DOI: 10.5061/dryad.xsj3tx9rb
title: Data from: Remote sensing and landcover in ring-necked pheasant research: A review of data sources and scales

description: Documenting wildlife–habitat relationships at multiple scales is essential
 for conservation. Remote sensing datasets and their derivatives (e.g.,
 landcover data) enable efficient multi-scale assessment of ring-necked
 pheasant (Phasianus colchicus) habitat, albeit with trade-offs among their
 thematic, spatial, temporal, and/or spectral grains and extents. For
 example, the National Agriculture Imagery Program provides fine spatial
 but coarse spectral grain imagery, both important for identifying pheasant
 habitats. Spatial technologies and datasets relevant to pheasant research
 are advancing, yet the information on the data sources utilized in
 research to date is limited. Remote sensing and landcover datasets surveys
 in pheasant research could help fill information gaps in pheasant–habitat
 relationships. In this systematic review we filtered 1,110 peer-reviewed
 pheasant habitat studies to 65 from the Central U.S.A. Temporal trends
 were tested in the broad use of remote sensing and the selection of remote
 sensing platforms and data types. Of the selected studies, 26 used remote
 sensing or landcover data, which were classified by the thematic, spatial,
 temporal and spectral grains and extents. Remote sensing and landcover
 data products increased over time, particularly satellite-based landcover
 products with relatively coarse thematic resolutions (e.g., crops and
 grassland), moderate spatial grains (e.g., 30-meter), and spatial extents
 (e.g., smaller than the average U.S.A. county). Remote sensing
 photography/imagery with multispectral sensors and coarse spectral
 resolution (e.g., 3 bands with 100 nm width) was also prominent but
 remained constant over time. We found no evidence of research with remote
 sensing or landcover data at multiple temporal grains and extents. Several
 studies lacked scale reporting, potentially limiting our inference. Scale
 transparency is important due to species selecting their habitat at
 multiple scales, making findings scale-dependent. Effective conservation
 requires scale-appropriate strategies. As remote sensing advances,
 opportunities for ring-necked pheasant habitat multi-scale assessment that
 fill remaining pheasant–habitat relationships knowledge gaps and support
 management decisions will increase.
description: <strong>Research selection and classification
 criteria</strong> Two authors of this paper
 independently collected and filtered articles following the same
 protocols. The search was performed in May 2024. The result from each
 author was then compared to check that the process was performed
 correctly. The data collection methods closely align with the ones
 employed by Barg et al (forthcoming 2024; doi: 10.5061/dryad.j3tx95xr4).
 The review followed the PRISMA guidelines for ecology and evolution (O’Dea
 et al., 2021) utilizing all databases and collections in the Web of
 Science. The final search query returned papers with titles or abstracts
 containing keywords related to pheasants and their habitats.
 The search initially returned 1100 papers, which we filtered to
 174 peer-reviewed articles based in the U.S.A. or Canada. These papers
 were further manually filtered to exclude (1) papers that were not about
 the ring-necked pheasant (<em>Phasianus colchicus)</em>, (2)
 non-original primary peer-reviewed research articles, and (3) articles
 that addressed pheasant habitat outside the study area (Great Plains,
 Midwest, and Corn Belt core area). We used the Great Plains regional
 delineation of Lavin et al. (2011), the Midwest delineation of National
 Geographic (2024), and the Corn Belt delineation of Green et al. (2019).
 Studies that only covered a portion of the study area were included. If no
 location information was available, the study was considered outside the
 study area. Manual filtering also removed papers that (4) did not research
 pheasant habitat, resource use, or survival associated with habitat, (5)
 developed models with data from other studies, to avoid replication, (6)
 simulation studies, (7) studies describing the geographic range of
 pheasants, and (8) capture, release or translocation studies, due to their
 lack of information on wild pheasant habitat. Studies that used a mix of
 wild and captive/translocated pheasants were excluded if the majority were
 captive or translocated. If a study had both distinct captive or
 translocated and wild pheasants in a wild setting, only results from the
 wild pheasants were regarded. Finally, filtering disregarded (9) genetic
 and diet studies that did not provide information about pheasant habitat,
 and (10) multispecies studies that did not focus on pheasant habitat but
 instead focused on biodiversity or richness metrics.
 Only papers that used remote sensing, landcover or vegetation
 indices were utilized in the scale classification. Papers using other
 variables such as land surface temperature or soil moisture were not
 considered, to maintain a focus on pheasant habitat and its
 characteristics. To address the time lag between the time the research was
 conducted and the publication year, which can inflate or deflate temporal
 findings, all papers were considered, regardless of when they were
 published. Remote sensing originates over a century ago with early
 technology using aerial photography from planes and even passenger
 pigeons. The inclusion of older studies illustrates the influence of
 fundamental methods in modern approaches to pheasant habitat research,
 especially studies from the mid-20th century onward. Such early studies
 provide historical context relevant to understanding long-term trends and
 methodology advancements (Gibson, 2013a and b). This process yielded a
 final set of 65 articles. We classified the 65 studies
 according to information found in their Introduction, Study Area, and
 Methods sections. Within the Methods sections of the 65 studies, we
 searched for keywords such as aerial, imagery, photography, remote
 sensing, landcover, and land use to determine whether the study used
 remote sensing and/or landcover data. Mixtures of remote sensing and
 landcover data with additional data, such as field observations and
 management records, were classified as utilizing these data types. For the
 studies that did use remote sensing and/or landcover, we noted whether or
 not ground-truthing was employed. Studies that used remote sensing or
 landcover were categorized based on the remote sensing data collection
 platform (aircraft, satellite or unknown) and the data type (photographs,
 landcover, indices, or unknown). The unknown categories for each platform,
 data type and scales were added to deal with missing information, report
 study quality and report their occurrence. From the 65
 studies, we calculated the percentage of articles researching pheasant
 habitat using remotely sensed or landcover data products as a data source.
 If the study area or remote sensing use was not clearly stated, we
 regarded those studies as outside the specific study area and as not using
 remote sensing. For example, if a paper stated that their study area was
 in a region that overlaps our study area but doesn’t specify where
 specifically in the region the study was conducted, it was counted as
 outside our study area. This ensured that reviewed papers were conducted
 in our study area and used remote sensing. We also determined the
 proportion of different remote sensing platforms and data types used in
 these articles. Percentages were calculated by dividing the usage of each
 platform or data type by the total usage of remote sensing products across
 all studies. We also determined the proportional use of different remote
 sensing datasets. This was calculated by dividing the times each remote
 sensing dataset was used by the total number of times all the remote
 sensing dataset were used. To understand the effect of the missing
 information described by the unknown categories, percentages were also
 calculated excluding such categories. All percentages were rounded to
 ensure a collective sum of 100%. The presence of multiple remote sensing
 products with different platforms and data types in the same studies was
 accounted for when calculating percentages.
 <strong>Temporal trends</strong>
 We conducted the Mann-Kendall tau trend test in R (version 4.4.0)
 with the ‘Kendall’ package accounting for ties to examine changes of
 pheasant habitat publication types in time. Ties occur when numerous
 values are identical in a dataset (McLeod, 2022; R core Team, 2024). The
 test was employed to detect whether remote sensing use in the pheasant
 habitat literature increased or decreased over time. This analysis was
 also performed to test long-term temporal patterns in pheasant habitat
 publications that utilize remote sensing, providing insights on the
 evolution of research methodologies used in time. This is a non-parametric
 test that identifies positive or negative trends in a time series by
 comparing each point to the subsequent one. This test is well suited for
 non-normal data, such as the one we had, and can handle outliers well,
 being an optimal choice for assessing temporal trends in pheasant studies.
 The outputs of the test are Kendall’s tau coefficient indicating the
 strength and direction of the relationship and the p-value indicating the
 significance of the relationship (Kendall, 1945; Sen, 1968, McLeod, 2022).
 The tidyr package was used to structure and organize the data while the
 ggplot2 and patchwork packages were used for plot visualizations (Wickham,
 2016; Pedersen, 2024; Wickham et al., 2024).
 <strong>Thematic, spatial, and temporal
 scale</strong> Papers that used remote sensing or
 landcover data (N =26) were classified based on the thematic, temporal,
 spatial, and spectral grain, and temporal and spatial extent with
 methodologies developed by the authors. Spectral grain (ability of sensors
 to distinguish between wavelengths) excludes landcover and indices usage
 in remote sensing. We calculated the percentages of papers using different
 thematic and spectral resolutions and spatial and temporal grains and
 extents. The thematic categories are from level 1 (most general) to 5
 (most specific in categorization). The National Land Cover Dataset (NLCD)
 classification system was the base for levels 3 and 4 of the
 classification, which is a modified version of the Anderson Land Cover
 Classification System (Anderson et al., 1976; U.S. Geological Survey,
 2019). The finer levels of classification are integrated from other
 standard thematic schemes. The Conservation Reserve Program (CRP) land
 classification was integrated into level 4 from the Hierarchical All Bird
 System (HABS) habitat classification that identifies bird habitats at
 multiple scales (McLachlan et al., 2007). Only shrubs, CRP practices,
 cultivated crops, herbaceous wetlands, and open water were described by
 pheasant studies in more detail for level 5 categorization. These levels
 were categorized according to the USDA CRP practice library, the Cropland
 Data Layer (CDL) classification, and the Stewart & Kantrud
 Classification System. The latter is a widely used method for wetland and
 water systems classification (Stewart & Kantrud, 1971; Farm
 Service Agency, 2024; National Agricultural Statistics Service, 2024). No
 standardized classification was used for shrubs in level 5 due to only one
 paper characterizing shrubs at the species level. Level 2 group classes
 include categories that are closely related (share similar landcover
 characteristics) in level 3 in the NLCD classification, except for wetland
 and water, as they have in common the presence of an aquatic environment.
 Level 1 provides a general classification of all the classes previously
 described. We determined the percentage of studies that
 used data in each hierarchical level of the classification system. The
 percentage was calculated by dividing the number of times a category was
 researched by the total number of reviewed studies (26). These were the
 categories selected by the paper in their method section before they were
 analyzed, as sometimes the classification changed to better-fit models
 during analysis. In cases where a landuse or landcover class was
 classified differently than in our system, we used our system. For
 instance, if a paper classified oats as herbaceous (level 2), we
 classified them as cultivated crops (level 5). When the paper thematic
 classification did not fit any of our system or was missing, it was
 classified as ‘other’ in our system. This ensured the standardization of
 the classification across papers. For example, urban environments were
 classified as other (level 2). The spatial grain was
 the pixel dimensions or stated photograph scale (e.g. 1:3000). If the
 grain was not stated on the paper, it was classified as unknown to report
 the occurrence of the missing information. Spatial extent was determined
 by both explicit reporting (study area) and by estimating it if not
 clearly stated. This estimation was conducted by assessing study maps and
 scale bars, considering indicators such as town size, study area, or study
 block areas, and by summing land cover buffer areas around survey points.
 If many count routes were present in a state or county (e.g., Breeding
 Bird Survey routes), the whole state/county was the study area. The
 percentages of various spatial grains and extents were calculated by
 dividing the frequency of each grain or extent by the total frequency of
 remote sensing or land cover products used in the studies. Remote sensing
 and land cover products were utilized a total of 37 times across the 26
 studies. This helped account for the fact that each remote sensing product
 may have had different grains (depending on the spectral band used and
 year of remote sensing dataset publication) and extent in each
 study. The temporal grain refers to the frequency of
 remote sensing photography/imagery or land cover mapping use in a study
 area. The temporal extent denotes the duration of the utilization of the
 remote-sensing product. Such information was linked to the sensor's
 temporal resolution and the frequency of imagery and land cover updates,
 tailored to its use in the specific study. For example, until 2024 the
 NLCD data had a temporal resolution of 3 to 5 years (now it is yearly) and
 a temporal extent going from 1992 to 2023 (now it is 1985/86 to present;
 U.S. Geological Survey, 2024a and b). If the study used such data in 2001,
 2006, and 2011, the temporal grain would have been 5 years and the extent
 10 years. If the temporal grain or extent was unclear it was classified as
 unknown to report the occurrence of missing temporal information. The
 percentages of different temporal grains and extents were calculated
 similarly to the spatial grain and extent. The spectral
 grain determined the habitat type and quality extractable from remote
 sensing data. Different spectral bands can extrapolate diverse vegetation
 information which can be relevant to pheasants. For example, natural color
 photographs (visible spectrum) can only identify broad habitats such as
 grasslands. Multispectral images using the red and infrared bands can
 identify vegetation characteristics such as greenness which may be better
 suited for pheasant habitat identification. The spectral information
 informs the kind of habitat that can be extrapolated from remote sensing
 (Gibson et al., 2013a and b; Amirkhiz et al., 2023). The spectral grain
 was calculated for photographs and images, not landcover or indices (e.g.,
 NDVI), and referred to the number of spectral bands and their width
 difference to create the photograph or image. If the spectral grain was
 not clearly stated in the study, it was estimated from the information
 given, as indicators of it were often present. For instance, if the study
 reported that natural color aerial photographs were used, the study most
 likely used the blue, green and red bands that respectively range from 400
 to 500 nm, 500 nm to 600 nm and 600 to 700 nm. In that case, three bands
 and spectral resolution width of 100 nm (difference between wavelength
 ranges) was reported as the most likely scenario. If the spectral grain
 was not possible to estimate, it was classified as unknown to report the
 occurrence of such missing information. The percentage of band number and
 bandwidth difference used was calculated similarly to the spatial
 grain.
 <strong>References</strong>
 Amirkhiz, R. G., John, R., & Swanson, D. L. (2023). A
 Bayesian approach for multiscale modeling of the influence of seasonal and
 annual habitat variation on relative abundance of ring-necked pheasant
 roosters. <em>Ecological
 Informatics</em>, <em>75</em>(1), 102003. https://doi.org/10.1016/j.ecoinf.2023.102003. Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey, Professional Paper, 964. Campbell, J. B., & Wynne, R. H. (2011). Image resolution. In J. B. Campbell & R. H. Wynne (Eds.), <em>Introduction to remote sensing</em> (pp. 285–298). New York, NY: Guilford Press. Farm Service Agency. (2024). CRP practices library. U.S. Department of Agriculture. https://www.fsa.usda.gov/programs-and-services/conservation-programs/crp-practices-library/index. Gibson, P. J. & Power. C. H., (2013a). Principles of Remote Sensing. Introductory remote sensing principles and concepts. In P. J. Gibson & C. H., Powers (Eds.), <em>Introductory Remote Sensing Principles and Concepts </em>(pp. 11–66). New York, NY: Routledge. Gibson, P. J. & Power. C. H., (2013b). Remote sensing Systems. In P. J. Gibson & C. H., Powers (Eds.), <em>Introductory Remote Sensing Principles and Concepts </em>(pp. 66–127). New York, NY: Routledge. Green, T. R., Kipka, H., David, O., & McMaster, G. S. (2018). Where is the USA Corn Belt, and how is it changing?. <em>Science of the Total Environment</em>, 618, 1613-1618. https://doi.org/10.1016/j.scitotenv.2017.09.325. Kendall, M. G. (1945). The treatment of ties in ranking problems. Biometrika, 33(3), 239-251. https://doi.org/10.2307/2332303. Lavin, S. J., Shelley, F. M., & Archer, J. C. (2011). <em>Atlas of the Great Plains</em> (pp 18-21). China: University of Nebraska Press. McLeod A (2022). _Kendall: Kendall Rank Correlation and Mann-Kendall Trend Test_. R package version 2.2.1. https://CRAN.R-project.org/package=Kendall. McLachlan, M., Carter, M., Rustay, C., & Venture, P. L. J. (2007). Effects of the Conservation Reserve Program on priority mixed-grass prairie birds. <em>USDA Natural Resources Conservation Service, WA</em>. National Agricultural Statistics Service. (2024). CropScape - Cropland Data Layer. Retrieved June 20, 2024, from https://nassgeodata.gmu.edu/CropScape/. National Geographic. (2024). United States regions, Education. Retrieved May 03, 2024, from https://education.nationalgeographic.org/resource/united-states-regions/. O’Dea, R. E., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W. A., Parker, T. H., Gurevitch, J., Page, M. J., Stewart, G., Moher, D., & Nakagawa, S. (2021). Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: A PRISMA extension. <em>Biological Reviews</em>, <em>96</em>(5), 1695–1722. https://doi.org/10.1111/brv.12721  Pedersen T (2024). <em>patchwork: The Composer of Plots</em>. R package version 1.3.0.9000, https://github.com/thomasp85/patchwork, https://patchwork.data-imaginist.com. R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. https://doi.org/10.2307/2285891. Stewart, R. E., & Kantrud, H. A. (1971). <em>Classification of natural ponds and lakes in the glaciated prairie region</em> (Vol. 92, pp 27-57). Washington: US Bureau of Sport Fisheries and Wildlife. Turner, M. G., Gardner, R. H., O'neill, R. V., & O'Neill, R. V. (2001). Introduction to Landscape Ecology and Scale. In: Turner, M. G., and Gardner, R. H., <em>Landscape ecology in theory and practice</em> (pp. 1-32). New York: Springer. U.S. Geological Survey. (2019). NLCD classes. U.S. Geological Survey. Retrieved November 7, 2024, from https://www.mrlc.gov/. Wickham (2016), H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. Wickham H, Vaughan D, Girlich M (2024). <em>tidyr: Tidy Messy Data</em>. R package version 1.3.1, https://github.com/tidyverse/tidyr, https://tidyr.tidyverse.org.  
description: # Pheasant research temporal trends analysis
 [https://doi.org/10.5061/dryad.xsj3tx9rb](https://doi.org/10.5061/dryad.xsj3tx9rb) ## Description of the data and file structure This dataset includes the data and code needed to reproduce the data summary and analysis from the associated publication. The data was collected to review the ring-necked pheasant (*Phasianus colchicus)* scale research from a remote sensing perspective. The ultimate aim is to understand this declining species' habitat needs at multiple scales. The data was collected from the Web of Science using keywords relating to pheasants and habitats, and then the data was manually filtered to select relevant studies, mainly on the Great Plains. The Excel file lists the studies that were considered and selected or disregarded depending on our chosen criteria. Sixty-five studies were found researching pheasant habitat in the study area, 26 of which used remote sensing. The study methodology, remote sensing data types and platforms, and the thematic, spatial, temporal, and spectral scales (by grain and extent) were summarised from the selected studies in R and Excel to understand literature trends. The data provides important insights into pheasant research scale knowledge and gaps using a technology that is becoming more and more important in wildlife ecology.  ### Files and variables #### File: Remote_sensing_and_landcover_in_ring-necked_pheasant_research_-__Data_Submission.xlsx **Description:** The file is stored in Excel in xlsx format.  ##### Variables * The "Disregarded studies" tab reports the studies selected by the keyword criteria when researching articles in the Web of Science but did not fit our manual selection criteria. Relevant information to identify the articles is in columns A to I. Column J describes why the study was disregarded, and column K describes whether the article was disregarded by just reading the title and the abstract or scanning the main text. Y means yes, and N means no. * The "Selected studies" tab reports the 65 studies that fit our selection criteria. Relevant information to identify the articles is in columns A to I. Whether the research was conducted in the Great Plains, Midwest, and the Cornbelt is described from column K to M. Column N describes if the study used remote sensing to research pheasant habitat. Y means yes, and N means no. In columns O to R, the count, total, and percentages of studies in a selected study area are calculated. * The "Remote sensing studies" tab reports the 26 studies that used remote sensing to study pheasant habitat. Relevant information to identify the articles is in columns A to I. Columns J and K list what remote sensing sources were used in the studies and the number of sources used in each study. Remote sensing platform types and data types for each study are reported in columns L and M. (x and number) indicate how many times that platform or data type was used. Column N describes if ground truth was used with remote sensing data in the study. * The "Remote sensing sources" tab lists all the remote sensing sources used in the 26 studies and the number and percentage of times the sources were used in total.  * The "Thematic grain" tab uses columns A and B to identify the studies. The other columns indicate whether each thematic grain was utilized in the study. If a thematic grain was used, it is marked with a "1"; if it was not used, the space is left blank. The final row reports the percentage of times each thematic grain was used. * The "Spatial scale" tab has columns A and B to identify the studies. The spatial grain for each study is documented in columns C to F. Some studies utilized multiple remote sensing products, resulting in the reporting of various grains across these columns. If a grain column is left blank, it indicates that there is no additional grain information to report for that particular study. The spatial extent is recorded in columns G to J, following a similar format as the spatial grain. Columns L to S provide a summary of both the grain and extent of the data. * The "Temporal scale" tab is structured similarly to the Spatial scale tab but for the temporal scale.  * The "Spectral grain" tab is structured similarly to the spatial and temporal scale, reporting bands and bandwidth instead of grain and extent.  ## Code/software The code was created and runs in R version 4.4.0 (2024-04-24 ucrt). R is a free and open programming language used for statistical analysis and data visualization. The following packages were used to run the code: Kendall, tidyr, patchwork, and ggplot2. The Kendall package was used for the temporal statistical analysis, the tidyr to structure the data, and patchwork and ggplot2 for data visualization.  #### Pheasant-literature-temporal-trends code: Each section of the code is separated by #-------- The first section lists the packages and libraries used to run the code. The second section creates the data frame for the remote sensing methodologies (describes how many studies used or did not use remote sensing per year), analyzes the temporal trend depending on methodology, and computes the figures for visualization.  The third section does the same as the previous section but only for studies that used remote sensing, categorizing them by platform. The fourth section does the same as the third section but for remote sensing data type. There is no need to load data or external files to run the code. #### Pheasant-literature-percentage code: The code is structured similarly to the previous code. Each section of the code is separated by #-------- The first section lists the packages and libraries used to run the code. The second section calculates the percentage of remote sensing platforms. The third section calculates percentages of remote sensing data types.