{"title":"Issue Information","authors":"","doi":"10.1002/rse2.279","DOIUrl":"https://doi.org/10.1002/rse2.279","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44787996","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}
M. Belotti, Yuting Deng, Wenlong Zhao, Victoria F. Simons, Zezhou Cheng, Gustavo Perez, Elske K. Tielens, Subhransu Maji, D. Sheldon, Jeffrey F. Kelly, K. Horton
In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications.
{"title":"Long‐term analysis of persistence and size of swallow and martin roosts in the US Great Lakes","authors":"M. Belotti, Yuting Deng, Wenlong Zhao, Victoria F. Simons, Zezhou Cheng, Gustavo Perez, Elske K. Tielens, Subhransu Maji, D. Sheldon, Jeffrey F. Kelly, K. Horton","doi":"10.1002/rse2.323","DOIUrl":"https://doi.org/10.1002/rse2.323","url":null,"abstract":"In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41792496","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}
T. Preston, Aaron N. Johnston, Kyle G. Ebenhoch, Robert H. Diehl
Non‐native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non‐native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel‐2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel‐2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape‐level mapping.
{"title":"Beyond presence mapping: predicting fractional cover of non‐native vegetation in Sentinel‐2 imagery using an ensemble of MaxEnt models","authors":"T. Preston, Aaron N. Johnston, Kyle G. Ebenhoch, Robert H. Diehl","doi":"10.1002/rse2.325","DOIUrl":"https://doi.org/10.1002/rse2.325","url":null,"abstract":"Non‐native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non‐native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel‐2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel‐2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape‐level mapping.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49586282","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. Ridge, Alexandra E. DiGiacomo, Antonio B. Rodriguez, Joshua D. Himmelstein, D. Johnston
Physical structures generated from ecosystem engineers can have a cascade of impacts on the ecological community and the surrounding landscape. The Eastern oyster Crassostrea virginica can form extensive intertidal reefs, whose three‐dimensional structures provide ecosystem services like nursery and foraging habitat for fishes and invertebrates and shoreline stabilization. Measurements of the structural properties of these reefs provide opportunities to quantitatively assess associated services. There is a growing variety of tools available for measuring three‐dimensional (3D) properties of intertidal habitats, including two remote sensing methods that capture 3D structural metrics in a number of environments. We surveyed reefs using a terrestrial laser scanner (TLS, LiDAR) and imagery from unoccupied aircraft systems (UAS, or drones) processed through Structure from Motion photogrammetry. Comparisons of digital elevation models from repetitive flights over an oyster reef to checkpoints yielded mean horizontal and vertical root mean square errors (RMSE) of −0.54 ± 0.47 cm and 0.97 ± 1.0 cm (Mean ± SD), respectively, indicating high accuracy among UAS surveys. Compared to TLS products, point cloud densities from UAS‐derived products were more consistent across the reef elevation gradient and much denser overall except in the low reef zone, which was proximal to most of the TLS scan locations. Comparisons of structural metrics between UAS and TLS showed similarities in metrics like profile and planform curvatures, yet indicated UAS surveys produced higher values of surface complexity and slope. Results indicate that UAS photogrammetry can produce robust oyster reef structural metrics that can be highly useful in oyster conservation and restoration.
由生态系统工程师产生的物理结构可以对生态群落和周围景观产生一连串的影响。东方牡蛎(Crassostrea virginica)可以形成广泛的潮间带礁，其三维结构为鱼类和无脊椎动物提供了苗圃和觅食栖息地，并为海岸线稳定提供了生态系统服务。对这些珊瑚礁结构特性的测量为定量评估相关服务提供了机会。有越来越多的工具可用于测量潮间带栖息地的三维(3D)特性，包括在许多环境中捕获三维结构度量的两种遥感方法。我们使用陆地激光扫描仪(TLS, LiDAR)和通过运动摄影测量处理的无人飞机系统(UAS或无人机)的图像来调查珊瑚礁。将重复飞越牡蛎礁的数字高程模型与检查站进行比较，平均水平和垂直均方根误差(RMSE)分别为- 0.54±0.47 cm和0.97±1.0 cm (mean±SD)，表明UAS调查的精度很高。与TLS产品相比，来自UAS衍生产品的点云密度在整个珊瑚礁高程梯度上更加一致，除了靠近大多数TLS扫描位置的低珊瑚礁区域外，总体密度更高。通过比较UAS和TLS的结构指标，可以发现在剖面和平台曲率等指标上存在相似之处，但也表明UAS测量的表面复杂性和坡度值更高。结果表明，UAS摄影测量可以产生稳健的牡蛎礁结构指标，对牡蛎保护和恢复具有重要意义。
{"title":"Comparison of 3D structural metrics on oyster reefs using unoccupied aircraft photogrammetry and terrestrial LiDAR across a tidal elevation gradient","authors":"J. Ridge, Alexandra E. DiGiacomo, Antonio B. Rodriguez, Joshua D. Himmelstein, D. Johnston","doi":"10.1002/rse2.324","DOIUrl":"https://doi.org/10.1002/rse2.324","url":null,"abstract":"Physical structures generated from ecosystem engineers can have a cascade of impacts on the ecological community and the surrounding landscape. The Eastern oyster Crassostrea virginica can form extensive intertidal reefs, whose three‐dimensional structures provide ecosystem services like nursery and foraging habitat for fishes and invertebrates and shoreline stabilization. Measurements of the structural properties of these reefs provide opportunities to quantitatively assess associated services. There is a growing variety of tools available for measuring three‐dimensional (3D) properties of intertidal habitats, including two remote sensing methods that capture 3D structural metrics in a number of environments. We surveyed reefs using a terrestrial laser scanner (TLS, LiDAR) and imagery from unoccupied aircraft systems (UAS, or drones) processed through Structure from Motion photogrammetry. Comparisons of digital elevation models from repetitive flights over an oyster reef to checkpoints yielded mean horizontal and vertical root mean square errors (RMSE) of −0.54 ± 0.47 cm and 0.97 ± 1.0 cm (Mean ± SD), respectively, indicating high accuracy among UAS surveys. Compared to TLS products, point cloud densities from UAS‐derived products were more consistent across the reef elevation gradient and much denser overall except in the low reef zone, which was proximal to most of the TLS scan locations. Comparisons of structural metrics between UAS and TLS showed similarities in metrics like profile and planform curvatures, yet indicated UAS surveys produced higher values of surface complexity and slope. Results indicate that UAS photogrammetry can produce robust oyster reef structural metrics that can be highly useful in oyster conservation and restoration.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45443639","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}
M. Zoffoli, P. Gernez, S. Oiry, L. Godet, S. Dalloyau, B. F. Davies, L. Barillé
Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.
{"title":"Remote sensing in seagrass ecology: coupled dynamics between migratory herbivorous birds and intertidal meadows observed by satellite during four decades","authors":"M. Zoffoli, P. Gernez, S. Oiry, L. Godet, S. Dalloyau, B. F. Davies, L. Barillé","doi":"10.1002/rse2.319","DOIUrl":"https://doi.org/10.1002/rse2.319","url":null,"abstract":"Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42839478","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}
Rewilding has been suggested as an effective strategy for addressing environmental challenges such as the intertwined biodiversity and climate change crises, but there is little information to guide the monitoring of rewilding projects. Since rewilding focuses on enhancing ecosystem functionality, with no defined endpoint, monitoring strategies used in restoration are often inappropriate, as they typically focus on assessing species composition, or the ecological transition of an ecosystem towards a defined desired state. We here discuss how satellite remote sensing can provide an opportunity to address existing knowledge and data gaps in rewilding science. We first discuss how satellite remote sensing is currently being used to inform rewilding initiatives and highlight current barriers to the adoption of this type of technology by practitioners and scientists involved with rewilding. We then identify opportunities for satellite remote sensing to help address current knowledge gaps in rewilding, including gaining a better understanding of the role of animals in ecosystem functioning; improving the monitoring of landscape‐scale connectivity; and assessing the impacts of rewilding on the conservation status of rewilded sites. Though significant barriers remain to the widespread use of satellite remote sensing to monitor rewilding projects, we argue that decisions on monitoring approaches and priorities need to be part of implementation plans from the start, involving both remote sensing experts and ecologists. Making use of the full potential of satellite remote sensing for rewilding ultimately requires integrating species and ecosystem perspectives at the monitoring, knowledge‐producing and decision‐making levels. Such an integration will require a change in know‐how, necessitating increased inter‐disciplinary interactions and collaborations, as well as conceptual shifts in communities and organizations traditionally involved in biodiversity conservation.
{"title":"Current and future opportunities for satellite remote sensing to inform rewilding","authors":"N. Pettorelli, Henrike Schulte to Bühne","doi":"10.1002/rse2.321","DOIUrl":"https://doi.org/10.1002/rse2.321","url":null,"abstract":"Rewilding has been suggested as an effective strategy for addressing environmental challenges such as the intertwined biodiversity and climate change crises, but there is little information to guide the monitoring of rewilding projects. Since rewilding focuses on enhancing ecosystem functionality, with no defined endpoint, monitoring strategies used in restoration are often inappropriate, as they typically focus on assessing species composition, or the ecological transition of an ecosystem towards a defined desired state. We here discuss how satellite remote sensing can provide an opportunity to address existing knowledge and data gaps in rewilding science. We first discuss how satellite remote sensing is currently being used to inform rewilding initiatives and highlight current barriers to the adoption of this type of technology by practitioners and scientists involved with rewilding. We then identify opportunities for satellite remote sensing to help address current knowledge gaps in rewilding, including gaining a better understanding of the role of animals in ecosystem functioning; improving the monitoring of landscape‐scale connectivity; and assessing the impacts of rewilding on the conservation status of rewilded sites. Though significant barriers remain to the widespread use of satellite remote sensing to monitor rewilding projects, we argue that decisions on monitoring approaches and priorities need to be part of implementation plans from the start, involving both remote sensing experts and ecologists. Making use of the full potential of satellite remote sensing for rewilding ultimately requires integrating species and ecosystem perspectives at the monitoring, knowledge‐producing and decision‐making levels. Such an integration will require a change in know‐how, necessitating increased inter‐disciplinary interactions and collaborations, as well as conceptual shifts in communities and organizations traditionally involved in biodiversity conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47075770","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}
E. Kleiven, Pedro G. Nicolau, S. Sørbye, J. Aars, N. Yoccoz, R. Ims
Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi‐annual population cycles of voles and other small mammal species they interact with.
{"title":"Using camera traps to monitor cyclic vole populations","authors":"E. Kleiven, Pedro G. Nicolau, S. Sørbye, J. Aars, N. Yoccoz, R. Ims","doi":"10.1002/rse2.317","DOIUrl":"https://doi.org/10.1002/rse2.317","url":null,"abstract":"Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi‐annual population cycles of voles and other small mammal species they interact with.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48441928","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}
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}
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