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The Powell Center provides an environment for cross-disciplinary scientific collaboration. The Center expands U.S. Geological Survey earth system science synthesis research activities by fostering the innovation that results from accumulated knowledge, constructive errors, and the "information spillover" that emerges from collaborative settings. Working Groups at the Powell Center use existing data to produce new knowledge..
VisTrails is an open-source management and scientific workflow system designed to integrate the best of both scientific workflow and scientific visualization systems. Developers can extend the functionality of the VisTrails system by creating custom modules for bundled VisTrails packages. The Invasive Species Science Branch of the U.S. Geological Survey (USGS) Fort Collins Science Center (FORT) and the U.S. Department of the Interior’s North Central Climate Science Center have teamed up to develop and implement such a module—the Software for Assisted Habitat Modeling (SAHM). SAHM expedites habitat modeling and helps maintain a record of the various input data, the steps before and after processing, and the modeling options incorporated in the construction of an ecological response model. There are four main advantages to using the SAHM:VisTrails combined package for species distribution modeling: (1) formalization and tractable recording of the entire modeling process; (2) easier collaboration through a common modeling framework; (3) a user-friendly graphical interface to manage file input, model runs, and output; and (4) extensibility to incorporate future and additional modeling routines and tools. In order to meet increased interest in the SAHM:VisTrails package, the FORT offers a training course twice a year. The course includes a combination of lecture, hands-on work, and discussion. Please join us and other ecological modelers to learn the capabilities of the SAHM:VisTrails package.
In their recently published report, State of the Mountain Lion: A Call to End Trophy Hunting of America’s Lion , the Humane Society of the United States suggested that mountain lion ( Puma concolor ) hunting should be abolished in the United States. The report claims this recommendation is based on scientific arguments that demonstrate the overharvest of mountain lions throughout much of their current range in the United States. We reviewed the science presented by the Humane Society to support their call for the cessation of mountain lion hunting. Rather than provide a rigorous assessment of the peer-reviewed scientific literature and available data on mountain lion ecology, population dynamics and management, the report uses a fundamentally unscientific approach that starts with an a priori assumption that hunting is detrimental to the long-term persistence of mountain lion populations, then attempts to use scientific arguments to support this value-based position. The report frequently ignores or selectively interprets relevant peer-reviewed literature, weakening the scientific credibility of the report. The report relies on imprecise and inadequate demographic measures, questionable data, and simplistic methodologies to derive dubious estimates of potential lion densities; it compares these estimates to various measures produced by State agencies (which themselves vary in reliability as estimates of abundance) to purportedly illustrate the detrimental effects of hunting. The approach used in the report to support the predetermined supposition that mountain lion populations are over-hunted fails to serve as a scientifically defensible foundation for management recommendations range-wide or at the State level.
Repeated sampling and grain-size analysis of surficial sediments along the sandy shorelines of Louisiana is necessary to characterize coastal-zone sediment properties and evaluate sediment transport patterns within the nearshore environments. In 2008, and again in 2015 and 2016, sediment grab samples were collected along the shorelines of the western Chenier plain, the Isles Dernieres (Raccoon, Whiskey, Trinity and East Islands), the Lafourche delta (Timbalier Islands, Caminada Headland, and Grand Isle), the modern delta (Grand Terre Islands from Chaland Headland to Sandy Point), and the Chandeleur Islands (from Curlew Island to Hewes Point). The samples were collected as part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program in collaboration with the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS–SPCMSC) and the University of New Orleans Pontchartrain Institute for Environmental Studies (UNO–PIES). Physical properties of the samples (sediment grain size and sorting) were measured and provided in data reports to CPRA. Additional samples collected by the USGS from around Breton Island in 2014 and 2015 supplemented the 2015–2016 BICM data to complete the coastwide dataset. This report compares the results of the 2008 and 2015–2016 sedimentologic analyses and documents changes in composition (percent sand) and mean sediment grain size between the two time periods. At most sample sites, differences in mean grain size varied by less than ±0.25 Φ. The largest changes occurred at sites located near tidal inlets or along rapidly eroding shorelines.
Lago de Cidra is a reservoir located on the confluence of Rio de Bayamon, Rio Sabana, and Quebrada Prieta, in the municipality of Cidra in east-central Puerto Rico, about 3.0 kilometers northeast of the town of Cidra. The dam is owned and operated by the Puerto Rico Aqueduct and Sewer Authority (PRASA), and was constructed in 1946 as a 6.54-million-cubic-meter supplemental water supply for the San Juan metropolitan area. The reservoir impounds the waters of Rio de Bayamon, Rio Sabana and Quebrada Prieta. The reservoir has a drainage area of 21.4 square kilometers. The dam is a concrete gravity and earthfill structure with a length of approximately 165 meters and a structural height of 24 meters. The spillway portion of the dam is an ungated ogee crest about 40 meters long with a crest elevation of 403.00 meters above mean sea level. Additional information and operational procedures are listed in Soler-Lopez (1999). During August 14-15, 2007, the U.S. Geological Survey (USGS), Caribbean Water Science Center (CWSC), in cooperation with the PRASA, conducted a bathymetric survey of Lago de Cidra to update the reservoir storage capacity and actualize the reservoir sedimentation rate by comparing the 2007 data with the previous 1997 bathymetric survey data. The purpose of this report is to describe and document the USGS sedimentation survey conducted at Lago de Cidra during August 2007, including the methods used to update the reservoir storage capacity, sedimentation rates, and areas of substantial sediment accumulation since 1997.
This report reflects on the past, present, and potential future of community and citizen science (CCS) in the Elwha River watershed, with particular focus on the years before and after a major restoration event: the removal of two dams that had impacted the river system for a century. We ask: how does CCS feature in the Elwha story and how could it feature? We use the term CCS to reference the broad range of ways in which members of the public might participate in authentic science and monitoring processes, including students and both paid and unpaid interns: participants are individuals contributing to scientific projects without prior formal training in the topic. Removal of the Elwha dams was a large-scale, complex project, and communities had an important role to play: the Lower Elwha Klallam Tribe (LEKT) and other local groups were a large part of the original drive to remove the dams. Some funding and policy requirements for monitoring are ending, but there is still much to learn from the changes happening in the Elwha, requiring ongoing research and monitoring. In 2022, the Elwha scientific community came together in a multifaceted effort called the “Elwha ScienceScape” to mark the ten-year anniversary of dam removal and to plan for future monitoring. One of ScienceScape’s priorities is expanding CCS efforts, and because the Elwha dam removal is a powerful international symbol of large-scale watershed restoration, ScienceScape is well-positioned to inform and emphasize the potential role of CCS in dam removal worldwide. This report presents insights about Elwha CCS from an academic literature review and discussions with scientists and many others that have been working in the Elwha. We found that the history of CCS on the Elwha is important but understated, with few scientific papers acknowledging support by volunteers of various kinds. Recent and ongoing CCS projects on the Elwha tend to be focused on biological phenomena, and most are associated with educational opportunities (across many types of institutions) and paid internships. We also noted that most Elwha CCS projects required volunteers with particular pre-existing skill sets (e.g., botanical knowledge) or time to impart specialized training (e.g. boat use), leading many projects towards engagement with a smaller number of volunteers. Partners working in the Elwha are considering a wide range of potential new CCS projects, and these ideas are in varying stages of development. Many new projects would broaden public involvement in terms of the opportunities available and increase the variety of focal topics for research and monitoring. This increased breadth is promising: there are indications that the local community’s interests also range widely, from fish recovery after dam removal to dam removal impacts on humans. Elwha CCS projects have encountered some challenges and barriers, including the administrative burden of coordinating volunteers and managing liability concerns. But Elwha ScienceScape scientists are committed to the value that CCS brings both to the research itself as well as to those who participate in these projects. CCS can be a way to increase equity in science and engage people who would not otherwise participate in research, and in many cases the research simply wouldn’t be possible without their help. Support with project administration, volunteer management, and data management could help in expanding CCS efforts and broadening their inclusivity. More systematic tracking of CCS projects to assess how they contribute to research and to community and participant benefit could be helpful in establishing and maintaining a long-term CCS strategy in the Elwha.
The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrological models and in land surface parameterization schemes connected to atmospheric models. The MOPEX science strategy involves: database creation, a priori parameter estimation methodology development, parameter refinement or calibration, and the demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrological basins in the United States (US) and in other countries. This database is being continuously expanded to include basins from various hydroclimatic regimes throughout the world. MOPEX research has largely been driven by a series of international workshops that have brought interested hydrologists and land surface modellers together to exchange knowledge and experience in developing and applying parameter estimation techniques. With its focus on parameter estimation, MOPEX plays an important role in the international context of other initiatives such as GEWEX, HEPEX, PUB and PILPS. This paper outlines the MOPEX initiative, discusses its role in the scientific community, and briefly states future directions.
The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.
Immediately following the M w 7.2 Darfield, New Zealand, earthquake, over 180 Quake‐Catcher Network (QCN) low‐cost micro‐electro‐mechanical systems accelerometers were deployed in the Canterbury region. Using data recorded by this dense network from 2010 to 2013, we significantly improved the QCN rapid magnitude estimation relationship. The previous scaling relationship ( Lawrence et al. , 2014 ) did not accurately estimate the magnitudes of nearby (<35 km) events. The new scaling relationship estimates earthquake magnitudes within 1 magnitude unit of the GNS Science GeoNet earthquake catalog magnitudes for 99% of the events tested, within 0.5 magnitude units for 90% of the events, and within 0.25 magnitude units for 57% of the events. These magnitudes are reliably estimated within 3 s of the initial trigger recorded on at least seven stations. In this report, we present the methods used to calculate a new scaling relationship and demonstrate the accuracy of the revised magnitude estimates using a program that is able to retrospectively estimate event magnitudes using archived data.
This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1002/essoar.10508554.1 ) in the AGU section paleoclimatology and paleoceanography (P&P), and a discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (Section 3) Local knowledge, global gain: improving interactions within the scientific community and with locals, indigenous communities, stakeholders, and the public; (Section 4) Field, experimental, remote sensing, and real-time data research and application. P&P projects can better include ICON principles by directly incorporating them into research proposals. A promising way to overcome the challenges of interdisciplinarity and integration is to foster networking, which will advance our research discipline through the application of ICON.
Since 1959, the U.S. Geological Survey has conducted a cooperative water resources program (CWP) with the City of Brunswick and Glynn County in the Brunswick, Georgia, area. Since the late 1950s, the salinity of ground water in the Upper Floridan aquifer near downtown Brunswick, Georgia, has been increasing, and its occurrence has been detected across an area of increasing size. Pumping of the Upper Floridan aquifer near downtown Brunswick has lowered water levels in the aquifer and resulted in an upward hydraulic gradient between the highly saline parts of the Lower Floridan aquifer and the normally fresh Upper Floridan aquifer. Saltwater likely enters the Upper Floridan aquifer through localized, vertically oriented conduits of relatively high permeability and moves laterally in response to the distribution of stresses within the aquifer. The Brunswick-Glynn County CWP for fiscal year 2006 includes the operation and maintenance of 12 continuous water-level recorders. In addition, water-level data were collected from 52 wells and water from 70 wells was analyzed for chloride concentration during June 2005. Geophysical logs were obtained from one well to assess whether the cause of elevated chloride concentration could be due to leaky well casing. A summary of the Georgia Department of Natural Resources, Environmental Protection Division (GaEPD) Georgia Coastal Sound Science Initiative (CSSI) activities that directly benefit the CWP-Brunswick-Glynn County is included in this report. The GaEPD CSSI is a program of scientific and feasibility studies to support development of a final strategy to protect the Upper Floridan aquifer from saltwater contamination. These data presented in this report are needed by State and local authorities to manage water resources effectively in the coastal area of Georgia.
Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.
These remotely sensed, geographically referenced elevation measurements of lidar-derived submerged topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Barnegat Bay, New Jersey, acquired pre-Hurricane Sandy on October 18, 22, 23, and 26, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.
These remotely sensed, geographically referenced elevation measurements of lidar-derived seamless (bare-earth and submerged) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for part of Fire Island, New York, acquired pre-Hurricane Sandy on October 27, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed originally in a NASA-USGS collaboration. The exploration and processing of lidar data in an interactive or batch mode is supported using ALPS. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. The Airborne Lidar Processing System (ALPS) is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.
Precipitation and streamwater samples were collected from 16 November 1999 to 17 November 2000 in two watersheds at Acadia National Park, Maine, and analyzed for mercury (Hg) and dissolved inorganic nitrogen (DIN, nitrate plus ammonium). Cadillac Brook watershed burned in a 1947 fire that destroyed vegetation and soil organic matter. We hypothesized that Hg deposition would be higher at Hadlock Brook (the reference watershed, 10.2 ??g/m2/year) than Cadillac (9.4 ??g/m2/year) because of the greater scavenging efficiency of the softwood vegetation in Hadlock. We also hypothesized the Hg and DIN export from Cadillac Brook would be lower than Hadlock Brook because of elemental volatilization during the fire, along with subsequently lower rates of atmospheric deposition in a watershed with abundant bare soil and bedrock, and regenerating vegetation. Consistent with these hypotheses, Hg export was lower from Cadillac Brook watershed (0.4 ??g/m2/year) than from Hadlock Brook watershed (1.3 ??g/m2/year). DIN export from Cadillac Brook (11.5 eq/ ha/year) was lower than Hadlock Brook (92.5 eq/ha/year). These data show that ??50 years following a wildfire there was lower atmospheric deposition due to changes in forest species composition, lower soil pools, and greater ecosystem retention for both Hg and DIN. ?? Springer Science + Business Media B.V. 2006.
We evaluated the use of a simple rake sampling technique for predicting the biomass of submersed aquatic vegetation. Vegetation sampled from impounded areas of the Mississippi River using a rake sampling technique, was compared with vegetation harvested from 0.33-m2 quadrats. The resulting data were used to model the relationship between rake indices and vegetation biomass (total and for individual species). We constructed linear regression models using log-transformed biomass data for sites sampled in 1999 and 2000. Data collected in 2001 were used to validate the resulting models. The coefficient of determination (R 2) for predicting total biomass was 0.82 and ranged from 0.59 (Potamogeton pectinatus) to 0.89 (Ceratophyllum demersum) for individual species. Application of the model to estimate total submersed aquatic vegetation is illustrated using data collected independent of this study. The accuracy and precision of the models tested indicate that the rake method data may be used to predict total vegetation biomass and biomass of selected species; however, the method should be tested in other regions, in other plant communities, and on other species. ?? 2006 Springer Science+Business Media B.V.
Study Region This study region encompasses the Transboundary San Pedro and Santa Cruz aquifers which are shared between the states of Sonora (Mexico) and Arizona (US). Special regional considerations include a semi-arid climate, basin-fill aquifers with predominantly montane recharge areas, economic drivers in the mining, trade, and military sectors, groundwater-dependent cities with expanding cones of depression, interbasin groundwater transfers, ground- and surface-water contamination, and protected aquatic and riparian habitats that act as significant migration corridors for hundreds of species, including some that are threatened and endangered. Study Focus We focus on lessons learned from the hydrologic assessment of the Transboundary San Pedro and Santa Cruz aquifers. We conducted the work, in two phases: (1) laying the groundwork and (2) implementation. The “laying the groundwork” phase consisted of binational meetings with stakeholders and key actors (agencies and individuals), and the development of an understanding of the physical, institutional, historical, and socio-political context. This led to signing of the binational Transboundary Aquifer Assessment Program (TAAP) agreement in 2009 and detailed the process for cooperation and coordination in the assessment of shared aquifers. The implementation phase began with an agreement to proceed with the study of four “focus” aquifers (Santa Cruz, San Pedro, Mesilla (Conejos-Médanos in Mexico), and Hueco Bolson (Bolsón del Hueco in Mexico)) and development of associated technical teams. Though we do include a brief discussion of the lessons learned from the physical science portion of the study, the results have been described and published elsewhere. The bulk of the paper instead focuses on the findings and lessons learned from the integration of social-science perspectives into a largely physical-science based program, since there is a growing recognition of the need for this type of approach especially in the management and assessment of transboundary aquifers. New Hydrological Insights for the Region The Sonora-Arizona effort succeeded because both countries were adequately represented, and because of flexibility of skills and ability of teams comprising both university and government scientists. Teams included social and earth scientists. Including the social sciences was critical to research design and implementation, and to addressing the cultural, institutional, and socio-political contexts of transboundary aquifer assessment. Significant components of the continuing implementation phase include strategic planning, data compilation and analysis, cross-border integration of datasets, geophysical and geochemical surveys, and internal, peer, and stakeholder engagement.
Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle ( Glyptemys insculpta ) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λ mean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.