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The untapped potential of camera traps for farmland biodiversity monitoring: current practice and outstanding agroecological questions 相机陷阱用于农田生物多样性监测的未开发潜力:当前实践和突出的农业生态问题
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-12-13 DOI: 10.1002/rse2.426
Stephanie Roilo, Tim R. Hofmeester, Magali Frauendorf, Anna Widén, Anna F. Cord
Agroecosystems are experiencing a biodiversity crisis. Biodiversity monitoring is needed to inform conservation, but existing monitoring schemes lack standardisation and are biased towards birds, insects and plants. Automated monitoring techniques offer a promising solution, but while passive acoustic monitoring and remote sensing are increasingly used, the potential of camera traps (CTs) in farmland remains underexplored. We reviewed CT publications from the last 30 years and found only 59 articles that sampled farmland habitats in Europe. The main research topics addressed management or (avian) conservation issues, such as monitoring wildlife‐livestock interactions, nest predation, and the use of feeders and water troughs. Fewer studies employed landscape‐wide approaches to investigate species' habitat use or activity patterns over large agricultural areas. We discuss existing barriers to a more widespread use of CTs in farmland and suggest strategies to overcome them: boxed CTs tailored for small mammals, reptiles and amphibians, perch‐mounted CTs for raptor monitoring and time‐lapse imagery can help in overcoming the technical challenges of monitoring (small) elusive species in open habitats where misfires and missed detections are more frequent. Such approaches would also expand the taxonomic coverage of farmland monitoring schemes towards under‐surveyed species and species groups. Moreover, the engagement of farmers in CT‐based biodiversity monitoring programmes and advances in computer vision for image classification provide opportunities for low‐cost, broad‐scale and automated monitoring schemes. Research priorities that could be tackled through such CT applications include basic science topics such as unravelling animal space use in agricultural landscapes, and how this is influenced by varying agricultural practices. Management‐related research priorities relate to crop damage and livestock predation by wildlife, disease transmission between wildlife and livestock, effects of agrochemicals on wildlife, and the monitoring and assessment of conservation measures. Altogether, CTs hold great, yet unexplored, potential to advance agroecological research.
农业生态系统正在经历一场生物多样性危机。需要对生物多样性进行监测,以便为保护工作提供信息,但现有的监测计划缺乏标准化,而且偏重于鸟类、昆虫和植物。自动监测技术提供了一种前景广阔的解决方案,但在被动声学监测和遥感技术得到越来越多应用的同时,农田中相机陷阱(CT)的潜力仍未得到充分挖掘。我们查阅了过去 30 年的 CT 出版物,发现只有 59 篇文章对欧洲的农田栖息地进行了采样。主要研究课题涉及管理或(鸟类)保护问题,如监测野生动物与家畜的相互作用、巢穴捕食以及喂食器和水槽的使用。较少研究采用全景观方法来调查物种在大面积农业区的栖息地利用或活动模式。我们讨论了在农田中更广泛地使用 CT 的现有障碍,并提出了克服这些障碍的策略:为小型哺乳动物、爬行动物和两栖动物量身定制的盒式 CT,用于猛禽监测的栖架安装式 CT,以及延时成像,都有助于克服在开放的栖息地监测(小型)难以捉摸的物种所面临的技术挑战,因为在开放的栖息地,误射和漏检更为频繁。这些方法还将扩大农田监测计划的分类覆盖范围,使其涵盖调查不足的物种和物种群。此外,农民对基于 CT 的生物多样性监测计划的参与以及用于图像分类的计算机视觉技术的进步为低成本、大规模和自动化监测计划提供了机会。可通过此类 CT 应用解决的研究重点包括基础科学课题,如了解农业景观中动物空间的使用情况,以及不同农业实践对其产生的影响。与管理相关的研究重点涉及野生动物对作物的破坏和对牲畜的捕食、野生动物和牲畜之间的疾病传播、农用化学品对野生动物的影响以及对保护措施的监测和评估。总之,CT 在推动生态农业研究方面具有巨大的潜力,但尚未得到开发。
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引用次数: 0
Quantifying range‐ and topographical biases in weather surveillance radar measures of migratory bird activity 量化气象监测雷达对候鸟活动测量的范围和地形偏差
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-12-13 DOI: 10.1002/rse2.423
Miguel F. Jimenez, Birgen Haest, Ali Khalighifar, Annika L. Abbott, Abigail Feuka, Aitao Liu, Kyle G. Horton
Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. Here, we assess these biases with relation to the Cheyenne, WY Next Generation Weather Radar (NEXRAD) site, one of the large‐scale radars in a network of 160 weather surveillance stations based in the United States. We compared local density measures collected using a mobile, vertically looking radar with reflectivity from the NEXRAD station in the corresponding area. Both mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated (r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage. Range‐corrected NEXRAD reflectivity was a stronger predictor of observed mobile radar densities than uncorrected reflectivity at the mean nightly scale, suggesting that current range correction methods are somewhat effective at correcting for this bias. At the within night temporal scale, corrected and uncorrected reflectivity models performed similarly up to 65 km, but beyond this distance, uncorrected reflectivity became a stronger predictor than range‐corrected reflectivity, suggesting range limitations to these corrections. Together, our findings further validate weather radar as an ornithological tool, but also highlight and quantify potential sampling biases.
气象雷达系统已成为研究夜间鸟类迁徙的核心工具。然而,虽然有研究试图通过与其他取样技术的比较来验证天气雷达数据,但很少有研究明确研究了范围和地形阻挡对取样探测的影响--这些关键因素可能会使更广泛的推论产生偏差。在这里,我们结合怀俄明州夏安下一代天气雷达(NEXRAD)站点来评估这些偏差,该站点是美国 160 个天气监测站网络中的大型雷达之一。我们将使用移动式垂直观测雷达收集的当地密度测量值与 NEXRAD 站在相应地区的反射率进行了比较。NEXRAD 和移动雷达之间的平均夜间迁徙活动和夜间迁徙活动都有很强的相关性(r = 0.85 和 0.70),但这种关系随着距离和波束阻挡的增加而减弱。在平均夜间尺度上,经测距校正的 NEXRAD 反射率比未经校正的反射率更能预测观测到的移动雷达密度,这表明目前的测距校正方法在一定程度上有效地校正了这种偏差。在夜间时间尺度内,校正和未校正反射率模型在 65 千米范围内的表现类似,但在此距离之外,未校正反射率比测距校正反射率的预测能力更强,这表明这些校正方法存在测距限制。总之,我们的研究结果进一步验证了天气雷达作为鸟类学工具的有效性,同时也强调并量化了潜在的取样偏差。
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引用次数: 0
A random encounter model for wildlife density estimation with vertically oriented camera traps 垂直方向相机陷阱野生动物密度估计的随机相遇模型
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-12-02 DOI: 10.1002/rse2.427
Shuiqing He, J. Marcus Rowcliffe, Hanzhe Lin, Chris Carbone, Yorick Liefting, Shyam K. Thapa, Bishnu P. Shrestha, Patrick A. Jansen
The random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables of the animals (average group size, speed and activity level) and characteristics of camera sensors. The REM has been widely used for setups in which cameras are mounted on trees or other structures aimed parallel to the ground. Here, we modify the REM formula to accommodate an alternative field of view acquired with vertically oriented camera traps, a type of deployment used to avoid camera theft and damage. We show how the calculations can be adapted to account for a different detection zone with minor modifications. We find that the effective detection area can be close to a rectangle with dimensions influenced by the properties of the Fresnel lens of the camera's motion sensor, the body mass of different species and the height of the camera. The other REM parameters remain the same. We tested the modified REM (vREM) by applying it to wildlife data collected with vertically oriented camera traps in Bardia National Park, Nepal. We further validated that the effective detection area for the camera model used was best approximated as a rectangle shape using maximum likelihood estimation. Density estimates obtained broadly matched independent density estimates for nine species from the previous studies in Bardia with varying body sizes by four orders of magnitude. We conclude that these modifications allow the REM to be effectively used for mammal density estimation for species with a wide range of body sizes, with vertically oriented camera traps.
随机遭遇模型(REM)通过校正动物的一组生物变量(平均群体大小、速度和活动水平)和相机传感器的特征的捕获率,从相机陷阱数据中估计动物密度。REM被广泛用于将摄像机安装在树木或其他与地面平行的结构上。在这里,我们修改了REM公式,以适应垂直定向相机陷阱获得的另一种视野,这是一种用于避免相机被盗和损坏的部署类型。我们展示了如何通过微小的修改来调整计算以适应不同的检测区域。我们发现有效检测区域可以接近于一个矩形,其大小受摄像机运动传感器菲涅耳透镜的特性、不同物种的体重和摄像机高度的影响。其他REM参数保持不变。我们将改进后的快速眼动(vREM)应用于尼泊尔巴迪亚国家公园垂直定向相机陷阱收集的野生动物数据。我们进一步验证了使用最大似然估计的相机模型的有效检测区域最好近似为矩形。密度估计结果与先前在巴迪亚进行的9个物种的独立密度估计大致相符,这些物种的体型大小各不相同,相差4个数量级。我们得出的结论是,这些修改允许快速眼动有效地用于哺乳动物密度估计物种具有广泛的身体大小,垂直定向相机陷阱。
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引用次数: 0
A comparison of established and digital surface model (DSM)‐based methods to determine population estimates and densities for king penguin colonies, using fixed‐wing drone and satellite imagery 利用固定翼无人机和卫星图像,比较了基于数字表面模型(DSM)的确定王企鹅种群估计和密度的方法
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-11-29 DOI: 10.1002/rse2.424
J. Coleman, N. Fenney, P.N. Trathan, A. Fox, E. Fox, A. Bennison, L. Ireland, M.A. Collins, P.R. Hollyman
Drones are being increasingly used to monitor wildlife populations; their large spatial coverage and minimal disturbance make them ideal for use in remote environments where access and time are limited. The methods used to count resulting imagery need consideration as they can be time‐consuming and costly. In this study, we used a fixed‐wing drone and Beyond Visual Line of Sight flying to create high‐resolution imagery and digital surface models (DSMs) of six large king penguin colonies (colony population sizes ranging from 10,671 to 132,577 pairs) in South Georgia. We used a novel DSM‐based method to facilitate automated and semi‐automated counts of each colony to estimate population size. We assessed these DSM‐derived counts against other popular counting and post‐processing methodologies, including those from satellite imagery, and compared these to the results from four colonies counted manually to evaluate accuracy and effort. We randomly subsampled four colonies to test the most efficient and accurate methods for density‐based counts, including at the colony edge, where population density is lower. Sub‐sampling quadrats (each 25 m2) together with DSM‐based counts offered the best compromise between accuracy and effort. Where high‐resolution drone imagery was available, accuracy was within 3.5% of manual reference counts. DSM methods were more accurate than other established methods including estimation from satellite imagery and are applicable for population studies across other taxa worldwide. Results and methods will be used to inform and develop a long‐term king penguin monitoring programme.
无人机越来越多地用于监测野生动物种群;它们的大空间覆盖范围和最小的干扰使它们非常适合在访问和时间有限的偏远环境中使用。用于计算结果图像的方法需要考虑,因为它们可能耗时且昂贵。在这项研究中,我们使用了固定翼无人机和超视距飞行技术,在南乔治亚州建立了6个大型王企鹅群落(种群规模从10,671对到132,577对)的高分辨率图像和数字表面模型(DSMs)。我们使用了一种新的基于DSM的方法来促进每个菌落的自动化和半自动计数,以估计种群规模。我们将这些DSM衍生计数与其他流行的计数和后处理方法(包括卫星图像计数)进行了评估,并将其与四个人工计数菌落的结果进行了比较,以评估准确性和工作量。我们随机抽样了四个菌落,以测试最有效和准确的基于密度的计数方法,包括在种群密度较低的菌落边缘。次抽样样方(每25平方米)与基于DSM的计数一起提供了准确性和工作量之间的最佳折衷。在高分辨率无人机图像可用的情况下,精度在人工参考计数的3.5%以内。DSM方法比其他现有方法(包括卫星图像估计)更准确,适用于全球其他分类群的种群研究。研究结果和方法将用于制定长期的王企鹅监测计划。
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引用次数: 0
Illuminating the Arctic: Unveiling seabird responses to artificial light during polar darkness through citizen science and remote sensing 照亮北极:通过公民科学和遥感揭示极地黑暗中海鸟对人工光的反应
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-11-24 DOI: 10.1002/rse2.425
Kaja Balazy, Dariusz Jakubas, Andrzej Kotarba, Katarzyna Wojczulanis‐Jakubas
Artificial light at night (ALAN) has global impacts on animals, often negative, yet its effects in polar regions remains largely underexplored. These regions experience prolonged darkness during the polar night, while human activity and artificial lighting are rapidly increasing. In this study, we analyzed a decade of citizen science data on light‐sensitive seabird occurrences in Longyearbyen, a High‐Arctic port settlement, to examine the impact of environmental factors including ALAN during polar night. Our investigation incorporated remote sensing data on nighttime lights levels, sea ice presence, and air temperature measurements from local meteorological station. Our findings reveal that artificial light may potentially impact seabird diversity in this region, with overall diversity decreasing alongside light intensity. However, the relationship between artificial light and seabird diversity was not uniformly negative; individual species exhibited varied responses. We also detected a correlation between artificial light and air temperature, emphasizing the complexity of environmental interactions. Notably, the piscivorous Black Guillemot (Cepphus grylle), the dominant species in Longyearbyen during the polar night, showed increased contribution in the local seabird assemblage with higher light levels. In contrast, the zooplanktivorous Little Auk (Alle alle) exhibited reduced contribution with higher light intensity and increased presence with higher air temperatures. We hypothesize that these differing responses are closely tied to the distinct dietary habits, varying sensitivity to artificial light due to individual adaptations, and overall ecological flexibility of these species, underscoring the need for further research. This study, which uniquely combines citizen science with remote sensing data, represents the first effort to systematically assess the effects of artificial lighting on seabirds during the polar night. The findings underscore the potential importance of this issue for seabird conservation in polar regions.
夜间人工照明(ALAN)对全球动物都有影响,而且往往是负面的,但其对极地地区的影响在很大程度上仍未得到充分探索。这些地区在极夜会经历长时间的黑暗,而人类活动和人工照明却在迅速增加。在这项研究中,我们分析了高纬度北极港口居民点朗伊尔城十年来对光敏感的海鸟出现情况的公民科学数据,以研究包括 ALAN 在内的环境因素对极夜的影响。我们的调查结合了夜间灯光亮度、海冰存在情况的遥感数据以及当地气象站的气温测量数据。我们的研究结果表明,人工光照可能会对该地区的海鸟多样性产生潜在影响,总体多样性会随着光照强度的降低而降低。然而,人工光照与海鸟多样性之间的关系并不是一致的负相关;个别物种表现出不同的反应。我们还检测到人工光照与气温之间的相关性,强调了环境相互作用的复杂性。值得注意的是,食鱼的黑斑鸠(Cepphus grylle)是朗伊尔城极夜的主要物种,它在当地海鸟群中的比例随着光照度的增加而增加。与此相反,浮游动物小白头翁(Alle alle)则表现出光照强度越高,其贡献率越低,而气温越高,其存在率越高。我们推测,这些不同的反应与这些物种不同的饮食习惯、个体适应性导致的对人工光照的不同敏感度以及整体生态灵活性密切相关,这也强调了进一步研究的必要性。这项研究将公民科学与遥感数据独特地结合在一起,是系统评估极夜人工照明对海鸟影响的首次尝试。研究结果强调了这一问题对极地海鸟保护的潜在重要性。
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引用次数: 0
Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision 利用无人驾驶飞机系统和计算机视觉对涉水鸟类进行近实时监测
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-11-08 DOI: 10.1002/rse2.421
Ethan P. White, Lindsey Garner, Ben G. Weinstein, Henry Senyondo, Andrew Ortega, Ashley Steinkraus, Glenda M. Yenni, Peter Frederick, S. K. Morgan Ernest
Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.
由于航空调查方法的发展,以及使用计算机视觉模型对生物个体进行识别和分类,在大面积地理区域进行野生动物种群监测变得越来越可行。然而,航空调查仍然不经常进行,而且从获取航空图像到将其转换为种群监测数据之间往往会有很长时间的延迟。近实时监测对于积极的管理决策和生态预测越来越重要。要在大范围内实现这一目标,需要结合机载图像、将图像处理成生物个体信息的计算机视觉模型,以及确保图像在获取后迅速处理成数据的自动化工作流程。在此,我们介绍了在美国佛罗里达州大沼泽地对涉禽进行近实时监测的端到端工作流程。我们使用无人驾驶飞机系统(又称无人机)以每周一次的频率采集图像,处理成正交合成图(使用 Agisoft metashape),使用 Retinanet-50 物体检测器转换成个体级物种数据,进行后处理、存档,并在基于网络的可视化平台上展示(使用 Shiny)。工作流程的主要组成部分是使用 Snakemake 自动完成的。底层计算机视觉模型能够准确地检测物体、进行物种分类,并对六个目标物种(白鹮、大白鹭、大蓝鹭、鹳和鹭琵鹭)中的五个物种进行总计数和物种计数。该模型在雪鹭方面的表现较差,原因是标签数量较少,且难以将雪鹭与白朱鹭(数量最多的物种)区分开来。通过将调查后处理自动化,这些物种的种群数据几乎可以实时获得(调查日期后 1 周),为生态预测和积极管理提供了所需的时间尺度信息。
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引用次数: 0
Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer 利用 ECOSTRESS 数据和机器学习方法研究野火动态:澳大利亚东南部黑色夏季的案例
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-11-05 DOI: 10.1002/rse2.422
Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher
Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. Our models identified vegetation conditions with a 3‐week time lag before wildfire events in the fourth week and predicted the probability of wildfire occurrences in the subsequent week (fifth week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires. Results suggest that the pre‐fire vegetation was affected by wildfires in areas with WUE above 2 g C kg−1 H₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index and topographic slope were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.
野火的风险和发生率都在增加。澳大利亚几十年来破坏性最大的野火发生在 2019-2020 年。然而,在开发有效模型以了解野火蔓延的可能性(易感性)和火前植被状况方面仍存在挑战。美国国家航空航天局(NASA)最近发射的 ECOSTRESS 提供了一个机会,可以通过测量野火发生前的生态系统压力和干旱状况,以 70 米的高分辨率监测火灾动态。我们将 ECOSTRESS 数据、植被指数、降雨量和地形数据作为自变量,将火灾事件作为因变量纳入机器学习算法,并将其应用于 2019-2020 年历史上的澳大利亚野火。利用这些数据,我们在这些野火事件发生前一周预测了90%以上的野火事件。我们的模型确定了第四周野火事件发生前 3 周的植被状况,并预测了随后一周(第五周)发生野火的概率。在所有预测野火的模型中,ECOSTRESS 水利用效率(WUE)始终是最主要的因素。结果表明,在 WUE 超过 2 g C kg-1 H₂O 的地区,火灾前植被受野火影响的概率为 95%。此外,ECOSTRESS 蒸发压力指数和地形坡度也是预测野火易感性的重要因素。这些结果表明了 ECOSTRESS 数据在预测和分析野火方面的巨大潜力,并强调了 ECOSTRESS 数据所显示的干旱条件在野火事件中的关键作用。我们在这项研究中开发的方法和成果可以帮助政策制定者、火灾管理者和城市规划者在未来评估、管理、准备和缓解野火。
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引用次数: 0
Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques 基于人工智能的被动声学技术揭示亚马逊海牛关键栖息地
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-10-31 DOI: 10.1002/rse2.418
Florence Erbs, Mike van der Schaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André
For many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic methods need to be developed, tested and applied. Combining passive acoustics with deep learning models, we developed a method to monitor the secretive Amazonian manatee over two consecutive flooded seasons in the Brazilian Amazon floodplains. Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. Similar classification performance in terms of precision (range: 0.83–1.00) and recall (range: 0.97–1.00) was achieved for each year. Using this model, we evaluated manatee acoustic presence over a total of 226 days comprising recording periods in 2021 and 2022. Manatee vocalizations were consistently detected during both years, reaching 94% daily temporal occurrence in 2021, and up to 11 h a day with detections during peak presence. Manatee calls were characterized by a high emphasized frequency and high repetition rate, being mostly produced in rapid sequences. This vocal behavior strongly indicates an exchange between females and their calves. Combining passive acoustic monitoring with deep learning models, and extending temporal monitoring and increasing species detectability, we demonstrated that the approach can be used to identify manatee core habitats according to seasonality. The combined method represents a reliable, cost‐effective, scalable ecological monitoring technique that can be integrated into long‐term, standardized survey protocols of aquatic species. It can considerably benefit the monitoring of inaccessible regions, such as the Amazonian freshwater systems, which are facing immediate threats from increased hydropower construction.
对于许多濒危物种来说,由于视觉可探测性低和行为难以捉摸,传统的视觉调查在收集关键信息方面受到限制,从而阻碍了合理保护战略的制定。被动声学可以经济有效地获取陆地和水下的长期数据。然而,要从大型数据集中提取有价值的信息,需要开发、测试和应用自动方法。我们将被动声学与深度学习模型相结合,开发出一种方法,在巴西亚马逊洪泛平原连续两个汛期监测神秘的亚马逊海牛。随后,我们根据发声频率和时间特征研究了栖息地使用背景下的发声行为参数。卷积神经网络模型成功地检测到了亚马逊海牛的发声,训练数据的平均精度为 0.98。在精确度(范围:0.83-1.00)和召回率(范围:0.97-1.00)方面,每年都取得了相似的分类效果。利用该模型,我们对 2021 年和 2022 年共计 226 天的海牛声学存在进行了评估。在这两年中,海牛的叫声一直都能被探测到,2021 年海牛叫声的日出现率高达 94%,在海牛出现高峰期,每天的探测时间长达 11 小时。海牛叫声的特点是强调频率高、重复率高,大多以快速序列发出。这种发声行为强烈表明雌海牛与幼海牛之间存在交流。我们将被动声学监测与深度学习模型相结合,并扩大了时间监测范围,提高了物种可探测性,证明该方法可用于根据季节性识别海牛的核心栖息地。这种综合方法是一种可靠、经济、可扩展的生态监测技术,可纳入长期、标准化的水生物种调查方案。它可以极大地促进对亚马逊淡水系统等难以进入地区的监测,这些地区正面临着水电建设增加所带来的直接威胁。
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引用次数: 0
Combining satellite and field data reveals Congo's forest types structure, functioning and composition 结合卫星和实地数据揭示刚果森林类型的结构、功能和组成
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-10-12 DOI: 10.1002/rse2.419
Juliette Picard, Maïalicah M. Nungi‐Pambu Dembi, Nicolas Barbier, Guillaume Cornu, Pierre Couteron, Eric Forni, Gwili Gibbon, Felix Lim, Pierre Ploton, Robin Pouteau, Paul Tresson, Tom van Loon, Gaëlle Viennois, Maxime Réjou‐Méchain
Tropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.
热带潮湿森林并不是地图上经常标示的或全球模型所认为的均匀的绿色地毯。在遥感产品和人工智能的帮助下,现在可以更精确地绘制出不同空间尺度的森林类型。在这项研究中,我们绘制了刚果北部大尺度植被图,并评估了主要森林类型的环境驱动因素、森林结构、花卉和功能组成以及动物组成。为了绘制该地图,我们使用了 Sentinel-2 卫星图像和最新的深度学习架构。我们通过将地图与排水深度代用指标(HAND,最近排水指数以上的高度)相连接,测试了由地形确定的水源对植被类型分布的影响。我们还通过将地图与来自大型清单和卫星图像的数据相连接,描述了植被类型的结构和组成(植物学、功能和相关动物群)。我们发现,排水深度是森林类型分布的主要驱动因素,不同的森林类型具有不同的结构、组成和功能,这为我们了解其起源和演替动态提供了新的视角。我们不仅讨论了土壤水深度的关键作用,还讨论了随着时间的推移不断复制此类地图对准确监测热带森林类型和功能的重要性,并就未来研究应更加关注的特殊森林类型(马缨丹科森林和单优势吉尔伯特碘龙森林)提出了见解。在当前全球变化的背景下,预计热带地区的森林结构和组成将发生重大变化,对森林类型的时空动态及其相关的花卉和动物组成进行适当的监测战略将大大有助于预测有害的变化。
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引用次数: 0
Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests 早期光谱动态显示了野火后森林的独特生长模式
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-09-18 DOI: 10.1002/rse2.420
Sarah Smith‐Tripp, Nicholas C. Coops, Christopher Mulverhill, Joanne C. White, Sarah Gergel
Western North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.
北美西部最近发生的大规模野火急剧增加,而且往往非常严重。森林火灾后,了解结构恢复的模式非常重要,因为恢复模式会影响关键的生态系统服务。卫星观测提供的连续森林监测特别有利于捕捉火灾后森林开始恢复的关键时期。然而,优化光学卫星图像以推断当前和未来的森林结构和组成是一项挑战。我们发现有必要了解早期光谱动态(火灾后 5 年)如何为火灾干扰后的结构恢复模式提供信息。为了创建这些结构模式,我们使用高密度遥控飞机(RPAS)激光雷达(光探测和测距)收集了森林结构指标。我们在不列颠哥伦比亚省内陆受火灾严重干扰的森林中采用了空间换时间的方法。在该地区,我们收集了 RPAS 激光雷达和相应的野外地块数据,用于预测火灾后 5、8、11、12 和 16 年与管理相关的结构属性,包括裸地百分比、针叶树比例、茎干密度和基部面积。我们将森林结构属性与火灾发生 5 年后从 Landsat 时间序列数据中得出的独特早期光谱响应或轨迹进行了比较。从七种植被指数(NBR、NDMI、NDVI、TCA、TCB、TCG 和 TCW)的光谱响应中,共识别出八种独特的光谱恢复轨迹,它们描述了用 RPAS 激光雷达捕捉到的五种不同的结构恢复模式。两种结构模式覆盖了 80% 以上的研究区域。这两种模式都有较强的针叶树再生,但其中一种模式的基部面积较大,裸地较多;另一种模式的茎干密度较高,但基部面积较小,落叶比例较高。我们的方法强调了利用早期光谱响应捕捉独特光谱轨迹及其相关的独特结构恢复模式的能力。
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引用次数: 0
期刊
Remote Sensing in Ecology and Conservation
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