Non-invasive UAV-based monitoring of a wetland indicator dragonfly in a floating-mat wetland: Implications for habitat assessment and management

Non-invasive UAV-based monitoring of a wetland indicator dragonfly in a floating-mat wetland: Implications for habitat assessment and management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-invasive UAV-based monitoring of a wetland indicator dragonfly in a floating-mat wetland: Implications for habitat assessment and management Hideyuki Niwa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8904919/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Wetlands Ecology and Management → Version 1 posted 9 You are reading this latest preprint version Abstract Effective wetland management requires reliable indicator species and monitoring methods that minimize ecosystem disturbance. This study presents a non-invasive UAV-based approach for monitoring the distribution and habitat associations of Nannophya pygmaea, a wetland indicator dragonfly, in a protected floating-mat wetland in Japan. High-resolution aerial imagery enabled direct detection of individuals across the entire wetland, while multispectral and thermal data were used to characterize vegetation structure and surface temperature. Across eight surveys conducted during a single emergence season, 558 adult individuals were identified, allowing quantification of within-season dynamics and sex-specific spatial distribution. Logistic regression analyses revealed clear associations between occurrence probability and UAV-derived environmental variables, with surface temperature exerting the strongest influence. Vegetation structure and thermal conditions jointly explained fine-scale habitat differentiation between males and females. The approach allows repeated monitoring without entering fragile wetland habitats and provides spatially explicit information relevant to habitat condition assessment. These findings demonstrate that UAV-based monitoring of indicator species can support adaptive wetland management by linking species responses to microhabitat structure and environmental change. monitoring wetlands dragonfly Nannophya pygmaea ecosystem management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Monitoring involves collecting and analyzing data on a given species and continually evaluating the current situation relative to management goals (Alexander et al. 2012 ). In monitoring, it is crucial to accurately understand changes in the distribution of indicator species across the survey area and to identify the factors driving these changes (Dronova et al. 2021 ). Monitoring is frequently based on field surveys, which are time-consuming, labor-intensive, and constrained by survey timing, resulting in a limited range of species being monitored (Rominger and Meyer 2019 ). In addition, field-based monitoring is restricted by various factors, including difficult site access, survey-related safety risks, and ecosystem disturbance caused by the surveys themselves (Michez et al. 2016 ; Rominger and Meyer 2019 ). Wetlands exemplify environments that are particularly challenging to monitor (Michez et al. 2016 ). Remote sensing can mitigate many of the limitations associated with conventional monitoring. Recently, uncrewed aerial vehicle (UAV) platforms have been added to remote sensing tools such as satellites and crewed aircraft. Although UAV platforms are inferior to satellites and crewed aircraft in terms of area coverage, they offer advantages that are not matched by other remote sensing platforms. These advantages include high spatial resolution, cost efficiency, flexibility in survey planning, and reduced survey time for relatively small target areas (Pádua et al. 2017 ). The application of UAV platforms enables efficient monitoring through improved identification of indicator species distributions across survey areas (Dronova et al. 2021 ). Researchers have emphasized the importance of optimizing monitoring efforts and reallocating saved time to ecosystem management (Knoth et al. 2013 ; Harvey et al. 2019 ; Langhammer 2019 ), as well as the growing role of UAV platforms in monitoring. However, applications of UAV platforms targeting relatively small insects remain largely limited to the detection of pest outbreaks in agricultural systems (Filho et al. 2020 ; Moses-Gonzales and Brewer 2021 ). In such cases, the insects themselves are not directly detected; instead, detection relies on changes in crops caused by pest activity. Direct detection of relatively small insects using UAVs has been demonstrated in only a few cases, such as Niwa and Hirata ( 2022 ), making this a particularly challenging research topic. Niwa and Hirata ( 2022 ) successfully mapped the distribution of the world’s smallest dragonfly, Nannophya pygmaea , in wetlands using a compact UAV equipped with a telephoto lens. This method made it possible to identify the distribution of N. pygmaea in wetlands that had previously been difficult to survey because floating mats hinder exploration, survey areas are extensive, and access is legally restricted. However, no method has yet been presented for monitoring within-season variation and spatial distribution over time. Meanwhile, effective monitoring requires not only tracking changes in the distribution of indicator species across entire survey areas but also identifying the factors driving those changes (Dronova et al. 2021 ). Therefore, if environmental factors affecting N. pygmaea habitat can be investigated concurrently with distribution surveys using UAV platforms, this approach could be developed into a new monitoring method for wetlands. Furthermore, the expanding application of UAV platforms in ecosystem research is generating diverse approaches to data collection, processing, and analysis, making the establishment of repeatable monitoring workflows increasingly important (Gomez and Purdie 2016 ; Tmušić et al. 2020 ). Floating-mat wetlands are particularly vulnerable to physical disturbance and hydrological alteration, making the development of non-invasive monitoring methods a priority for sustainable management. Indicator species that reflect fine-scale habitat structure may provide practical tools for assessing wetland condition and guiding adaptive management strategies. Therefore, this study aims to propose a repeatable monitoring workflow that simultaneously investigates the distribution of Nannophya pygmaea , a wetland indicator species, and the environmental factors affecting its habitat using two types of UAVs in wetlands where field surveys are difficult to conduct. 2. Methods 2.1. Target Species The target species for monitoring was Nannophya pygmaea , a dragonfly species inhabiting wetlands (Fig. 1 ). N. pygmaea is approximately 20 mm in body length, making it Japan’s smallest dragonfly and among the smallest worldwide (Ishida et al. 1988). N. pygmaea is distributed across East Asia, Southeast Asia, and Australia (Ishida et al. 1988). In Japan, its distribution is localized to Honshu, Shikoku, and Kyushu (Yoshida et al. 2004). The distribution area of N. pygmaea in Japan has declined markedly due to wetland loss caused by vegetation succession and land development (Yabu and Nakajima 1996). Consequently, many prefectures classify N. pygmaea as a threatened or near-threatened species. It is a highly rare species specialized to wetlands and is considered an indicator of wetland health. Thus, N. pygmaea is suitable as an indicator species for wetland monitoring. 2.2. Study Site The study site was the Mizorogaike wetland, located in Kita Ward, Kyoto City, Kyoto Prefecture, Japan (Fig. 2 ). The Mizorogaike wetland is a pond with an area of approximately 8 ha and a maximum depth of 2 m. Moreover, it contains an extensive floating mat, which is rare in the lowlands of western Japan, covering about one-third of the water surface (Tsujino et al. 2007 ). Although the Mizorogaike wetland borders an urban area, it represents a valuable ecosystem inhabited by glacial-period relict species. The Mizorogaike wetland is designated as a national natural monument in Japan. Together with Osegahara, the Mizorogaike wetland is known for its abundance of N. pygmaea in Japan (Yoshida et al. 2004). 2.3. Dragonfly Survey Dragonfly surveys were conducted using a DJI Mavic 3T (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China). A flight plan created in UgCS (SPH Engineering, Latvia, EU) was imported into DJI Pilot 2 and used to acquire photographs. Survey lines were spaced 10 m apart to cover the entire wetland, with 503 waypoints placed at 10 m intervals along the survey lines (Fig. 3 ). The UAV flew at an altitude of 6 m above the ground, paused for 1 s at each waypoint, and captured still images (4000 × 3000 pixels) using a 7× telephoto lens. The area covered by a single still image was approximately 1.13 m × 1.5 m. Preliminary trials conducted at flight altitudes of 6 m and 10 m confirmed that surface vegetation was not disturbed by downdrafts, that the UAV did not collide with low shrubs scattered throughout the wetland, and that N. pygmaea individuals could be identified. Based on these trials, the flight altitude was set at 6 m. Surveys were conducted eight times at approximately two-week intervals between June 10 and September 24, 2024 (Table 1 ). During each survey, instantaneous wind speed and air temperature were measured using a BENETECH GM816 anemometer, and spherical photographs were taken using a RICOH THETA SC to record weather conditions (Fig. 4 ). In addition, a DJI D-RTK 2 mobile station was deployed to correct UAV positional information using real-time kinematic (RTK) positioning. Table 1 Survey date, wind speed, and air temperature. The DJI M3T was used for distribution surveys of the N. pygmaea , while the DJI Matrice 300 was used for surveys of habitat factors. Date DJI M3T DJI Matrice 300 Wind speed (instantaneous) (m/s) Air temperature (Celsius) Start End Start End 2024-06-10 9:42 10:54 9:58 10:16 0.7 30.7 2024-06-25 9:48 11:04 9:52 10:09 0 28 2024-07-09 9:44 10:59 10:08 10:26 0.9 31.6 2024-07-23 9:33 10:42 10:15 10:32 1.3 33.5 2024-08-05 9:41 10:50 10:15 10:28 0 37.6 2024-08-22 9:44 10:54 10:18 10:36 1.8 33.7 2024-09-10 9:39 10:57 10:05 10:23 2.2 30.2 2024-09-24 9:40 10:49 10:09 10:26 0 33.7 Captured images were displayed at 100% magnification using an image viewer, and the presence or absence of N. pygmaea was visually identified for each image (Fig. 5 ). Each image required approximately 20–30 s for inspection. Individuals of N. pygmaea were recorded and classified as males, immature males, or females. Mature males and immature males exhibit different environmental preferences (Yabu and Nakajima 1996) and can be distinguished based on coloration differences (red versus orange); therefore, they were classified separately. Females were identified by their characteristic striped pattern. The DJI Mavic 3T records the geographic coordinates of image locations. Using ArcGIS Pro (Esri Japan Corporation, Tokyo, Japan), point data were generated from the latitude and longitude recorded for each image. By combining these point data with the identification results for N. pygmaea , distribution datasets were created. Data from all eight surveys in which N. pygmaea was present were used to calculate population density for each category (male, immature male, and female) using kernel density estimation in ArcGIS Pro. The parameters were set as follows: no population field, output cell size of 0.5 m, and area units in square meters. 2.4. Environmental Factor Survey Environmental factor surveys were conducted using a MicaSense Altum multispectral sensor (AgEagle Aerial Systems Inc., Kansas, USA) mounted on a DJI Matrice 300. The MicaSense Altum is equipped with five sensors that record visible-to-near-infrared wavelengths (400–900 nm) and one sensor that records thermal infrared wavelengths (8000–14,000 nm). The spatial resolutions of the thermal infrared sensor and the other five sensors are 160 × 120 pixels and 2064 × 1544 pixels, respectively. The flight plan created in UgCS was imported into DJI Pilot 2, and the same flight plan was used for each survey. Using a ground sampling distance of 3 cm per pixel, flight plans were designed with forward and side overlaps of 80% and 70%. Environmental surveys were conducted eight times on the same days as the dragonfly surveys. The Altum sensor was operated together with an optical Downwelling Light Sensor (DLS 2) to record irradiance and sun-angle correction information. Images were processed using photogrammetric methods to produce orthomosaics with a spatial resolution of 3 cm per pixel, corrected using DLS 2 parameters. Five ground control points were established within the survey area, and their positions were corrected using XYZ coordinates obtained from RTK surveys during photogrammetric processing. Photogrammetric processing was performed using Agisoft Metashape Professional version 2.1.2 (Agisoft LLC, St. Petersburg, Russia). To evaluate vegetation density, the normalized difference vegetation index (NDVI) was calculated from orthomosaic images using near-infrared (NIR) and red (R) wavelengths as follows: NDVI = (NIR − R) / (NIR + R) To evaluate water area distribution, the normalized difference water index (NDWI) was calculated from orthomosaic images using near-infrared (NIR) and green (G) wavelengths as follows: NDWI = (G − NIR) / (G + NIR) Data Analysis Circular buffers with a radius of 0.5 m were generated around each image location point. To ensure that each buffer fit within the area captured by a single still image used for the dragonfly survey, the buffer radius was set to 0.5 m. Mean surface temperature (ST), NDVI, and NDWI values within each buffer polygon were calculated using the zonal statistics function in ArcGIS Pro. Sex was treated as a binary response variable (male = 1, female = 0), with immature males classified as males. A full logistic regression model was fitted with all UAV-derived environmental variables—normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (ST)—included as explanatory variables. All continuous predictors were standardized using z-score transformation prior to analysis to allow direct comparison of effect sizes among variables. Model selection was not performed because all predictors were included based on a priori ecological hypotheses. The relative importance of environmental variables was assessed using the absolute values of standardized regression coefficients (|β|), while the direction of sex-specific associations was interpreted based on the sign of the coefficients. Positive coefficients indicate environmental conditions associated with a higher probability of male occurrence, whereas negative coefficients indicate conditions associated with a higher probability of female occurrence. 3. Results Approximately 558 adult dragonflies were identified across the eight surveys (Table 2 ). The majority of individuals were males (439 individuals, 79%), followed by 82 females (15%) and 37 immature males (7%). July 9 recorded the highest number of Nannophya pygmaea , and more than 100 individuals were identified between June 25 and August 5. Females were not detected after August 22. Figure 6 shows the spatial distribution of N. pygmaea on each survey date. From June 25 to July 23, when female abundance was relatively high, the distributions of males and females overlapped broadly across the survey area. Table 2 Within-season variation in identified number of N. pygmaea Type 06–10 06–25 07–09 07–23 08 − 05 08–22 09–10 09–24 Total Male 17 52 113 108 97 34 15 3 439 Immature male 3 16 15 1 1 1 37 Female 4 33 21 16 8 82 Total 24 101 149 125 106 34 16 3 558 Figure 7 presents population density maps calculated from all locations at which N. pygmaea individuals were observed during the eight surveys. The spatial patterns of population density differed between males and females. Predicted probability curves derived from the logistic regression model further illustrated how sex-specific occurrence probabilities varied along environmental gradients (Fig. 9 ). For each environmental variable, predicted probabilities were calculated while holding the other variables at their mean values. The shapes of the prediction curves indicated monotonic changes in the probability of male occurrence across gradients of NDVI, NDWI, and surface temperature (ST), consistent with the direction and magnitude of the standardized regression coefficients. Environmental variables with larger absolute standardized coefficients produced steeper prediction curves, indicating stronger differentiation between male (including immature male) and female occurrence environments. In contrast, variables with smaller effect sizes showed more gradual changes in predicted probabilities across their observed ranges. These model-based predictions provide an intuitive visualization of sex-specific associations with microhabitat conditions inferred from UAV-derived environmental data. The logistic regression model revealed clear associations between sex and UAV-derived environmental variables. Because all predictors were standardized, the absolute values of the regression coefficients were directly comparable and were therefore used to assess relative importance (Fig. 10 ). Among the three variables examined, surface temperature (ST) exhibited the largest standardized effect size, indicating that this variable contributed most strongly to sex-specific differences in occurrence environments. The signs of the standardized coefficients further indicated the direction of these associations: positive coefficients corresponded to environmental conditions associated with a higher probability of male occurrence, whereas coefficients with the opposite sign indicated conditions more strongly associated with female occurrence. These results suggest that males (including immature males) and females differ systematically in their use of microhabitats characterized by vegetation structure, moisture availability, and surface temperature. 4. Discussion 4.1. Monitoring the Distribution of N. Pygmaea Within-season variation in the emergence of Nannophya pygmaea and changes in its spatial distribution were identified by applying the same flight plan and conducting regular photographic surveys. Prior to this study, the only available information on the distribution of N. pygmaea in the Mizorogaike wetland was that it occurred on the floating mat in the central part of the pond. In contrast, the present study enabled tracking of spatial distribution changes and identification of differences between males and females. With respect to within-season variation in emergence, previously available information was limited to broad regional descriptions, such as reports that many adults appear from May to June in Honshu, Japan (Yabu and Nakajima 1996). In this study, within-season variation in the emergence of N. pygmaea was clarified specifically for the Mizorogaike wetland. Males are easily visible because of their red coloration, which stands out in areas with sparse vegetation. In contrast, females are often found perched on relatively tall plants and are difficult to detect due to their cryptic coloration. Therefore, females are expected to be less detectable than males. The females identified in this study are considered likely to have emerged near male territories for mating. In addition, other dragonfly species occur in the Mizorogaike wetland, including species with red coloration similar to that of N. pygmaea . However, due to clear differences in body size, misidentification as N. pygmaea is unlikely. Although the possibility that the same individual appeared in multiple still images cannot be completely excluded, this likelihood is considered low given the long resting periods on plants and the infrequent long-distance movements of N. pygmaea , in combination with the UAV flight speed and spacing between survey lines. In addition to N. pygmaea , damselflies were the most frequently photographed taxa. Similar to N. pygmaea , damselflies tend to move slowly and remain stationary on plants, whereas species that move rapidly were rarely photographed. Therefore, the survey method used in this study is considered applicable primarily to dragonfly species that exhibit limited movement. At the study site, much of the surface is composed of floating mat, making on-foot surveys difficult. This condition is considered the primary reason why detailed distribution information for N. pygmaea had not previously been available. The use of UAVs in this study to quantify the distribution of N. pygmaea in areas that are difficult to survey can therefore be regarded as a significant methodological advance. In addition, wetlands inhabited by N. pygmaea are particularly vulnerable to disturbance caused by surveys; however, the UAV-based approach employed here allows surveys to be conducted without entering the wetland, thereby minimizing environmental disturbance. Accurate monitoring of rare species populations is essential for understanding long-term population trends and identifying habitat threats (Reckling et al. 2021 ), and long-term population monitoring is a central component of conservation efforts (Alexander et al. 2012 ). Accordingly, the method developed in this study is considered useful for long-term monitoring of N. pygmaea and is expected to contribute substantially to its conservation. When developing new survey methods, comparison with conventional approaches, such as mark–recapture techniques and exuviae sampling, is necessary. As noted above, conventional survey methods are difficult to implement in deep mud pools, which complicates direct comparisons. However, comparisons with conventional methods may be feasible by restricting surveys to areas with relatively easy access, and this should be addressed in future studies. 4.2. Monitoring of Environmental Factors The sex-specific associations detected in this study are consistent with previously reported life-history traits and habitat-use patterns of Nannophya pygmaea . Yabu and Nakajima (1996) reported that immature adults move after emergence into grasslands near water with relatively tall and dense vegetation, whereas mature males subsequently return to water-adjacent areas and establish territories by perching on plants in grasslands characterized by low vegetation and small, shallow open water. In this context, NDVI, which reflects vegetation amount and greenness, can be interpreted as an indicator of vegetation height and density in wetland grasslands. Accordingly, the observed relationships between NDVI and sex-specific occurrence probabilities likely capture this ontogenetic and behavioral shift in habitat use. The tendency for males to be associated with warmer microhabitats is also ecologically plausible, as elevated body temperature enhances reproductive activity and performance in dragonflies (Schreiner et al. 2020 ), and males occupying open, low-vegetation territories near shallow water may benefit from increased solar radiation. Together, these results suggest that fine-scale variation in vegetation structure and thermal conditions, detectable using UAV-derived indices, mediates sex-specific spatial segregation in N. pygmaea , linking individual behavior and reproductive ecology to microhabitat heterogeneity within wetland landscapes. In monitoring, it is essential to understand changes in the distribution of indicator species across survey areas and to clarify the factors causing these changes (Dronova et al. 2021 ). By using two types of UAVs, this study attempted to simultaneously monitor the distribution of N. pygmaea and associated environmental factors. The results of the environmental factor analysis are consistent with known habitat characteristics of N. pygmaea , demonstrating that this approach can simultaneously assess species distribution and the environmental conditions influencing habitat use. In the Mizorogaike wetland, environmental alterations such as vegetation changes caused by invasion of deer ( Cervus nippon ) have become a concern (Niwa 2021 ). The UAV-based approach developed here may enable monitoring of the impacts of such vegetation changes on the distribution of N. pygmaea . In addition, the still images acquired in this study captured small and rare wetland plant species, such as Drosera rotundifolia and Utricularia bifida , allowing their spatial distribution and within-season variation to be assessed in a manner similar to that used for N. pygmaea . Because of the extremely high spatial resolution of the imagery, many other plant species can also be identified, suggesting potential applications for vegetation classification and detection of invasive species. These features can be assessed simultaneously while visually identifying N. pygmaea . 4.3. Management Implications The UAV-based monitoring framework developed in this study provides a practical tool for wetland managers seeking to assess ecosystem condition while minimizing disturbance to sensitive habitats. In floating-mat wetlands such as Mizorogaike, conventional ground surveys are difficult and may cause physical damage to fragile substrates. The non-invasive approach presented here enables repeated, spatially explicit assessment of indicator species distribution and associated habitat conditions without entering the wetland. Because N. pygmaea responds to fine-scale variation in vegetation structure and surface temperature, changes in its spatial distribution may serve as an early signal of environmental alteration, including vegetation shifts caused by deer invasion or hydrological modification. The integration of species detection with UAV-derived environmental indices allows managers to link biological responses directly to habitat characteristics, facilitating evidence-based decision making. Moreover, the repeatable flight design and standardized data processing workflow support long-term monitoring programs and allow temporal comparisons across years. This framework may be extended to other wetland indicator species and plant communities, thereby enhancing the capacity of managers to detect ecosystem change and evaluate restoration outcomes in fragile wetland systems. 5. Conclusion The methodology developed in this study enables the simultaneous assessment of population dynamics and spatial distribution changes of Nannophya pygmaea , together with environmental factors affecting its habitat. It allows repeated surveys to be conducted without entering wetlands. This approach represents an innovative monitoring method that leverages the advantages of UAV platforms and is considered highly suitable for long-term monitoring. By enabling long-term monitoring of N. pygmaea , an indicator species for wetlands, this methodology is expected to contribute substantially to the ecosystem management of the Mizorogaike wetland as well as other wetland ecosystems. Declarations All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Compliance with Ethical Standards Disclosure of potential conflicts of interest Funding This research received no external funding. Research involving Human Participants and/or Animals Not applicable Informed consent Not applicable Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Author Contribution H. N. wrote the main manuscript text and prepared all figures. H. N. designed an investigation plan and conducted a field survey. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Alexander, H.M., Reed, A.W., Kettle, W.D., Slade, N.A., Bodbyl Roels, S.A., Collins, C.D., Salisbury, V., 2012. Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers. PLoS One 7. https://doi.org/10.1371/journal.pone.0052762 Dronova, I., Kislik, C., Dinh, Z., Kelly, M., 2021. A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data. Drones 5, 45. https://doi.org/10.3390/drones5020045 Filho, F.H.I., Heldens, W.B., Kong, Z., De Lange, E.S., 2020. Drones: Innovative technology for use in precision pest management. J. Econ. Entomol. https://doi.org/10.1093/jee/toz268 Gomez, C., Purdie, H., 2016. UAV- based Photogrammetry and Geocomputing for Hazards and Disaster Risk Monitoring – A Review. Geoenvironmental Disasters 3, 23. https://doi.org/10.1186/s40677-016-0060-y Harvey, M.C., Hare, D.K., Hackman, A., Davenport, G., Haynes, A.B., Helton, A., Lane, J.W., Briggs, M.A., 2019. Evaluation of stream and wetland restoration using UAS-based thermal infrared mapping. Water (Switzerland) 11. https://doi.org/10.3390/w11081568 Ishida, S., Kozima, K., 1988. Illustrated guide for identification of the Japanese Odonata., 107–108. Tokai University Press, Hirathuka Knoth, C., Klein, B., Prinz, T., Kleinebecker, T., 2013. Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs. Appl. Veg. Sci. 16, 509–517. https://doi.org/10.1111/avsc.12024 Langhammer, J., 2019. UAV Monitoring of Stream Restorations. Hydrology. https://doi.org/10.3390/hydrology6020029 Michez, A., Piégay, H., Jonathan, L., Claessens, H., Lejeune, P., 2016. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery. International Journal of Applied Earth Observation and Geoinformation 44, 88–94. https://doi.org/https://doi.org/10.1016/j.jag.2015.06.014 Moses-Gonzales, N., Brewer, M.J., 2021. A Special Collection: Drones to Improve Insect Pest Management. J. Econ. Entomol. https://doi.org/10.1093/jee/toab081 Niwa, H., 2021. Assessing the activity of deer and their influence on vegetation in a wetland using automatic cameras and low altitude remote sensing (LARS). Eur. J. Wildl. Res. 67, 1–7. https://doi.org/10.1007/s10344-020-01450-6 Niwa, H., Hirata, T., 2022. A New Method for Surveying the World’s Smallest Class of Dragonfly in Wetlands Using Unoccupied Aerial Vehicles. Drones 6, 427. https://doi.org/10.3390/drones6120427 Pádua, L., Vanko, J., Hruška, J., Adão, T., Sousa, J.J., Peres, E., Morais, R., 2017. UAS, sensors, and data processing in agroforestry: a review towards practical applications. Int. J. Remote Sens. 38, 2349–2391. https://doi.org/10.1080/01431161.2017.1297548 Reckling, W., Mitasova, H., Wegmann, K., Kauffman, G., Reid, R., 2021. Efficient drone-based rare plant monitoring using a species distribution model and ai-based object detection. Drones 5. https://doi.org/10.3390/drones5040110 Rominger, K., Meyer, S.E., 2019. Application of UAV-Based methodology for census of an endangered plant species in a fragile habitat. Remote Sens. (Basel). 11. https://doi.org/10.3390/rs11060719 Schreiner, G.D., Duffy, L.A., Brown, J.M., 2020. Thermal response of two sexually dimorphic Calopteryx (Odonata) over an ambient temperature range. Ecol. Evol. 10, 12341–12347. https://doi.org/https://doi.org/10.1002/ece3.6864 Tmušić, G., Manfreda, S., Aasen, H., James, M.R., Gonçalves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J.J., Mészáros, J., Zhuang, R., Johansen, K., Malbeteau, Y., DeLima, I.P., Davids, C., Herban, S., McCabe, M.F., 2020. Current practices in UAS-based environmental monitoring. Remote Sens. (Basel). 12, 1–35. https://doi.org/10.3390/rs12061001 Tsujino R., Matsui, K., Ushimaru, A., Seo, A., Kawase, D., Uchihasi, H., Suzuki, K., Takahashi, J., Yumoto, T., Takemon, Y., 2007. Invasion of the Mizorogaike Wetland by sika deer, and their effects on vegetation, Japanese Journal of Conservation Ecology,12: 20–27 Yabu, S., nakashima, A., 1996. Ecological studies on the conservation of Nannophya pygmaea Rambur populations and habitats. J. Japanese Inst. Landsc. Archit. 60, 324–328, doi: 10.5632/jila.60.324 . Yoshita, S., Minami, Y., Ueda, T., 2004. Water chemistry of several habitats of a tiny dragonfly, Nannophya pygmaea Rambur. Japanese J. Environ. Entomol. Zool. 15, 13–17, doi: 10.11257/jjeez.15.13 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Wetlands Ecology and Management → Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8904919","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598466914,"identity":"0575d604-a752-4c88-9538-17512e66156e","order_by":0,"name":"Hideyuki Niwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZiBmbJBgbAAxEyoSYOJsxGo5Q4wWBrAWBogWxrYE/CpBwJydx/AD4w4L2X6JHGODh/PSEvv7DzB++MHAl4dLi2Uzj7EE4xkJ45kzcowTErflJM64kcAs2cPAVoxLi8Fh3g0SjG0SiRtu5BgfSNxWkdhwg4FBGuiXxAbcWjb/AGnZD9YypyJx/vkDzL8JaNkGsUUC5LCGnMQNBxLYCNjC/80isU3CeMaZZ8UGCcfSjDfeSGyz7DHA45fzx5JvfGyrk+1vT94s+aMmWXbe+cOHb/yoOIYzxMAgAUQIJMC4oDgyOJaAXS0y4D+Awq0hQssoGAWjYBSMEAAAM3VaO6DiGXEAAAAASUVORK5CYII=","orcid":"","institution":"Kyoto University of Advanced Science","correspondingAuthor":true,"prefix":"","firstName":"Hideyuki","middleName":"","lastName":"Niwa","suffix":""}],"badges":[],"createdAt":"2026-02-18 01:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8904919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8904919/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11273-026-10144-w","type":"published","date":"2026-04-27T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104402358,"identity":"6fdbf5a0-aa44-4717-8f87-ccea507b433d","added_by":"auto","created_at":"2026-03-11 12:15:10","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93033,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/06ceea2a5d877eb23b52d3a3.jpeg"},{"id":104012707,"identity":"c8bdcbbf-f510-422a-9678-62c15c7d1f58","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eN. pygmaea\u003c/em\u003e, male (left), immature male (center), and female (right). These were photographed at the Mizorogaike wetland on July 9, 2024. Photographed by Hideyuki Niwa.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/ef706a0a64f3ae06afb2dcce.jpeg"},{"id":104402962,"identity":"7b95e9e6-8a58-4086-aff8-89cd8dbb71dd","added_by":"auto","created_at":"2026-03-11 12:17:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":314383,"visible":true,"origin":"","legend":"\u003cp\u003eStudy site. This map is based on the GSI Tiles collection published by Geospatial Information Authority of Japan.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/eb047ea837b0a1afdc7efeee.jpeg"},{"id":104012709,"identity":"34ad4335-26fd-49b9-9611-bd3a335fffd5","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":344612,"visible":true,"origin":"","legend":"\u003cp\u003eFlight plan used for the distribution survey of \u003cem\u003eN. pygmaea\u003c/em\u003e. Photos were taken at 503 waypoints.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/8355a63bd3a076a2a8a0968e.jpeg"},{"id":104012704,"identity":"95d4801f-c05f-42ca-8b74-d6b8db56cebc","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1198293,"visible":true,"origin":"","legend":"\u003cp\u003eSpherical photographs taken to record the weather conditions during the survey.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/8c6cbb124f6cf9954c84d009.jpeg"},{"id":104012712,"identity":"bddcf1fa-bad7-4caf-a5af-b95726abcbc2","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3643880,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the identification of \u003cem\u003eN. pygmaea\u003c/em\u003e, a male, an immature male, and a female. Date taken: June 25, 2024. \u003cem\u003eN. pygmaea\u003c/em\u003e are visible at the arrow's starting point, and the circle shows their enlarged images.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/2376f8809a090c64844326b8.jpeg"},{"id":104012706,"identity":"64aa984e-a1b4-49ec-ad3b-135f4664e7fd","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1794331,"visible":true,"origin":"","legend":"\u003cp\u003eWithin-season variation in spatial distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e. The background orthomosaic image was created from images taken on the same day.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/f24fef9666cb0ae616dfd72f.jpeg"},{"id":104012711,"identity":"3b24d0f7-8ee0-4e30-a98a-f80aad68f2d2","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":573392,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation density of \u003cem\u003eN. pygmaea\u003c/em\u003e, calculated by combining the results of the eight surveys.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/1a7632559217d3fc631f1622.jpeg"},{"id":104402359,"identity":"1bb70fbf-9567-4526-8b6c-148305ba0f94","added_by":"auto","created_at":"2026-03-11 12:15:10","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":987520,"visible":true,"origin":"","legend":"\u003cp\u003eST and NDVI/NDWI were used as habitat environment factors for \u003cem\u003eN. pygmaea\u003c/em\u003e. Example for June 25, 2024.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/7c471966ebcfb9333e1a38d5.jpeg"},{"id":104012705,"identity":"466b78d5-30e4-4238-9ac7-a2bb7a8fca72","added_by":"auto","created_at":"2026-03-05 16:10:59","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":229483,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted occurrence probability by sex as a function of UAV-derived environmental variables. Curves show fitted probabilities from a logistic regression model with sex (male vs female) as the binary response variable and standardized NDVI, NDWI, and surface temperature (ST) as predictors. Dashed line indicate 95% confidence intervals, and non-focal variables were held at their mean values when generating each curve.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/076bf9a844222c09bb1e25cf.jpeg"},{"id":108438090,"identity":"fa0c73b7-0f0e-4fb2-9916-5f3cba69e94a","added_by":"auto","created_at":"2026-05-04 16:07:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9449702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8904919/v1/f8e02d17-e460-4636-abb1-688cf4f33314.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-invasive UAV-based monitoring of a wetland indicator dragonfly in a floating-mat wetland: Implications for habitat assessment and management","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMonitoring involves collecting and analyzing data on a given species and continually evaluating the current situation relative to management goals (Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In monitoring, it is crucial to accurately understand changes in the distribution of indicator species across the survey area and to identify the factors driving these changes (Dronova et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Monitoring is frequently based on field surveys, which are time-consuming, labor-intensive, and constrained by survey timing, resulting in a limited range of species being monitored (Rominger and Meyer \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, field-based monitoring is restricted by various factors, including difficult site access, survey-related safety risks, and ecosystem disturbance caused by the surveys themselves (Michez et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rominger and Meyer \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Wetlands exemplify environments that are particularly challenging to monitor (Michez et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRemote sensing can mitigate many of the limitations associated with conventional monitoring. Recently, uncrewed aerial vehicle (UAV) platforms have been added to remote sensing tools such as satellites and crewed aircraft. Although UAV platforms are inferior to satellites and crewed aircraft in terms of area coverage, they offer advantages that are not matched by other remote sensing platforms. These advantages include high spatial resolution, cost efficiency, flexibility in survey planning, and reduced survey time for relatively small target areas (P\u0026aacute;dua et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The application of UAV platforms enables efficient monitoring through improved identification of indicator species distributions across survey areas (Dronova et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Researchers have emphasized the importance of optimizing monitoring efforts and reallocating saved time to ecosystem management (Knoth et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Harvey et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Langhammer \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as the growing role of UAV platforms in monitoring. However, applications of UAV platforms targeting relatively small insects remain largely limited to the detection of pest outbreaks in agricultural systems (Filho et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moses-Gonzales and Brewer \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In such cases, the insects themselves are not directly detected; instead, detection relies on changes in crops caused by pest activity. Direct detection of relatively small insects using UAVs has been demonstrated in only a few cases, such as Niwa and Hirata (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), making this a particularly challenging research topic.\u003c/p\u003e \u003cp\u003eNiwa and Hirata (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) successfully mapped the distribution of the world\u0026rsquo;s smallest dragonfly, \u003cem\u003eNannophya pygmaea\u003c/em\u003e, in wetlands using a compact UAV equipped with a telephoto lens. This method made it possible to identify the distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e in wetlands that had previously been difficult to survey because floating mats hinder exploration, survey areas are extensive, and access is legally restricted. However, no method has yet been presented for monitoring within-season variation and spatial distribution over time. Meanwhile, effective monitoring requires not only tracking changes in the distribution of indicator species across entire survey areas but also identifying the factors driving those changes (Dronova et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, if environmental factors affecting \u003cem\u003eN. pygmaea\u003c/em\u003e habitat can be investigated concurrently with distribution surveys using UAV platforms, this approach could be developed into a new monitoring method for wetlands. Furthermore, the expanding application of UAV platforms in ecosystem research is generating diverse approaches to data collection, processing, and analysis, making the establishment of repeatable monitoring workflows increasingly important (Gomez and Purdie \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tmušić et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFloating-mat wetlands are particularly vulnerable to physical disturbance and hydrological alteration, making the development of non-invasive monitoring methods a priority for sustainable management. Indicator species that reflect fine-scale habitat structure may provide practical tools for assessing wetland condition and guiding adaptive management strategies.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to propose a repeatable monitoring workflow that simultaneously investigates the distribution of \u003cem\u003eNannophya pygmaea\u003c/em\u003e, a wetland indicator species, and the environmental factors affecting its habitat using two types of UAVs in wetlands where field surveys are difficult to conduct.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Target Species\u003c/h2\u003e \u003cp\u003eThe target species for monitoring was \u003cem\u003eNannophya pygmaea\u003c/em\u003e, a dragonfly species inhabiting wetlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eN. pygmaea\u003c/em\u003e is approximately 20 mm in body length, making it Japan\u0026rsquo;s smallest dragonfly and among the smallest worldwide (Ishida et al. 1988). \u003cem\u003eN. pygmaea\u003c/em\u003e is distributed across East Asia, Southeast Asia, and Australia (Ishida et al. 1988). In Japan, its distribution is localized to Honshu, Shikoku, and Kyushu (Yoshida et al. 2004). The distribution area of \u003cem\u003eN. pygmaea\u003c/em\u003e in Japan has declined markedly due to wetland loss caused by vegetation succession and land development (Yabu and Nakajima 1996). Consequently, many prefectures classify \u003cem\u003eN. pygmaea\u003c/em\u003e as a threatened or near-threatened species. It is a highly rare species specialized to wetlands and is considered an indicator of wetland health. Thus, \u003cem\u003eN. pygmaea\u003c/em\u003e is suitable as an indicator species for wetland monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Site\u003c/h2\u003e \u003cp\u003eThe study site was the Mizorogaike wetland, located in Kita Ward, Kyoto City, Kyoto Prefecture, Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Mizorogaike wetland is a pond with an area of approximately 8 ha and a maximum depth of 2 m. Moreover, it contains an extensive floating mat, which is rare in the lowlands of western Japan, covering about one-third of the water surface (Tsujino et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Although the Mizorogaike wetland borders an urban area, it represents a valuable ecosystem inhabited by glacial-period relict species. The Mizorogaike wetland is designated as a national natural monument in Japan. Together with Osegahara, the Mizorogaike wetland is known for its abundance of \u003cem\u003eN. pygmaea\u003c/em\u003e in Japan (Yoshida et al. 2004).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Dragonfly Survey\u003c/h2\u003e \u003cp\u003eDragonfly surveys were conducted using a DJI Mavic 3T (Da-Jiang Innovations Science and Technology Co., Ltd., Shenzhen, China). A flight plan created in UgCS (SPH Engineering, Latvia, EU) was imported into DJI Pilot 2 and used to acquire photographs. Survey lines were spaced 10 m apart to cover the entire wetland, with 503 waypoints placed at 10 m intervals along the survey lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The UAV flew at an altitude of 6 m above the ground, paused for 1 s at each waypoint, and captured still images (4000 \u0026times; 3000 pixels) using a 7\u0026times; telephoto lens. The area covered by a single still image was approximately 1.13 m \u0026times; 1.5 m.\u003c/p\u003e \u003cp\u003ePreliminary trials conducted at flight altitudes of 6 m and 10 m confirmed that surface vegetation was not disturbed by downdrafts, that the UAV did not collide with low shrubs scattered throughout the wetland, and that \u003cem\u003eN. pygmaea\u003c/em\u003e individuals could be identified. Based on these trials, the flight altitude was set at 6 m. Surveys were conducted eight times at approximately two-week intervals between June 10 and September 24, 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During each survey, instantaneous wind speed and air temperature were measured using a BENETECH GM816 anemometer, and spherical photographs were taken using a RICOH THETA SC to record weather conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, a DJI D-RTK 2 mobile station was deployed to correct UAV positional information using real-time kinematic (RTK) positioning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey date, wind speed, and air temperature. The DJI M3T was used for distribution surveys of the \u003cem\u003eN. pygmaea\u003c/em\u003e, while the DJI Matrice 300 was used for surveys of habitat factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDJI M3T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDJI Matrice 300\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWind speed (instantaneous) (m/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAir temperature (Celsius)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-06-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9:58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-06-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11:04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9:52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-07-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-07-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-08-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-08-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-09-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024-09-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10:49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCaptured images were displayed at 100% magnification using an image viewer, and the presence or absence of \u003cem\u003eN. pygmaea\u003c/em\u003e was visually identified for each image (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Each image required approximately 20\u0026ndash;30 s for inspection. Individuals of \u003cem\u003eN. pygmaea\u003c/em\u003e were recorded and classified as males, immature males, or females. Mature males and immature males exhibit different environmental preferences (Yabu and Nakajima 1996) and can be distinguished based on coloration differences (red versus orange); therefore, they were classified separately. Females were identified by their characteristic striped pattern.\u003c/p\u003e \u003cp\u003eThe DJI Mavic 3T records the geographic coordinates of image locations. Using ArcGIS Pro (Esri Japan Corporation, Tokyo, Japan), point data were generated from the latitude and longitude recorded for each image. By combining these point data with the identification results for \u003cem\u003eN. pygmaea\u003c/em\u003e, distribution datasets were created. Data from all eight surveys in which \u003cem\u003eN. pygmaea\u003c/em\u003e was present were used to calculate population density for each category (male, immature male, and female) using kernel density estimation in ArcGIS Pro. The parameters were set as follows: no population field, output cell size of 0.5 m, and area units in square meters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Environmental Factor Survey\u003c/h2\u003e \u003cp\u003eEnvironmental factor surveys were conducted using a MicaSense Altum multispectral sensor (AgEagle Aerial Systems Inc., Kansas, USA) mounted on a DJI Matrice 300. The MicaSense Altum is equipped with five sensors that record visible-to-near-infrared wavelengths (400\u0026ndash;900 nm) and one sensor that records thermal infrared wavelengths (8000\u0026ndash;14,000 nm). The spatial resolutions of the thermal infrared sensor and the other five sensors are 160 \u0026times; 120 pixels and 2064 \u0026times; 1544 pixels, respectively. The flight plan created in UgCS was imported into DJI Pilot 2, and the same flight plan was used for each survey. Using a ground sampling distance of 3 cm per pixel, flight plans were designed with forward and side overlaps of 80% and 70%. Environmental surveys were conducted eight times on the same days as the dragonfly surveys.\u003c/p\u003e \u003cp\u003eThe Altum sensor was operated together with an optical Downwelling Light Sensor (DLS 2) to record irradiance and sun-angle correction information. Images were processed using photogrammetric methods to produce orthomosaics with a spatial resolution of 3 cm per pixel, corrected using DLS 2 parameters. Five ground control points were established within the survey area, and their positions were corrected using XYZ coordinates obtained from RTK surveys during photogrammetric processing. Photogrammetric processing was performed using Agisoft Metashape Professional version 2.1.2 (Agisoft LLC, St. Petersburg, Russia).\u003c/p\u003e \u003cp\u003eTo evaluate vegetation density, the normalized difference vegetation index (NDVI) was calculated from orthomosaic images using near-infrared (NIR) and red (R) wavelengths as follows:\u003c/p\u003e \u003cp\u003eNDVI = (NIR\u0026thinsp;\u0026minus;\u0026thinsp;R) / (NIR\u0026thinsp;+\u0026thinsp;R)\u003c/p\u003e \u003cp\u003eTo evaluate water area distribution, the normalized difference water index (NDWI) was calculated from orthomosaic images using near-infrared (NIR) and green (G) wavelengths as follows:\u003c/p\u003e \u003cp\u003eNDWI = (G\u0026thinsp;\u0026minus;\u0026thinsp;NIR) / (G\u0026thinsp;+\u0026thinsp;NIR)\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData Analysis\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eCircular buffers with a radius of 0.5 m were generated around each image location point. To ensure that each buffer fit within the area captured by a single still image used for the dragonfly survey, the buffer radius was set to 0.5 m. Mean surface temperature (ST), NDVI, and NDWI values within each buffer polygon were calculated using the zonal statistics function in ArcGIS Pro.\u003c/p\u003e \u003cp\u003eSex was treated as a binary response variable (male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;0), with immature males classified as males. A full logistic regression model was fitted with all UAV-derived environmental variables\u0026mdash;normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (ST)\u0026mdash;included as explanatory variables. All continuous predictors were standardized using z-score transformation prior to analysis to allow direct comparison of effect sizes among variables. Model selection was not performed because all predictors were included based on a priori ecological hypotheses. The relative importance of environmental variables was assessed using the absolute values of standardized regression coefficients (|β|), while the direction of sex-specific associations was interpreted based on the sign of the coefficients. Positive coefficients indicate environmental conditions associated with a higher probability of male occurrence, whereas negative coefficients indicate conditions associated with a higher probability of female occurrence.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eApproximately 558 adult dragonflies were identified across the eight surveys (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The majority of individuals were males (439 individuals, 79%), followed by 82 females (15%) and 37 immature males (7%). July 9 recorded the highest number of \u003cem\u003eNannophya pygmaea\u003c/em\u003e, and more than 100 individuals were identified between June 25 and August 5. Females were not detected after August 22. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the spatial distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e on each survey date. From June 25 to July 23, when female abundance was relatively high, the distributions of males and females overlapped broadly across the survey area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWithin-season variation in identified number of \u003cem\u003eN. pygmaea\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e06\u0026ndash;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06\u0026ndash;25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e07\u0026ndash;09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e07\u0026ndash;23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e08\u0026thinsp;\u0026minus;\u0026thinsp;05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e08\u0026ndash;22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e09\u0026ndash;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e09\u0026ndash;24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmature male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents population density maps calculated from all locations at which \u003cem\u003eN. pygmaea\u003c/em\u003e individuals were observed during the eight surveys. The spatial patterns of population density differed between males and females.\u003c/p\u003e \u003cp\u003ePredicted probability curves derived from the logistic regression model further illustrated how sex-specific occurrence probabilities varied along environmental gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). For each environmental variable, predicted probabilities were calculated while holding the other variables at their mean values. The shapes of the prediction curves indicated monotonic changes in the probability of male occurrence across gradients of NDVI, NDWI, and surface temperature (ST), consistent with the direction and magnitude of the standardized regression coefficients. Environmental variables with larger absolute standardized coefficients produced steeper prediction curves, indicating stronger differentiation between male (including immature male) and female occurrence environments. In contrast, variables with smaller effect sizes showed more gradual changes in predicted probabilities across their observed ranges. These model-based predictions provide an intuitive visualization of sex-specific associations with microhabitat conditions inferred from UAV-derived environmental data.\u003c/p\u003e \u003cp\u003eThe logistic regression model revealed clear associations between sex and UAV-derived environmental variables. Because all predictors were standardized, the absolute values of the regression coefficients were directly comparable and were therefore used to assess relative importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Among the three variables examined, surface temperature (ST) exhibited the largest standardized effect size, indicating that this variable contributed most strongly to sex-specific differences in occurrence environments. The signs of the standardized coefficients further indicated the direction of these associations: positive coefficients corresponded to environmental conditions associated with a higher probability of male occurrence, whereas coefficients with the opposite sign indicated conditions more strongly associated with female occurrence. These results suggest that males (including immature males) and females differ systematically in their use of microhabitats characterized by vegetation structure, moisture availability, and surface temperature.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Monitoring the Distribution of \u003cem\u003eN. Pygmaea\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eWithin-season variation in the emergence of \u003cem\u003eNannophya pygmaea\u003c/em\u003e and changes in its spatial distribution were identified by applying the same flight plan and conducting regular photographic surveys. Prior to this study, the only available information on the distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e in the Mizorogaike wetland was that it occurred on the floating mat in the central part of the pond. In contrast, the present study enabled tracking of spatial distribution changes and identification of differences between males and females. With respect to within-season variation in emergence, previously available information was limited to broad regional descriptions, such as reports that many adults appear from May to June in Honshu, Japan (Yabu and Nakajima 1996). In this study, within-season variation in the emergence of \u003cem\u003eN. pygmaea\u003c/em\u003e was clarified specifically for the Mizorogaike wetland.\u003c/p\u003e \u003cp\u003eMales are easily visible because of their red coloration, which stands out in areas with sparse vegetation. In contrast, females are often found perched on relatively tall plants and are difficult to detect due to their cryptic coloration. Therefore, females are expected to be less detectable than males. The females identified in this study are considered likely to have emerged near male territories for mating. In addition, other dragonfly species occur in the Mizorogaike wetland, including species with red coloration similar to that of \u003cem\u003eN. pygmaea\u003c/em\u003e. However, due to clear differences in body size, misidentification as \u003cem\u003eN. pygmaea\u003c/em\u003e is unlikely. Although the possibility that the same individual appeared in multiple still images cannot be completely excluded, this likelihood is considered low given the long resting periods on plants and the infrequent long-distance movements of \u003cem\u003eN. pygmaea\u003c/em\u003e, in combination with the UAV flight speed and spacing between survey lines.\u003c/p\u003e \u003cp\u003eIn addition to \u003cem\u003eN. pygmaea\u003c/em\u003e, damselflies were the most frequently photographed taxa. Similar to \u003cem\u003eN. pygmaea\u003c/em\u003e, damselflies tend to move slowly and remain stationary on plants, whereas species that move rapidly were rarely photographed. Therefore, the survey method used in this study is considered applicable primarily to dragonfly species that exhibit limited movement.\u003c/p\u003e \u003cp\u003eAt the study site, much of the surface is composed of floating mat, making on-foot surveys difficult. This condition is considered the primary reason why detailed distribution information for \u003cem\u003eN. pygmaea\u003c/em\u003e had not previously been available. The use of UAVs in this study to quantify the distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e in areas that are difficult to survey can therefore be regarded as a significant methodological advance. In addition, wetlands inhabited by \u003cem\u003eN. pygmaea\u003c/em\u003e are particularly vulnerable to disturbance caused by surveys; however, the UAV-based approach employed here allows surveys to be conducted without entering the wetland, thereby minimizing environmental disturbance. Accurate monitoring of rare species populations is essential for understanding long-term population trends and identifying habitat threats (Reckling et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and long-term population monitoring is a central component of conservation efforts (Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Accordingly, the method developed in this study is considered useful for long-term monitoring of \u003cem\u003eN. pygmaea\u003c/em\u003e and is expected to contribute substantially to its conservation.\u003c/p\u003e \u003cp\u003eWhen developing new survey methods, comparison with conventional approaches, such as mark\u0026ndash;recapture techniques and exuviae sampling, is necessary. As noted above, conventional survey methods are difficult to implement in deep mud pools, which complicates direct comparisons. However, comparisons with conventional methods may be feasible by restricting surveys to areas with relatively easy access, and this should be addressed in future studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Monitoring of Environmental Factors\u003c/h2\u003e \u003cp\u003eThe sex-specific associations detected in this study are consistent with previously reported life-history traits and habitat-use patterns of \u003cem\u003eNannophya pygmaea\u003c/em\u003e. Yabu and Nakajima (1996) reported that immature adults move after emergence into grasslands near water with relatively tall and dense vegetation, whereas mature males subsequently return to water-adjacent areas and establish territories by perching on plants in grasslands characterized by low vegetation and small, shallow open water. In this context, NDVI, which reflects vegetation amount and greenness, can be interpreted as an indicator of vegetation height and density in wetland grasslands. Accordingly, the observed relationships between NDVI and sex-specific occurrence probabilities likely capture this ontogenetic and behavioral shift in habitat use. The tendency for males to be associated with warmer microhabitats is also ecologically plausible, as elevated body temperature enhances reproductive activity and performance in dragonflies (Schreiner et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and males occupying open, low-vegetation territories near shallow water may benefit from increased solar radiation. Together, these results suggest that fine-scale variation in vegetation structure and thermal conditions, detectable using UAV-derived indices, mediates sex-specific spatial segregation in \u003cem\u003eN. pygmaea\u003c/em\u003e, linking individual behavior and reproductive ecology to microhabitat heterogeneity within wetland landscapes.\u003c/p\u003e \u003cp\u003eIn monitoring, it is essential to understand changes in the distribution of indicator species across survey areas and to clarify the factors causing these changes (Dronova et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By using two types of UAVs, this study attempted to simultaneously monitor the distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e and associated environmental factors. The results of the environmental factor analysis are consistent with known habitat characteristics of \u003cem\u003eN. pygmaea\u003c/em\u003e, demonstrating that this approach can simultaneously assess species distribution and the environmental conditions influencing habitat use.\u003c/p\u003e \u003cp\u003eIn the Mizorogaike wetland, environmental alterations such as vegetation changes caused by invasion of deer (\u003cem\u003eCervus nippon\u003c/em\u003e) have become a concern (Niwa \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The UAV-based approach developed here may enable monitoring of the impacts of such vegetation changes on the distribution of \u003cem\u003eN. pygmaea\u003c/em\u003e. In addition, the still images acquired in this study captured small and rare wetland plant species, such as \u003cem\u003eDrosera rotundifolia\u003c/em\u003e and \u003cem\u003eUtricularia bifida\u003c/em\u003e, allowing their spatial distribution and within-season variation to be assessed in a manner similar to that used for \u003cem\u003eN. pygmaea\u003c/em\u003e. Because of the extremely high spatial resolution of the imagery, many other plant species can also be identified, suggesting potential applications for vegetation classification and detection of invasive species. These features can be assessed simultaneously while visually identifying \u003cem\u003eN. pygmaea\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Management Implications\u003c/h2\u003e \u003cp\u003eThe UAV-based monitoring framework developed in this study provides a practical tool for wetland managers seeking to assess ecosystem condition while minimizing disturbance to sensitive habitats. In floating-mat wetlands such as Mizorogaike, conventional ground surveys are difficult and may cause physical damage to fragile substrates. The non-invasive approach presented here enables repeated, spatially explicit assessment of indicator species distribution and associated habitat conditions without entering the wetland. Because \u003cem\u003eN. pygmaea\u003c/em\u003e responds to fine-scale variation in vegetation structure and surface temperature, changes in its spatial distribution may serve as an early signal of environmental alteration, including vegetation shifts caused by deer invasion or hydrological modification. The integration of species detection with UAV-derived environmental indices allows managers to link biological responses directly to habitat characteristics, facilitating evidence-based decision making.\u003c/p\u003e \u003cp\u003eMoreover, the repeatable flight design and standardized data processing workflow support long-term monitoring programs and allow temporal comparisons across years. This framework may be extended to other wetland indicator species and plant communities, thereby enhancing the capacity of managers to detect ecosystem change and evaluate restoration outcomes in fragile wetland systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe methodology developed in this study enables the simultaneous assessment of population dynamics and spatial distribution changes of \u003cem\u003eNannophya pygmaea\u003c/em\u003e, together with environmental factors affecting its habitat. It allows repeated surveys to be conducted without entering wetlands. This approach represents an innovative monitoring method that leverages the advantages of UAV platforms and is considered highly suitable for long-term monitoring. By enabling long-term monitoring of \u003cem\u003eN. pygmaea\u003c/em\u003e, an indicator species for wetlands, this methodology is expected to contribute substantially to the ecosystem management of the Mizorogaike wetland as well as other wetland ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \"Ethical responsibilities of Authors\" as found in the Instructions for Authors.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e \u003cp\u003eDisclosure of potential conflicts of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e \u003cp\u003eResearch involving Human Participants and/or Animals\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eInformed consent\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH. N. wrote the main manuscript text and prepared all figures. H. N. designed an investigation plan and conducted a field survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexander, H.M., Reed, A.W., Kettle, W.D., Slade, N.A., Bodbyl Roels, S.A., Collins, C.D., Salisbury, V., 2012. Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers. PLoS One 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0052762\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0052762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDronova, I., Kislik, C., Dinh, Z., Kelly, M., 2021. A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data. Drones 5, 45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/drones5020045\u003c/span\u003e\u003cspan address=\"10.3390/drones5020045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilho, F.H.I., Heldens, W.B., Kong, Z., De Lange, E.S., 2020. Drones: Innovative technology for use in precision pest management. J. Econ. Entomol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jee/toz268\u003c/span\u003e\u003cspan address=\"10.1093/jee/toz268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez, C., Purdie, H., 2016. UAV- based Photogrammetry and Geocomputing for Hazards and Disaster Risk Monitoring \u0026ndash; A Review. Geoenvironmental Disasters 3, 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40677-016-0060-y\u003c/span\u003e\u003cspan address=\"10.1186/s40677-016-0060-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey, M.C., Hare, D.K., Hackman, A., Davenport, G., Haynes, A.B., Helton, A., Lane, J.W., Briggs, M.A., 2019. Evaluation of stream and wetland restoration using UAS-based thermal infrared mapping. Water (Switzerland) 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w11081568\u003c/span\u003e\u003cspan address=\"10.3390/w11081568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshida, S., Kozima, K., 1988. Illustrated guide for identification of the Japanese Odonata., 107\u0026ndash;108. Tokai University Press, Hirathuka\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnoth, C., Klein, B., Prinz, T., Kleinebecker, T., 2013. Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs. Appl. Veg. Sci. 16, 509\u0026ndash;517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/avsc.12024\u003c/span\u003e\u003cspan address=\"10.1111/avsc.12024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanghammer, J., 2019. UAV Monitoring of Stream Restorations. Hydrology. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/hydrology6020029\u003c/span\u003e\u003cspan address=\"10.3390/hydrology6020029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichez, A., Pi\u0026eacute;gay, H., Jonathan, L., Claessens, H., Lejeune, P., 2016. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery. International Journal of Applied Earth Observation and Geoinformation 44, 88\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jag.2015.06.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2015.06.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoses-Gonzales, N., Brewer, M.J., 2021. A Special Collection: Drones to Improve Insect Pest Management. J. Econ. Entomol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jee/toab081\u003c/span\u003e\u003cspan address=\"10.1093/jee/toab081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa, H., 2021. Assessing the activity of deer and their influence on vegetation in a wetland using automatic cameras and low altitude remote sensing (LARS). Eur. J. Wildl. Res. 67, 1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10344-020-01450-6\u003c/span\u003e\u003cspan address=\"10.1007/s10344-020-01450-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa, H., Hirata, T., 2022. A New Method for Surveying the World\u0026rsquo;s Smallest Class of Dragonfly in Wetlands Using Unoccupied Aerial Vehicles. Drones 6, 427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/drones6120427\u003c/span\u003e\u003cspan address=\"10.3390/drones6120427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026aacute;dua, L., Vanko, J., Hruška, J., Ad\u0026atilde;o, T., Sousa, J.J., Peres, E., Morais, R., 2017. UAS, sensors, and data processing in agroforestry: a review towards practical applications. Int. J. Remote Sens. 38, 2349\u0026ndash;2391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431161.2017.1297548\u003c/span\u003e\u003cspan address=\"10.1080/01431161.2017.1297548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReckling, W., Mitasova, H., Wegmann, K., Kauffman, G., Reid, R., 2021. Efficient drone-based rare plant monitoring using a species distribution model and ai-based object detection. Drones 5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/drones5040110\u003c/span\u003e\u003cspan address=\"10.3390/drones5040110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRominger, K., Meyer, S.E., 2019. Application of UAV-Based methodology for census of an endangered plant species in a fragile habitat. Remote Sens. (Basel). 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs11060719\u003c/span\u003e\u003cspan address=\"10.3390/rs11060719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreiner, G.D., Duffy, L.A., Brown, J.M., 2020. Thermal response of two sexually dimorphic Calopteryx (Odonata) over an ambient temperature range. Ecol. Evol. 10, 12341\u0026ndash;12347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1002/ece3.6864\u003c/span\u003e\u003cspan address=\"10.1002/ece3.6864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTmušić, G., Manfreda, S., Aasen, H., James, M.R., Gon\u0026ccedil;alves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J.J., M\u0026eacute;sz\u0026aacute;ros, J., Zhuang, R., Johansen, K., Malbeteau, Y., DeLima, I.P., Davids, C., Herban, S., McCabe, M.F., 2020. Current practices in UAS-based environmental monitoring. Remote Sens. (Basel). 12, 1\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs12061001\u003c/span\u003e\u003cspan address=\"10.3390/rs12061001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsujino R., Matsui, K., Ushimaru, A., Seo, A., Kawase, D., Uchihasi, H., Suzuki, K., Takahashi, J., Yumoto, T., Takemon, Y., 2007. Invasion of the Mizorogaike Wetland by sika deer, and their effects on vegetation, Japanese Journal of Conservation Ecology,12: 20\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYabu, S., nakashima, A., 1996. Ecological studies on the conservation of \u003cem\u003eNannophya pygmaea\u003c/em\u003e Rambur populations and habitats. J. Japanese Inst. Landsc. Archit. 60, 324\u0026ndash;328, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5632/jila.60.324\u003c/span\u003e\u003cspan address=\"10.5632/jila.60.324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshita, S., Minami, Y., Ueda, T., 2004. Water chemistry of several habitats of a tiny dragonfly, \u003cem\u003eNannophya pygmaea\u003c/em\u003e Rambur. Japanese J. Environ. Entomol. Zool. 15, 13\u0026ndash;17, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11257/jjeez.15.13\u003c/span\u003e\u003cspan address=\"10.11257/jjeez.15.13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"monitoring, wetlands, dragonfly, Nannophya pygmaea, ecosystem management","lastPublishedDoi":"10.21203/rs.3.rs-8904919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8904919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffective wetland management requires reliable indicator species and monitoring methods that minimize ecosystem disturbance. This study presents a non-invasive UAV-based approach for monitoring the distribution and habitat associations of Nannophya pygmaea, a wetland indicator dragonfly, in a protected floating-mat wetland in Japan. High-resolution aerial imagery enabled direct detection of individuals across the entire wetland, while multispectral and thermal data were used to characterize vegetation structure and surface temperature. Across eight surveys conducted during a single emergence season, 558 adult individuals were identified, allowing quantification of within-season dynamics and sex-specific spatial distribution. Logistic regression analyses revealed clear associations between occurrence probability and UAV-derived environmental variables, with surface temperature exerting the strongest influence. Vegetation structure and thermal conditions jointly explained fine-scale habitat differentiation between males and females. The approach allows repeated monitoring without entering fragile wetland habitats and provides spatially explicit information relevant to habitat condition assessment. These findings demonstrate that UAV-based monitoring of indicator species can support adaptive wetland management by linking species responses to microhabitat structure and environmental change.\u003c/p\u003e","manuscriptTitle":"Non-invasive UAV-based monitoring of a wetland indicator dragonfly in a floating-mat wetland: Implications for habitat assessment and management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 16:10:53","doi":"10.21203/rs.3.rs-8904919/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T16:52:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T14:31:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T00:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273859946069960793106288749440708616518","date":"2026-03-13T01:40:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177117074062516710830086678287893351240","date":"2026-02-28T09:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T00:16:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T15:52:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T12:00:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands Ecology and Management","date":"2026-02-18T01:03:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands-ecology-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wetl","sideBox":"Learn more about [Wetlands Ecology and Management](https://www.springer.com/journal/11273)","snPcode":"11273","submissionUrl":"https://submission.nature.com/new-submission/11273/3","title":"Wetlands Ecology and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a1483968-0272-4497-8f72-1ebad294eba1","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:07:23+00:00","versionOfRecord":{"articleIdentity":"rs-8904919","link":"https://doi.org/10.1007/s11273-026-10144-w","journal":{"identity":"wetlands-ecology-and-management","isVorOnly":false,"title":"Wetlands Ecology and Management"},"publishedOn":"2026-04-27 15:58:00","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2026-03-05 16:10:53","video":"","vorDoi":"10.1007/s11273-026-10144-w","vorDoiUrl":"https://doi.org/10.1007/s11273-026-10144-w","workflowStages":[]},"version":"v1","identity":"rs-8904919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8904919","identity":"rs-8904919","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}