A model for managing the Giant Garter Snake, Thamnophis gigas

Thamnophis gigas. Photo by Dave Feliz
The Giant Gartersnake (Thamnophis gigas) is a highly aquatic species that uses marshes and sloughs, low-gradient streams, ponds, and small lakes, with cattails, bulrushes, willows, or other emergent or water-edge vegetation. Because of the direct loss of natural habitat, this snake now relies heavily on rice fields in the Sacramento Valley, but it also uses managed marsh areas in various national wildlife refuges and state wildlife areas. Essential habitat components consist of adequate water during the snake's active season (early spring through mid-fall) to provide adequate permanent water to maintain dense populations of food organisms; emergent, herbaceous wetland vegetation, such as cattails and bulrushes, for escape cover and foraging habitat during the active season; upland habitat with grassy banks and openings in waterside vegetation for basking; and  higher elevation upland habitats for cover and refuge from flood waters during the snake's inactive season in the winter. The Giant Garter Snake is absent from large rivers and other waters with populations of large, introduced, predatory fishes, and from wetlands with sand, gravel, or rock substrates. Riparian woodlands do not provide suitable habitat because of excessive shade and inadequate prey resources. As of 1992, there were 13 known populations. Not all of have good viability. The adult population size is unknown but presumably is at least a few thousand. Estimates of population size for three local populations in the mid-1990s were in the low 100's. The species is now apparently extirpated or very rare in most of the former range in the San Joaquin Valley. Surveys in the 1970s and 1980s yielded some previously unknown localities and several cases of extirpation or at least severe population declines. The area of occupancy, number of subpopulations, and population size are probably continuing to decline, but the rate of decline is unknown.
 Because more than 90 percent of their historical wetland habitat has been converted to other uses, the species has been listed as threatened by the State of California (California Department of Fish and Game Commission, 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant Gartersnakes inhabit a highly modified landscape, with most extant populations occurring in the rice-growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat. In ricelands and managed marshes, many operational decisions likely impact the health and viability of Giant Gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant Gartersnake populations. Little is known, however, about the effects of these types of decisions on the viability of the populations.
In a recent report Halstead et al. (2015) recognized that Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations. Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders. Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system. The objective of this project was to develop a conceptual model of site-specific ecology of the Giant Gartersnake in the Sacramento Valley of California. The authors chose to develop the model at a site-specific scale because that is the scale at which most management decisions are made and at which Giant Gartersnake responses can be quantified. They used a Bayesian network model, and also quantified uncertainty associated with different nodes affecting ecology of the Giant Gartersnake, and the strength of influence of different variables on population growth rates of the species. This is a preliminary step in an ongoing process to clarify and quantify the effects of management actions on Giant Gartersnake populations.
They found population growth of the Giant Gartersnake was most influenced by demographic parameters, especially adult survival. Directly managing for increased survival or fecundity, however, generally is not feasible. Habitat quality, was strongly influenced by water availability and emergent vegetation and had a strong influence on both adult and first-year survival. Additional research into the effects of specific habitat attributes on Giant Gartersnake fitness is needed to better quantify the qualitative relations hypothesized in the Bayesian network model. In this regard, habitat quality; predator, parasite, and pathogen effects; and prey availability (particularly as it affects fecundity) all would be productive avenues for future research efforts. Alternatively, research could focus on those nodes for which the least information exists. For example, the scenario analysis indicated that changing Giant Gartersnake population growth from increasing to decreasing resulted in little change in many nodes. Competitor effects, other sources of mortality, and nearly all parents of habitat quality were changed little under increasing and decreasing population growth scenarios. This indicates that these variables either are truly unimportant for determining Giant Gartersnake population growth, or uncertainty in the strength of these relations precludes drawing conclusions about how these variables affect population growth of the Giant Gartersnake. The prudent course of action would be to conduct research into the effects of these variables on Giant Gartersnakes to determine which of these alternatives is correct.

Citation

Halstead, B.J., Wylie, G.D., Casazza, M.L., Hansen, E.C., Scherer, R.D., and Patterson, L.C., 2015, A conceptual model for site-level ecology of the giant gartersnake (Thamnophis gigas) in the Sacramento Valley, California: U.S.Geological Survey Open-File Report 2015-1152, 152 p., http://dx.doi.org/10.3133/ofr20151152.