Monitoring and modeling wetland chloride concentrations in relationship to oil and gas development.

Extraction of oil and gas via unconventional methods is becoming an important aspect of energy production worldwide. Studying the effects of this development in countries where these technologies are being widely used may provide other countries, where development may be proposed, with some insight in terms of concerns associated with development. A fairly recent expansion of unconventional oil and gas development in North America provides such an opportunity. Rapid increases in energy development in North America have caught the attention of managers and scientists as a potential stressor for wildlife and their habitats. Of particular concern in the Northern Great Plains of the U.S. is the potential for chloride-rich produced water associated with unconventional oil and gas development to alter the water chemistry of wetlands. We describe a landscape scale modeling approach designed to examine the relationship between potential chloride contamination in wetlands and patterns of oil and gas development. We used a spatial Bayesian hierarchical modeling approach to assess multiple models explaining chloride concentrations in wetlands. These models included effects related to oil and gas wells (e.g. age of wells, number of wells) and surficial geology (e.g. glacial till, outwash). We found that the model containing the number of wells and the surficial geology surrounding a wetland best explained variation in chloride concentrations. Our spatial predictions showed regions of localized high chloride concentrations. Given the spatiotemporal variability of regional wetland water chemistry, we do not regard our results as predictions of contamination, but rather as a way to identify locations that may require more intensive sampling or further investigation. We suggest that an approach like the one outlined here could easily be extended to more of an adaptive monitoring approach to answer questions about chloride contamination risk that are of interest to managers.

Large increases in methane emissions expected from North America's largest wetland complex.

Natural methane (CH4) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH4 flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH4 emissions from the Prairie Pothole Region (PPR), North America's largest wetland complex. Plot-scale CH4 emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH4 emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH4 emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH4 concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird.

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Capturing Spatiotemporal Patterns in Presence-Absence Data to Inform Monitoring and Sampling Designs for the Threatened Dakota Skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States.

Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence-absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.

A Bayesian approach for temporally scaling climate for modeling ecological systems.

With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models. The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that has potential advantages in modeling ecological response variables, including a flexible computation of the index over different timescales. However, little development has been made in terms of the choice of timescale for SPEI. We developed a Bayesian modeling approach for estimating the timescale for SPEI and demonstrated its use in modeling wetland hydrologic dynamics in two different eras (i.e., historical [pre-1970] and contemporary [post-2003]). Our goal was to determine whether differences in climate between the two eras could explain changes in the amount of water in wetlands. Our results showed that wetland water surface areas tended to be larger in wetter conditions, but also changed less in response to climate fluctuations in the contemporary era. We also found that the average timescale parameter was greater in the historical period, compared with the contemporary period. We were not able to determine whether this shift in timescale was due to a change in the timing of wet-dry periods or whether it was due to changes in the way wetlands responded to climate. Our results suggest that perhaps some interaction between climate and hydrologic response may be at work, and further analysis is needed to determine which has a stronger influence. Despite this, we suggest that our modeling approach enabled us to estimate the relevant timescale for SPEI and make inferences from those estimates. Likewise, our approach provides a mechanism for using prior information with future data to assess whether these patterns may continue over time. We suggest that ecologists consider using temporally scalable climate indices in conjunction with Bayesian analysis for assessing the role of climate in ecological systems.