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The Louisiana Pinesnake (Pituophis Ruthveni): at Risk of Extinction?

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The Louisiana Pinesnake (Pituophis Ruthveni): at Risk of Extinction? ARTICLES 609 ARTICLES Herpetological Review, 2018, 49(4), 609–619. © 2018 by Society for the Study of Amphibians and Reptiles The Louisiana Pinesnake (Pituophis ruthveni): At Risk of Extinction? Pituophis ruthveni (Louisiana Pinesnake) is one of the rarest Trapping efforts to survey P. ruthveni within its historical snakes in the United States (Conant 1956; Young and Vandeventer range in eastern Texas and western Louisiana began in 1992 and 1988; Rudolph et al. 2006). The historical range included portions have continued to the present. Despite increasing trapping effort of eastern Texas and western Louisiana (Sweet and Parker 1990; over time, capture rates in recent years have been low. Addition- Reichling 1995). This species is generally associated with sandy, ally, incidental records, including road-kills and hand captures well-drained soils; open pine forests, especially longleaf pine as well as those obtained from published literature, museum (Pinus palustris) savannahs; moderate to sparse midstory; and specimens, and current research activities by collaborators have a well-developed herbaceous understory dominated by grasses. declined. From 1992 to present, an increasingly large group of Baird’s Pocket Gophers (Geomys breviceps) are also associated formal and informal cooperators has been active in the field with loose, sandy soils, a low density of trees, open canopy, and and alert to the importance of reporting incidental (non-trap) P. an herbaceous understory (Himes et al. 2006; Wagner et al. 2017) ruthveni observations. Since 1992, a total of 49 unique inciden- and are the primary prey of P. ruthveni (Rudolph et al. 2002, tal records of P. ruthveni have been recorded. During the first 12 2006, 2012). Pituophis ruthveni also use gopher burrow systems years (1992–2003), 35 incidental records were obtained. During for shelter, thermoregulation (Ealy et al. 2004), hibernation the second 12 years (2004–2015), 14 incidental records were ob- (Rudolph et al. 2007), and escape from fire (Rudolph et al. 1998a). tained, a decline of 60% compared to the first 12 years. Although Land use changes have reduced the extent of large contiguous effort expended in locating snakes is unknown, we believe the blocks of open pine forests within the historical range of P. amount of effort increased substantially in the last half of this ruthveni (Bridges and Orzell 1989). In most of the remaining period due to many more observers involved in conservation ac- forested habitat, intensive short-rotation pine (Pinus spp.) tivities with P. ruthveni. silviculture and widespread fire suppression have reduced the The effects of declining habitat quality and fragmentation diverse herbaceous vegetation that characterized the original are amplified for rare species, especially those with small ranges pine forests of Louisiana and eastern Texas by increasing shade (Sodhi et al. 2009). The paucity of new captures under increasing (especially early in the rotation), the use of herbicides, and litter trapping effort and the increasing scarcity of incidental records, buildup in the absence of fire (Frost 1993). Reduction of the in conjunction with the previously acknowledged (Hammerson herbaceous vegetation may lead to declines of Geomys breviceps 2007) warning signs, raised questions about the current status of populations, which in turn, may lead to declines of P. ruthveni P. ruthveni. Are the snakes simply difficult to detect and exist in populations (Rudolph and Burgdorf 1997). small, stable populations or are they on the brink of extinction? The conservation status of P. ruthveni is in question; the The data available when population levels are low is International Union for Conservation of Nature (IUCN) Red generally inappropriate to estimate population size. Frequently, List of Threatened Species assessed P. ruthveni as endangered at only presence/absence, sightings, or capture data (with few to the global level, based on a limited area of occupancy, severely no recaptures) are available and include many zeros and/or fragmented habitat, and declining area, extent, and quality of gaps in data collection. Geographic range can be determined by habitat (criteria B1ab(iii); Hammerson 2007). Pituophis ruthveni plotting all presence records and determining occupied habitat was designated a Candidate Species under the U.S. Endangered minimum convex polygons. Extinction risk can be estimated Species Act in 1999 (USFWS 2013), and ultimately listed as a with quantitative analyses, including population viability Threatened Species in 2018 (USFWS 2018). analyses (PVA). These methods range from a simple time series approach to complex demographic models; the choice of which D. CRAIG RUDOLPH* to use depends on the available data (Gerber and González- JOSH B. PIERCE Suárez 2010). NANCY E. KOERTH For trapping data containing many periods with zero Wildlife Habitat and Silviculture Laboratory, Southern Research Station, captures, regression models of index of abundance (capture USDA Forest Service, 506 Hayter Street, Nacogdoches, Texas 75965, USA rate) over time, using zero-inflated distribution models (Welsh *Corresponding author; e-mail: [email protected] et al. 1996) can be used, but these methods do not take into account detectability. By their nature, it is hard to determine Herpetological Review 49(4), 2018 610 ARTICLES the abundance of snakes; ignoring detection probability of 1–9 years (depending on location) when no trapping occurred. may lead to incorrect conclusions (Kéry 2002; Steen 2010). Numerous cooperators have been involved in the trapping efforts Separating variation in year-to-year population density (trend) (see Acknowledgements). The purposes of these trapping efforts from sources of variation due to observation (detectability, trap have varied over the years to include capture of animals for design, placement, etc.) may strengthen a simple PVA (Holmes radio-telemetry studies (Rudolph and Burgdorf 1997; Rudolph 2001, Staples et al. 2004). et al. 1998b; Ealy et al. 2004; Himes et al. 2006), examination of Recently, state-space models have been used to estimate the effects of road mortality on snake populations (Rudolph et separately the contributions to variability from population al. 1999), and presence/absence surveys (Rudolph et al. 2006). fluctuations and from observation error in a time series of Traps were operated for variable numbers of years (Table 1) abundances (Dennis et al. 2006). Early models using maximum in 10 counties in Texas and 7 parishes in Louisiana (Appendix likelihood methods required no zeros in data and no gaps in time 1). Traps consisted of 1.2 × 1.2-m plywood and hardware cloth (Dennis et al. 2006). More recently developed state-space models boxes with a funnel entrance on each side and 15.2-m drift (Humbert et al. 2009; Dennis and Ponciano 2014) accommodate fences extending from each entrance (Burgdorf et al. 2005). All gaps in time, but not zero abundances. Autoregressive state- traps contained a water source and most traps contained hide space models are simple time series models that allow zero boxes for additional cover. The basic trap design underwent abundances and gaps in time. These are 2-part models that minor modification by the various individuals involved in separately estimate variation due to annual population changes the trapping program, and occasionally major modifications. (process error) and fluctuations in counts due to measurement, Minor modifications consisted of changes in mesh size, or observation error (de Valpine and Hastings 2002). alteration of funnel entrance diameter to reduce by-catch, and Because the detailed life history information required for minor structural modifications of the traps themselves. These most PVA models frequently is not available for populations modifications are unlikely to have had a major impact on trap at the brink of extinction, Holmes et al. (2007) developed a success. Cooperators made major modifications to trap design corrupted stochastic exponential with Gaussian errors (CSEG) in a few instances. One modification consisted of a straight line model. The model uses a time series of population counts to arrangement instead of one drift fence radiating from each of estimate mean annual growth rate and the variation due to the four sides of the box trap. Two different configurations were error. Although the model accommodates time periods with no involved with this major modification. One configuration (N = data, zero abundances are not allowed. Considering the zero 4) consisted of two box traps connected to each other by 30.5 m abundances as missing values will bias the predicted population of drift fence with one 15.2-m drift fence continuing from each size in those years, but will still provide acceptable estimates of trap. Only those funnel entrances associated with a drift fence population trend and process variance. These values can be used were open. These arrays consisted of the same amount of total to forecast risk of quasi-extinction over a range of time. Quasi- drift fence (70 m), and the same number of funnel openings (4) extinction risk is the probability that a population will experience as the single trap configuration. A second configuration (N = 2) a decline past a pre-defined threshold. Thresholds can be chosen consisted of three box traps with 53.3 m of drift fence between to reflect critical population sizes below which persistence trap 1 and 2, and 53.3 m of fencing between trap 2 and 3. These is questionable. Ellner and Holmes (2008) developed robust arrays consisted of 106.7 m of total drift fence, but used the same theoretical minimum uncertainty confidence intervals for quasi- number of funnel openings (4) as the single trap configuration. extinction risks calculated from the CSEG model. When graphed, A second major modification consisted of smaller (1.2 × 0.6 m) these confidence intervals can be used to visually display the traps (N = 3) that were partially buried. These traps used funnels probability of quasi-extinction across a range of thresholds and that were slightly tilted downward to potentially facilitate projection times. This allows managers to see the relationship snake movement into the box.
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