Alba Solsona-Berga, Natalie Posdaljian, J. Hildebrand, S. Baumann‐Pickering
Characterizing population structure and dynamics is critical for the conservation of endangered species. Monitoring sperm whales Physeter macrocephalus is especially difficult because it requires monitoring different latitudes to capture the dynamics of most populations. Since their remarkable sexual dimorphism in body size is reflected in their sounds, passive acoustic monitoring presents an opportunity to capture contiguous patterns in time, space, and over large scales. We show that the echolocation repetition rate (measured as inter‐click interval, ICI) as a proxy for body length is a suitable approach for large‐scale acoustic monitoring. Body length has previously been estimated from the time interval between pulses (IPI) within sperm whale echolocation clicks. These estimates can only be achieved when whales are oriented toward the recorder or directly facing away, resulting in sparse data. A representative subsample of data demonstrated that ICI and IPI are linearly correlated, allowing conversion of ICI distributions into likely body length categories. This approach was applied to three monitoring sites in the Gulf of Mexico (2010–2017), where sperm whale population structure and male movements were poorly understood. We identified three classes: large animals between 12–15 m (ICI between 0.72 and 1 sec), presumed to correspond to adult males, and small animals below 12 m (ICI between 0.44 and 0.64 sec) likely pertaining to social groups (mixed groups with adult females and their offspring), and the third class with mid‐sized animals (ICI between 0.64 and 0.83 sec) believed to contain adult females or sub‐adult males. Our results revealed spatial and seasonal variability of the population structure including possible male presence throughout the year and migratory patterns of the population. This approach provides a means to efficiently characterize the putative population structure of sperm whales to understand the population's geographical dynamics and population status, which is relevant under rapidly changing habitat conditions.
{"title":"Echolocation repetition rate as a proxy to monitor population structure and dynamics of sperm whales","authors":"Alba Solsona-Berga, Natalie Posdaljian, J. Hildebrand, S. Baumann‐Pickering","doi":"10.1002/rse2.278","DOIUrl":"https://doi.org/10.1002/rse2.278","url":null,"abstract":"Characterizing population structure and dynamics is critical for the conservation of endangered species. Monitoring sperm whales Physeter macrocephalus is especially difficult because it requires monitoring different latitudes to capture the dynamics of most populations. Since their remarkable sexual dimorphism in body size is reflected in their sounds, passive acoustic monitoring presents an opportunity to capture contiguous patterns in time, space, and over large scales. We show that the echolocation repetition rate (measured as inter‐click interval, ICI) as a proxy for body length is a suitable approach for large‐scale acoustic monitoring. Body length has previously been estimated from the time interval between pulses (IPI) within sperm whale echolocation clicks. These estimates can only be achieved when whales are oriented toward the recorder or directly facing away, resulting in sparse data. A representative subsample of data demonstrated that ICI and IPI are linearly correlated, allowing conversion of ICI distributions into likely body length categories. This approach was applied to three monitoring sites in the Gulf of Mexico (2010–2017), where sperm whale population structure and male movements were poorly understood. We identified three classes: large animals between 12–15 m (ICI between 0.72 and 1 sec), presumed to correspond to adult males, and small animals below 12 m (ICI between 0.44 and 0.64 sec) likely pertaining to social groups (mixed groups with adult females and their offspring), and the third class with mid‐sized animals (ICI between 0.64 and 0.83 sec) believed to contain adult females or sub‐adult males. Our results revealed spatial and seasonal variability of the population structure including possible male presence throughout the year and migratory patterns of the population. This approach provides a means to efficiently characterize the putative population structure of sperm whales to understand the population's geographical dynamics and population status, which is relevant under rapidly changing habitat conditions.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48676796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Issue Information","authors":"","doi":"10.1002/rse2.220","DOIUrl":"https://doi.org/10.1002/rse2.220","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43130335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large river deltas are usually ecologically important wetland habitats, but also fertile agricultural exploitation areas, creating a conflict of long‐running substantial interest between agricultural expansion and wetland conservation. Over the past several years, large‐scale cultivation of water‐consuming rice has been growing rapidly in the semi‐arid climate of the Yellow River Delta (YRD). Timely monitoring of rice cultivation dynamics is of great significance for sustainable ecological conservation of the delta, which has insufficient freshwater resources. This study proposed a stratified metrics‐based method that integrates statistical spectral indices and phenological metrics at different growing stages to improve the accuracy of rice paddy classification in areas where rice and wetlands coexist. We applied the method to time‐series Sentinel‐1/2 images to produce annual rice paddy maps of the YRD from 2016 to 2021. Together with rice paddy data from 2011 to 2015 from Statistical Yearbooks of Dongying Bureau of Statistics, we investigated the expansion dynamics over the past decade and in this paper discuss the advantages and disadvantages of rice cultivation expansion over wetland ecosystem conservation. Rapid expansion of rice cultivation intensifies water conflicts, and adversely affects wetland restoration in the YRD. Considering the important ecological services of rice paddies as alternative habitats, we argue for maintaining a reasonable scale of rice paddies and optimizing their distribution as a potential solution to achieving the overall sustainable conservation of the YRD in the context of water scarcity.
{"title":"Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity","authors":"Chong Huang, Chenchen Zhang","doi":"10.1002/rse2.320","DOIUrl":"https://doi.org/10.1002/rse2.320","url":null,"abstract":"Large river deltas are usually ecologically important wetland habitats, but also fertile agricultural exploitation areas, creating a conflict of long‐running substantial interest between agricultural expansion and wetland conservation. Over the past several years, large‐scale cultivation of water‐consuming rice has been growing rapidly in the semi‐arid climate of the Yellow River Delta (YRD). Timely monitoring of rice cultivation dynamics is of great significance for sustainable ecological conservation of the delta, which has insufficient freshwater resources. This study proposed a stratified metrics‐based method that integrates statistical spectral indices and phenological metrics at different growing stages to improve the accuracy of rice paddy classification in areas where rice and wetlands coexist. We applied the method to time‐series Sentinel‐1/2 images to produce annual rice paddy maps of the YRD from 2016 to 2021. Together with rice paddy data from 2011 to 2015 from Statistical Yearbooks of Dongying Bureau of Statistics, we investigated the expansion dynamics over the past decade and in this paper discuss the advantages and disadvantages of rice cultivation expansion over wetland ecosystem conservation. Rapid expansion of rice cultivation intensifies water conflicts, and adversely affects wetland restoration in the YRD. Considering the important ecological services of rice paddies as alternative habitats, we argue for maintaining a reasonable scale of rice paddies and optimizing their distribution as a potential solution to achieving the overall sustainable conservation of the YRD in the context of water scarcity.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47925434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals to visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs, or drones) to collect video of dolphins to characterize availability and investigate error surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with Australian humpback dolphins Sousa sahulensis off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (se = 0.26) and 39.27 sec (se = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7 sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.
{"title":"Using Unoccupied Aerial Vehicles to estimate availability and group size error for aerial surveys of coastal dolphins","authors":"A. Brown, S. Allen, Nat Kelly, A. Hodgson","doi":"10.1002/rse2.313","DOIUrl":"https://doi.org/10.1002/rse2.313","url":null,"abstract":"Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals to visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs, or drones) to collect video of dolphins to characterize availability and investigate error surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with Australian humpback dolphins Sousa sahulensis off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (se = 0.26) and 39.27 sec (se = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7 sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45559391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xungang Shi, Cheng Hu, J. Soderholm, J. Chapman, Huafeng Mao, Kai Cui, Zhijun Ma, Dongli Wu, R. Fuller
Each year, billions of birds migrate across the globe, and interpretation of weather radar signals is increasingly being used to document the spatial and temporal migration patterns in Europe and America. Such approaches are yet to be applied in the East Asian‐Australasian Flyway (EAAF), one of the most species‐rich and threatened flyways in the world. Logistical challenges limit direct on‐ground monitoring of migratory birds in many parts of the EAAF, resulting in knowledge gaps on population status and site use that limit evidence‐based conservation planning. Weather radar data have great potential for achieving comprehensive migratory bird monitoring along the EAAF. In this study, we discuss the feasibility and challenges of using weather radar to complement on‐ground bird migration surveys in the flyway. We summarize the location, capacity and data availability of weather radars across EAAF countries, as well as the spatial coverage of the radars with respect to migrants' geographic distribution and migration hotspots along the flyway, with an exemplar analysis of biological movement patterns extracted from Chinese weather radars. There are more than 430 weather radars in EAAF countries, covering on average half of bird species' passage and non‐breeding distributions, as well as 70% of internationally important sites for migratory shorebirds. We conclude that the weather radar network could be a powerful resource for monitoring bird movements over the full annual cycle throughout much of the EAAF, providing estimates of migration traffic rates, site use, and long‐term population trends, especially in remote and less‐surveyed regions. Analyses of weather radar data would complement existing ornithological surveys and help understand the past and present status of the avian community in a highly threatened flyway.
{"title":"Prospects for monitoring bird migration along the East Asian‐Australasian Flyway using weather radar","authors":"Xungang Shi, Cheng Hu, J. Soderholm, J. Chapman, Huafeng Mao, Kai Cui, Zhijun Ma, Dongli Wu, R. Fuller","doi":"10.1002/rse2.307","DOIUrl":"https://doi.org/10.1002/rse2.307","url":null,"abstract":"Each year, billions of birds migrate across the globe, and interpretation of weather radar signals is increasingly being used to document the spatial and temporal migration patterns in Europe and America. Such approaches are yet to be applied in the East Asian‐Australasian Flyway (EAAF), one of the most species‐rich and threatened flyways in the world. Logistical challenges limit direct on‐ground monitoring of migratory birds in many parts of the EAAF, resulting in knowledge gaps on population status and site use that limit evidence‐based conservation planning. Weather radar data have great potential for achieving comprehensive migratory bird monitoring along the EAAF. In this study, we discuss the feasibility and challenges of using weather radar to complement on‐ground bird migration surveys in the flyway. We summarize the location, capacity and data availability of weather radars across EAAF countries, as well as the spatial coverage of the radars with respect to migrants' geographic distribution and migration hotspots along the flyway, with an exemplar analysis of biological movement patterns extracted from Chinese weather radars. There are more than 430 weather radars in EAAF countries, covering on average half of bird species' passage and non‐breeding distributions, as well as 70% of internationally important sites for migratory shorebirds. We conclude that the weather radar network could be a powerful resource for monitoring bird movements over the full annual cycle throughout much of the EAAF, providing estimates of migration traffic rates, site use, and long‐term population trends, especially in remote and less‐surveyed regions. Analyses of weather radar data would complement existing ornithological surveys and help understand the past and present status of the avian community in a highly threatened flyway.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44008711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy ( RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 ( RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 ( RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 ( RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs ( B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.
{"title":"Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods","authors":"Demei Zhao, Jianing Zhen, Yinghui Zhang, Jing Miao, Z. Shen, Xiapeng Jiang, Junjie Wang, Jincheng Jiang, Yuzhi Tang, Guofeng Wu","doi":"10.1002/rse2.315","DOIUrl":"https://doi.org/10.1002/rse2.315","url":null,"abstract":"Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy ( RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 ( RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 ( RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 ( RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs ( B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45388324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Moeslund, K. K. Clausen, L. Dalby, Camilla Fløjgaard, M. Pärtel, N. Pfeifer, M. Hollaus, Ane Kirstine Brunbjerg
A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity—that is, absence of suitable species—at national extent (>43 000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field‐based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high‐dark‐diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High‐dark‐diversity habitats in forests had relatively tall vegetation, few natural‐forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain‐ and soil‐related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old‐growth elements such as dead wood and rot attacks.
{"title":"Using airborne lidar to characterize North European terrestrial high‐dark‐diversity habitats","authors":"J. Moeslund, K. K. Clausen, L. Dalby, Camilla Fløjgaard, M. Pärtel, N. Pfeifer, M. Hollaus, Ane Kirstine Brunbjerg","doi":"10.1002/rse2.314","DOIUrl":"https://doi.org/10.1002/rse2.314","url":null,"abstract":"A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity—that is, absence of suitable species—at national extent (>43 000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field‐based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high‐dark‐diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High‐dark‐diversity habitats in forests had relatively tall vegetation, few natural‐forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain‐ and soil‐related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old‐growth elements such as dead wood and rot attacks.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44733624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Camille Magneville, Marie‐Lou Leréec Le Bricquir, T. Dailianis, Grigorios Skouradakis, T. Claverie, S. Villéger
In the marine environment, fish contribute to key ecological processes such as controlling food‐webs through top‐down impacts, especially on algae. To date, the assessment of fish grazing activity has mostly been performed using short‐term (<1 h) censuses by divers or remote cameras which do not allow estimating the variability of grazing rate within and between days. However, understanding the temporal variation of fish activity and hence contribution of species to ecosystem functioning is of particular interest in the context of biological invasion. Here, using long‐duration remote underwater cameras, we recorded fish abundance and grazing events over three consecutive days in October 2019 in a shallow Mediterranean ecosystem from northern Crete. This novel approach allowed us to assess temporal variation of abundance and grazing activity of the two native (Sarpa salpa and Sparisoma cretense) and the two non‐indigenous fish species (Siganus rivulatus and Siganus luridus). Non‐indigenous Siganus rivulatus was the most common species in the studied coastal habitat, followed by the two native species while the non‐indigenous Siganus luridus was scarce. Overall, the non‐indigenous S. rivulatus and the native S. salpa are responsible for more than 90% of the recorded grazing activity with similar bite rates between the two species. More than 70% of the grazing activity arose in grazing pulses in the afternoon, supporting the diel feeding hypothesis according to which feeding is greater in the afternoon when nutritive quality of macrophytes is the highest. In addition, some of the highest peaks in grazing activity were driven by a few individuals. Hence, surveys of only abundance could not provide accurate estimates of herbivory. Last, Siganus rivulatus presence did not significantly affect grazing activity of the native Sarpa salpa. Our results demonstrate that long‐duration remote underwater videos are a useful tool to accurately assess the contribution of fishes to ecosystem functioning.
{"title":"Long‐duration remote underwater videos reveal that grazing by fishes is highly variable through time and dominated by non‐indigenous species","authors":"Camille Magneville, Marie‐Lou Leréec Le Bricquir, T. Dailianis, Grigorios Skouradakis, T. Claverie, S. Villéger","doi":"10.1002/rse2.311","DOIUrl":"https://doi.org/10.1002/rse2.311","url":null,"abstract":"In the marine environment, fish contribute to key ecological processes such as controlling food‐webs through top‐down impacts, especially on algae. To date, the assessment of fish grazing activity has mostly been performed using short‐term (<1 h) censuses by divers or remote cameras which do not allow estimating the variability of grazing rate within and between days. However, understanding the temporal variation of fish activity and hence contribution of species to ecosystem functioning is of particular interest in the context of biological invasion. Here, using long‐duration remote underwater cameras, we recorded fish abundance and grazing events over three consecutive days in October 2019 in a shallow Mediterranean ecosystem from northern Crete. This novel approach allowed us to assess temporal variation of abundance and grazing activity of the two native (Sarpa salpa and Sparisoma cretense) and the two non‐indigenous fish species (Siganus rivulatus and Siganus luridus). Non‐indigenous Siganus rivulatus was the most common species in the studied coastal habitat, followed by the two native species while the non‐indigenous Siganus luridus was scarce. Overall, the non‐indigenous S. rivulatus and the native S. salpa are responsible for more than 90% of the recorded grazing activity with similar bite rates between the two species. More than 70% of the grazing activity arose in grazing pulses in the afternoon, supporting the diel feeding hypothesis according to which feeding is greater in the afternoon when nutritive quality of macrophytes is the highest. In addition, some of the highest peaks in grazing activity were driven by a few individuals. Hence, surveys of only abundance could not provide accurate estimates of herbivory. Last, Siganus rivulatus presence did not significantly affect grazing activity of the native Sarpa salpa. Our results demonstrate that long‐duration remote underwater videos are a useful tool to accurately assess the contribution of fishes to ecosystem functioning.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42187134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emilio Luz-Ricca, K. Landolt, B. Pickens, M. Koneff
Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low‐altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost‐prohibitive. Therefore, automated methods for counting are critical to cost‐efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes (Antigone canadensis) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R‐CNN and a recently developed pixel‐density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R‐CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had <50% of the error of Faster R‐CNN as measured by mean percent error, root‐mean‐squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R‐CNN. Deep learning models, particularly pixel‐density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.
种群监测对候鸟的管理和保护工作至关重要，但传统的由人类观察员进行的低空空中调查受到观察员个人偏见和机组人员风险的困扰。使用遥感的航空调查可以减少偏差和风险，但人工计算图像中的野生动物是费力的，而且可能成本过高。因此，自动化的计数方法对于在大面积野生动物调查中应用具有成本效益的遥感至关重要。利用中波热红外传感器对美国内布拉斯加州中部普拉特河谷春季迁徙期间的沙丘鹤(Antigone canadensis)进行夜间调查。我们利用深度学习技术开发了一个基于热图像的沙丘鹤自动计数框架，评估和比较了两种自动计数模型的性能，并量化了空间分辨率对计数精度的影响。航空热成像数据收集于2018年3月和2021年3月;分析了40张图像。我们应用了两种深度学习模型:目标检测方法，Faster R - CNN和最近开发的像素密度估计方法ASPDNet。使用独立于训练图像的数据来确定模型的性能。空间分辨率对相对误差的影响通过β回归进行量化。我们的结果表明，ASPDNet模型的平均误差为9%，Faster R - CNN模型的平均误差为18%。大多数误差与漏报沙丘鹤有关。通过平均百分比误差、均方根误差和平均绝对误差来衡量，ASPDNet的误差小于Faster R - CNN的50%。空间分辨率对两种模型的精度都有影响，随着分辨率的增加，错误率也在增加，尤其是使用更快的R - CNN。深度学习模型，特别是像素密度估计器，可以准确地自动计数密集聚集环境中的候鸟，如夜间栖息地点。
{"title":"Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning","authors":"Emilio Luz-Ricca, K. Landolt, B. Pickens, M. Koneff","doi":"10.1002/rse2.301","DOIUrl":"https://doi.org/10.1002/rse2.301","url":null,"abstract":"Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low‐altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost‐prohibitive. Therefore, automated methods for counting are critical to cost‐efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes (Antigone canadensis) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R‐CNN and a recently developed pixel‐density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R‐CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had <50% of the error of Faster R‐CNN as measured by mean percent error, root‐mean‐squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R‐CNN. Deep learning models, particularly pixel‐density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47885701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Meijer, O. Franken, T. van der Heide, S. Holthuijsen, W. Visser, L. L. Govers, H. Olff
In soft‐bottom marine ecosystems, bedform variation is induced by wind‐ and tidal‐driven hydrodynamics. The resulting megaripples, sand waves and sandbanks form a spatially and temporally heterogeneous seafloor landscape. The strong physical forces imposed by the migration of these bedforms are important determinants for the occurrence of different macrozoobenthic species. Quantifying the effect of these forces can help in differentiating natural‐ and anthropogenically induced physical stressors. However, large‐scale mapping of seabed morphology at high resolution using multibeam echosounder is challenging, costly and time‐consuming, especially in shallow seas, prohibiting wide swaths. Instead, their bathymetry is typically studied using single‐beam transects that are interpolated to bathymetric grids with a relatively coarse resolution (20 m). However, this leaves out information on smaller scale (<20 m) bedforms that can be ecologically relevant. In the Dutch Wadden sea, a shallow tidal system, we characterized bedform variation at high resolution using single‐beam data for the first time. We calculated a 2‐D Terrain Ruggedness Index (TRI) at sub‐meter resolution along the single‐beam transects and interpolated the results to a full 3‐D grid. We then validated the result by relating TRI to independently modeled hydrodynamic parameters and to the distribution of macrozoobenthic species. We found that TRI successfully integrates the variation of tidal‐driven bed shear stress and wave‐driven orbital velocity. In addition, we found TRI to be a good predictor of the occurrence of macrozoobenthic species. The inferred small‐scale bedforms provide valuable information for separating the relative importance of natural dynamics versus anthropogenic disturbances such as dredging and bottom trawling activities. We discuss that by repurposing already available single‐beam data in this way, bedforms can be characterized at high resolution without the need for additional equipment or mapping campaigns, yielding novel input to decision‐making on marine management and conservation.
{"title":"Characterizing bedforms in shallow seas as an integrative predictor of seafloor stability and the occurrence of macrozoobenthic species","authors":"K. Meijer, O. Franken, T. van der Heide, S. Holthuijsen, W. Visser, L. L. Govers, H. Olff","doi":"10.1002/rse2.312","DOIUrl":"https://doi.org/10.1002/rse2.312","url":null,"abstract":"In soft‐bottom marine ecosystems, bedform variation is induced by wind‐ and tidal‐driven hydrodynamics. The resulting megaripples, sand waves and sandbanks form a spatially and temporally heterogeneous seafloor landscape. The strong physical forces imposed by the migration of these bedforms are important determinants for the occurrence of different macrozoobenthic species. Quantifying the effect of these forces can help in differentiating natural‐ and anthropogenically induced physical stressors. However, large‐scale mapping of seabed morphology at high resolution using multibeam echosounder is challenging, costly and time‐consuming, especially in shallow seas, prohibiting wide swaths. Instead, their bathymetry is typically studied using single‐beam transects that are interpolated to bathymetric grids with a relatively coarse resolution (20 m). However, this leaves out information on smaller scale (<20 m) bedforms that can be ecologically relevant. In the Dutch Wadden sea, a shallow tidal system, we characterized bedform variation at high resolution using single‐beam data for the first time. We calculated a 2‐D Terrain Ruggedness Index (TRI) at sub‐meter resolution along the single‐beam transects and interpolated the results to a full 3‐D grid. We then validated the result by relating TRI to independently modeled hydrodynamic parameters and to the distribution of macrozoobenthic species. We found that TRI successfully integrates the variation of tidal‐driven bed shear stress and wave‐driven orbital velocity. In addition, we found TRI to be a good predictor of the occurrence of macrozoobenthic species. The inferred small‐scale bedforms provide valuable information for separating the relative importance of natural dynamics versus anthropogenic disturbances such as dredging and bottom trawling activities. We discuss that by repurposing already available single‐beam data in this way, bedforms can be characterized at high resolution without the need for additional equipment or mapping campaigns, yielding novel input to decision‐making on marine management and conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42218536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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