABSTRACT FROM AUTHOR]

Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium sp.) is a major crop grown under rainfed conditions in Vertisols and associated soils in semi-arid tropical (SAT) regions of Peninsular India. In recent years, cotton productivity has declined due to various biophysical factors including pest and diseases, seasonal water stress soil degradation and poor crop management practices. In this study, we compare two methods for evaluating the suitability of Vertisols for cotton in contrasting two agro-ecological regions viz., sub-humid moist (SHM) region and semi-arid dry(SAD) were characterized. Twelve cotton growing Vertisols (seven from SHM and five from SAD) were evaluated for their suitability for cotton cultivation using soil quality index (SQI) and modified Sys-FAO method. SQIs were calculated using the weighted additive index from transformed scores of selected indicators by principal component analysis. For Sys-FAO method both biophysical and soil characteristics were considered for suitability evaluation. We found that the soils of SHM region were moderately suitable for cotton cultivation with soil moisture as the major limiting factor, whereas the soils of SAD region are marginally suitable due to high exchangeable sodium percentage and poor hydraulic conductivity. From this, it may be concluded that the weighted SQI has better agreement with the cotton yield. [ABSTRACT FROM AUTHOR]

Abstract: Lightweight drones have emerged recently as a remote sensing survey tool of choice for ecologists, conservation practitioners and environmental scientists. In published work, there are plentiful details on the parameters and settings used for successful data capture, but in contrast there is a dearth of information describing the operational complexity of drone deployment. Information about the practices of flying in the field, whilst currently lacking, would be useful for others embarking on new drone‐based investigations. As a group of drone‐piloting scientists, we have operated lightweight drones for research in over 25 projects, in over 10 countries, and in polar, desert, coastal and tropical ecosystems, with many hundreds of hours of flying experience between us. The purpose of this paper was to document the lesser‐reported methodological pitfalls of drone deployments so that other scientists can understand the spectrum of considerations that need to be accounted for prior to, and during drone survey flights. Herein, we describe the most common challenges encountered, alongside mitigation and remediation actions that increase the chances of safe and successful data capture. Challenges are grouped into the following categories: (i) pre‐flight planning, (ii) flight operations, (iii) weather, (iv) redundancy, (v) data quality, (vi) batteries. We also discuss the importance of scientists undertaking ethical assessment of their drone practices, to identify and mitigate potential conflicts associated with drone use in particular areas. By sharing our experience, our intention is that the paper will assist those embarking on new drone deployments, increasing the efficacy of acquiring high‐quality data from this new proximal aerial viewpoint. [ABSTRACT FROM AUTHOR]

Ecosystems dominated by low stature vegetation, including grasslands, shrublands and savannahs occupy ca. 35% of the terrestrial environment. There is growing appreciation of the important role these ecosystems play in the provision of globally important ecosystem services including the sequestration and storage of carbon, the provision of forage for livestock and wildlife, and ecosystems responses to disturbances such as fire. Understanding vegetation dynamics at landscape scales requires knowledge of changes in biomass through time and space. A range of approaches have been developed for measuring aboveground biomass, typically using allometry whereby plant dimensions are related to biomass via a limited number of observations from destructive harvests. However, ground-based allometric approaches are often very labour intensive, limiting the spatiotemporal resolution of observations. Consequently, these methods are prone to undersampling in spatially heterogeneous and/or temporally dynamic low stature ecosystems, such as grasslands and shrublands. Recent technological advances have enabled the acquisition of new datasets at fine spatial and temporal grains, for example via aerial images collected using lightweight unmanned aerial vehicles and processed with structure-from-motion (SfM) photogrammetry. Several studies have demonstrated strong relationships between aboveground biomass and remotely sensed vegetation attributes (e.g. Cunliffe et al., 2016). The rise in low cost, do-it-yourself remote sensing has seen the development and adoption of workflows that can be appropriate for specific applications, but which yield datasets and findings that are difficult to compare, hindering cross-site syntheses and the development of transferable understanding about the ecosystem properties retrievable from remote sensing observations.Here we present on a novel global experiment to investigate the transferability of drone-derived allometric relationships through space and time and taxa. Working with an ever-growing global network of drone-equipped scientists and land managers, we have led an experiment that exploits the broad availability of drone technology. Collaborating with diverse groups to collect new data, we have analysed these observations to measure vegetation structure linked with direct biomass observations from accompanying harvest plots. High standards of both quality and comparability are ensured through a standardised and comprehensive sampling protocol (Cunliffe and Anderson, 2019), followed by centralised processing and analysis. As of January 2019, 38 groups are planning data contributions, and eight teams have already collected 16 datasets, consisting of 303 observations (harvest plots) representing 18 different species and/or communities. Our preliminary findings indicating strong coefficients of determination between biomass and remotely sensed canopy attributes (R2 ranging from 0.64 to 0.95), and reveal new insights into the (dis)similarity of these allometric relationships between different taxa. We will share our aspirations of how this new and expanding global network can support further coordinated experiments to advance knowledge of plant trait relationships around our planet and fill the gaps between local and regional scale observations.References:Ahlström et al., (2015) Science, doi:10.1126/science.aaa1668. Poulter et al., (2014) Nature, doi:10.1038/nature13376. Cunliffe et al., (2016) Remote Sensing of Environment, doi:10.1016/j.rse.2016.05.019. Cunliffe and Anderson (2019) Protocol Exchange, doi:10.1038/protex.2018.134 [ABSTRACT FROM AUTHOR]

Drylands cover approximately 30% of the global land area, and the extent of water-limited ecosystems is predicted to increase due to climate change. Although other ecosystems account for a higher percentage of the global mean net ecosystem exchange (NEE), modelling studies suggest that inter-annual variability in global NEE is dominated by semi-arid ecosystems. Dryland ecosystems are extremely sensitive to precipitation. Consequently, high spatial and temporal variability in CO2 fluxes is common in these biomes, driven by precipitation distribution. This high spatiotemporal variability, coupled with an under-sampling of biogenic flux measurements in these biomes relative to more mesic ecosystems, contributes to greater uncertainty in knowledge of dryland carbon fluxes.As part of a new project investigating how Dryland Regions control Interannual Variability In Global Carbon flux (DRIVING-C), we are quantifying the variability in carbon fluxes around two existing semiarid EC sites. These sites are US-SEG, a Bouteloua-dominated grassland and US-Ses, a Larrea tridentata shrubland, both of which have been running continuously since 2007. At each site, a cluster of four low-cost eddy covariance systems were deployed in October 2018 around the existing flux tower, one located ca. 7 m from the existing tower to replicate its measurements, while the remaining three low cost systems were placed ca. 80 m away at equal intervals around a circle. Each low-cost system is composed by a Gill sonic anemometer placed at 6m height, a Vaisala closed path gas analyser for CO2 fluxes and a relative humidity sensor for H2O fluxes. To our knowledge, this deployment of eight low-cost EC in the south west of US (New Mexico, Sevilleta National Wildlife Refuge) represents the first attempt at investigating biogenic fluxes in semi-arid ecosystems over this short length scales (ca. 100's of m). This presentation will describe the low cost system, and share preliminary results from the first six months of intercomparisons between the ten EC systems. The low-cost towers are operating with satisfactory reliability, with all sensors returning reasonable and consistent values. Over the 3.5 year project, our comparisons with higher cost conventional towers will allow us to evaluate the ability of these low-cost systems to quantify spatial variability of biogenic fluxes and hence to validate the representativeness of conventional single towers at this observation scale. Should the low-cost EC demonstrate to be a reliable complement for the traditional system, this low cost technology could be deployed more widely to help address the issue of undersampling in dryland biomes. [ABSTRACT FROM AUTHOR]