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Dryad

Iowa herptile detection histories and landcover metrics

Cite this dataset

Harris, Jonathan (2024). Iowa herptile detection histories and landcover metrics [Dataset]. Dryad. https://doi.org/10.5061/dryad.xwdbrv1nm

Abstract

Predictions of species occurrence allow land managers to focus conservation efforts on locations where species are most likely to occur. Such analyses are rare for herpetofauna compared to other taxa, despite increasing evidence that herptile populations are declining because of land cover change and habitat fragmentation. Our objective was to create predictions of occupancy and colonization probabilities for 15 herptiles of greatest conservation need in Iowa. From 2006–2014, we surveyed 295 properties throughout Iowa for herptile presence using timed visual-encounter surveys, coverboards, and aquatic traps. Data were analyzed using robust design occupancy modeling with landscape-level covariates. Occupancy ranged from 0.01 (95% CI = -0.01, 0.03) for prairie ringneck snake (Diadophis punctatus arnyi) to 0.90 (95% CI = 0.898, 0.904) for northern leopard frog (Lithobates pipiens). Occupancy for most species correlated to landscape features at the 1-km scale. General patterns of species’ occupancy included the negative effects of agricultural features and the positive effects of water features on turtles and frogs. Colonization probabilities ranged from 0.007 (95% CI = 0.006, 0.008) for spiny softshell turtle (Apalone spinifera) to 0.82 (95% CI = 0.62, 1.0) for western fox snake (Pantherophis ramspotti). Colonization probabilities for most species were best explained by the effects of water and grassland landscape features. Predictive models had strong support (AUC > 0.70) for six out of 15 species (40%), including all three turtles studied. Our results provide estimates of occupancy and colonization probabilities and spatial predictions of occurrence for herptiles of greatest conservation need across the state of Iowa.

README: Iowa herptile detection histories and landcover metrics

https://doi.org/10.5061/dryad.xwdbrv1nm

We surveyed 295 properties throughout Iowa for herptile presence using timed visual-encounter surveys, cover boards, and aquatic traps from 2006-2014. We aggregated data into presence/absence for 17 species of greatest conservation need for analyses using robust-design occupancy models. We used ArcGIS (ver. 10.1) to measure landscape-level habitat variables derived from a 2009 Iowa Landcover file developed by the Iowa Department of Natural Resources. At 200 m, 500 m, and 1000 m radii, we recorded six land cover types: water, agriculture, wetland, woodland, and grassland. We then used FRAGSTATS (ver. 3.4) to summarize landcover metrics, including the percentage of landscape (PLND), largest patch index (LPI), patch density (PD), and edge density (ED) for all analyses.

Description of the data and file structure

The Excel file consists of 17 tabs, representing each herptile species surveyed and labeled with species' common names. The data setup for each species is identical and is intended to be used for multi-season occupancy modeling in Program Mark or RMark, where rows correspond to properties and columns contain data on detection histories, landcover covariates, and detection covariates.

Columns 1 and 3- Indicate to Program Mark or RMark that data between those columns (in this case, Property Name) are not relevant to analyses.

Column 2- Property names where herptile surveys were conducted.

Column 4- Detection histories for each species across visits and sites, with 0's representing surveys without detections, 1's representing surveys with detections, and periods representing a day without surveys.

Columns 5:80- Data on land cover metrics for each property. The first letters of the column labels identify the landcover type as either water (Wtr), agriculture (Ag), wetland (Wtl), woodland (Wod), or grassland (Grs). The numbers in the middle of column labels show the scale at which the landcover data were collected (200 m, 500 m, and 1 km radii around each survey location). The final letters in the column labels identify the landcover metric used, being the percentage of landscape (PLND), largest patch index (LPI), patch density (PD), and edge density (ED).

Columns 81:652- Air temperature in Fahrenheit at the time of the survey. The number of columns corresponds to the number of days during the entire survey period. A zero in these cells indicates that a survey was not done at that property on that day. 

Columns 653:1224- The percentage of cloud cover at the time of survey. The number of columns corresponds to the number of days during the entire survey period. A zero in these cells indicates that a survey was not done at that property on that day. 

Columns 1225:1796- Wind speed (mph) at the time of the survey. The number of columns corresponds to the number of days during the entire survey period. A zero in these cells indicates that a survey was not done at that property on that day.

Methods

We used three different methods for surveying herptiles throughout Iowa from 2006–2014. Occurrence data collected using all methods were compiled for all years to establish a single occurrence history for each species on each property. Properties were also clipped to the known ranges of each species, resulting in unequal sample sizes for each species.

We conducted standardized visual encounter surveys for herptiles each year from April to October. This survey method involved a timed search on each property at the beginning of the survey year. We divided each year into three survey seasons to minimize seasonal variation in the detection probability of different species. Those seasons were spring (15 April–15 June), summer (16 June–15 August), and fall (16 August–15 October). We surveyed each property for four person-hours twice during each of the three survey seasons for a maximum of six visits (24 person-hours) per year. We surveyed properties only during the year in which they were selected except for those properties that were selected for annual surveys. Surveys were conducted two weeks apart on average to increase independence among visits. During the spring and fall, we conducted surveys during warmer hours of the day, which typically fell between 10:00 hours and 18:00 hours, to maximize detection probabilities. Two species, Cope’s gray treefrog (Hyla chrysoscelis) and eastern gray treefrog (Hyla versicolor) can only be distinguished via auditory detections. Given that we could not confidently differentiate between the two species, we combined their detection histories and modeled both species in aggregate as the gray treefrog complex.

We also placed coverboards at both systematic and random locations on the property to increase detection probabilities for herptiles. Six coverboards were placed systematically in a hexagonal arrangement 200 m apart in the core habitat type on the property and an additional nine coverboards were placed in locations that seemed most likely to capture individuals, for a total of 15 coverboards per property. Coverboards are frequently used by many species of herpetofauna as habitats for thermoregulation and cover, but may be a more efficient method for capturing reptiles than amphibians. Each coverboard was marked with a global positioning system (GPS) unit and checked during each visual encounter survey. All herptiles observed during the survey were identified to species. Surveys were not conducted on cool days (< 10°C) or during periods of rain.

Lastly, we used aquatic traps to target amphibians and turtles. We set a variety of aquatic traps, which typically consisted of three hoop nets, three box traps, three fish traps, and up to six minnow traps, for two trap nights once per each of three survey seasons described above for a total of six trap nights per property. We identified water bodies on each property, which included ponds, wetlands, lakes, rivers, and streams. Aquatic traps were placed within waterbodies at locations deemed most likely for captures. We baited larger traps with fish (e.g., sardines, dead grass carp [Ctenopharyngodon idella]) and checked traps once daily. We replaced bait as needed when traps were checked.

We used ArcGIS (ver. 10.1) to measure landscape-level habitat variables within 200-m, 500-m, and 1000-m radii of each property. Buffers at each scale were placed around each survey mid-point for each property using the buffer tool in the ArcGIS toolbox (Analysis Tools, Proximity, Buffer). We then clipped a 2009 Iowa Landcover file, developed by the Iowa Department of Natural Resources, to all buffers at all properties. The 2009 Iowa Landcover was developed using satellite imagery and includes six landcover types: water, agriculture, development, wetland, woodland, and grassland. These land cover types were selected due to their potential influence on our focal species and their interest in land managers. We opted to use landcover data for Iowa from 2009 because it is centered in our time series of surveys (2006-2014) and because the file is higher resolution (3m) than most other landcover products.

We used FRAGSTATS (ver. 3.4) to summarize landcover metrics at each buffer. FRAGSTATS is a computer program that analyzes spatial patterns based on categorical maps and allows the user to pick from a variety of metrics to assess landscape configuration. We used the percentage of landscape (PLND), largest patch index (LPI), patch density (PD), and edge density (ED) for all analyses. The percentage of the landscape is the area of a land-use classification divided by the total area of the landscape. The largest patch index is the area of the largest patch of a land-use classification divided by the total area of the landscape. Patch density is the count of patches corresponding to a land-use classification divided by the landscape area. Edge density is the amount of linear edge on the landscape for a land-use classification divided by landscape area. We extracted each FRAGSTATS metric for each of the six landcover types within three different spatial scales (200, 500, and 1000 m radii) to be included as prediction covariates in our models.

For a more detailed description of herptile survey methodology, see the Iowa Department of Natural Resources manual on their Multiple Species Inventory and Monitoring (MSIM) program:

For more information on how landcover data were derived and collected see Harms et al. 2017:

Funding

United States Fish and Wildlife Service, Award: T-6-R-1, State Wildlife Grants

United States Fish and Wildlife Service, Award: T-6-R-2, State Wildlife Grants

United States Fish and Wildlife Service, Award: T-6-R-3, State Wildlife Grants

United States Fish and Wildlife Service, Award: T-6-R-4, State Wildlife Grants

United States Fish and Wildlife Service, Award: T-6-R-5, State Wildlife Grants