Natural Resource Scientists Research Articles

A simple, accurate, and explicit form of the Green–Ampt model to estimate infiltration, sorptivity, and hydraulic conductivity

AbstractFinding an explicit solution to the widely used Green–Ampt (G–A) one‐dimensional infiltration model has been subject of efforts for more than half a century. We derived an explicit semiempirical approach that combines accuracy with simplicity, a concept that has been generally neglected in previous studies. The equation is , with F (L), Ks (L/T), S (L/T0.5) and t (T) being cumulative infiltration, saturated hydraulic conductivity, sorptivity, and time, respectively. Relative errors (ɛ) by the application of this equation generally do not exceed ±0.3% in most applied infiltration problems faced by water resources engineering today. We show both numerically and mathematically that │ɛ│> 0.3% could only occur if Kst/F > 0.904, a criterion that could apply to sand and loamy sand soils (i.e., coarse texture) and if they experience infiltration rates for over 6 h and 19 h, respectively. Hence, we also derived a simple linear adjustment in the model as Fadj ≅ 0.9796 F + 0.335 S2/Ks to address these longer infiltration rates, and to assure that ɛ remains within the expected ±0.3% range of uncertainty. A linearized regression technique was also developed to accurately estimate S and Ks when the G–A model is used. We numerically demonstrated that our fitting method could be used even when the G–A approach is less valid (diffusive soils), provided that the actual value of the capillary length (λ) is initially known. An added benefit of our approach is that by setting λ equal to 1/3 and 2/3, it can significantly limit the range of initializing, unknown, a priori values of S and Ks, as these two parameters are estimated through the inverse solution of implicit infiltration models. Due to the model's simplicity and accuracy, our solution should find application among hydrologists, natural resource scientists, and engineers who wish to easily derive accurate estimations from the G–A infiltration approach and/or estimate sorptivity and hydraulic conductivity without encountering divergence problems.

An exploration of early career agricultural and natural resource scientists’ perceptions of social responsibility

An exploration of early career agricultural and natural resource scientists’ perceptions of social responsibility

Global concerns related to water biology and security: The need for language and policies that safeguard living resources versus those that dilute scientific knowledge

Increasingly, scientists and non-scientists, especially employees of government agencies, tend to use weak or equivocal language when making statements related to science policy and governmental regulation. We use recent publications to provide examples of vague language versus examples of strong language when authors write about regulating anthropogenic pressures on natural resources. Lifeless language is common in agency reports, policy documents, and even scientific papers published by academics. Such language limits success in regulating anthropogenic pressures on natural resources. This challenge must be recognized and countered as a driver of the condition of water and associated resources. We also list sources of vague wording, provide global examples of how ambiguous language and political influences have contributed to water resource degradation, discuss the recent history of science censorship, and offer possible solutions for more direct scientific discourse. We found that: (1) equivocal language was especially common in concluding statements and not only by government employees; (2) authors discussed confusing language concerns in an agency publication; and (3) agency employees sometimes used active, strong language. Key drivers of weak language include: (1) holding on to old paradigms and resisting new knowledge; (2) scientific uncertainty; (3) institutional manuscript review policies; (4) employment and funding insecurity; and (5) avoiding the appearance of advocacy. Examples associated with euphemistic language included climate change, flow and physical habitat alteration, dams, agriculture, mining, forestry, and fisheries, as well as resistance towards monitoring, assessing, and reporting ecological conditions. Suggestions for mitigating equivocal language involve employment protections and greater focus on scientific ethics. We conclude that natural resource scientists should resist calls to employ imprecise language. Instead, they should be strong advocates for prescriptive and protective natural resource actions—based on their science—to halt and reverse the systemic degradation of those resources.

A Podcast Ethnography: Exploring Covid-19's Effect On The Scientific Enterprise

COVID-19 has directly impacted the scientific enterprise and higher education. During times of high stress or disaster, adaptability and resiliency directly impact an individual’s mental health. There is a lack of research about the experiences of agricultural and natural resource scientists and students during the pandemic. The purpose of the study was to explore the impact of COVID-19 on agricultural and natural resource scientists, graduate students, and the scientific enterprise at a land grant university. Using a podcast ethnographic approach to analyze a COVID-19 focused series of the [Streaming Science] podcast, this study found the pandemic halted many scientists’ research projects, required instructors to engage with their students online, increased time spent with family, and created an added awareness of student wellbeing and mental health.

The Role of Spatial Information in Peri-Urban Ecosystem Service Valuation and Policy Investment Preferences

The supply of ecosystem services and the benefits that peri-urban areas provide to society are increasingly being modeled and studied using various ecological, environmental, social, and economic approaches. Nevertheless, the different types and levels of demand, preferences, or values for ecosystem services that different human beneficiaries have, often require information and econometric methods to account for human awareness or knowledge of the spatial underpinnings behind these processes, services, and benefits. Specifically, spatial information regarding the location of an ecosystem, its functions, and its services can play an important role in the value and support for policies affecting conservation of peri-urban ecosystems such as payments for ecosystem service (PES) programs. Such PES programs are policy instruments that promote the use of ecosystem services for resources management and conservation objectives. Therefore, to better address this understudied aspect in the landscape ecology and peri-urban ecosystem services modeling literature, we used an online, interactive, spatially explicit survey (n = 2359) in Bogotá, Colombia to evaluate the role of spatial information on investment and policy preferences for such programs. Using an econometric approach to account for respondents’ spatial literacy (i.e., spatial information) of peri-urban ecosystem services, we analyzed how knowledge of space affected an individual’s choices related to ecosystem services and the economic value of environmental and conservation policies. We found that, as spatial literacy increased, respondents were more likely to prefer that government invest in regulating ecosystem services, specifically water resources, and less likely to prefer investing in other ecosystem services. Although spatial literacy did not necessarily affect respondent’s actual willingness to pay (WTP) for these policies in the form of monthly monetary payments, it did influence the types of programs respondents cared about and the magnitude of resources they were willing to invest. Our findings suggested that increasing spatial literacy would change preferences for government spending but not an individuals’ WTP in contexts such as peri-urban areas and PES programs. Results could be used by landscape ecologists, conservation biologists, natural resource scientists, and environmental/ecological economists to better understand and design more efficient education, conservation, and management strategies to increase public engagement in peri-urban contexts.

Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model

The spatiotemporal variation of any landscape patterns is as a result of complex interactions of social, economic, demographic, technological, political, biophysical and cultural factors. Modelling land use and land cover (LULC) changes is essential for natural resource scientists, decision-makers and planners in developing comprehensive medium and long-term plans for tackling environmental or other related sustainable development issues. The current study used an integrated approach that combines remote sensing and GIS to simulate and predict plausible LULC changes for Dedza district in Malawi for the years 2025 and 2035 based on Cellular Automata (CA)-Markov Chain model embedded in IDRISI Software. The model was validated using a simulated and actual LULC of 2015. The overall agreement between the two maps was 0.98 (98%) with a simulation error of 0.03 (3.0%). The more detailed analysis of validation results based on the kappa variations showed a satisfactory level of accuracy with a Kno, Kstandard and Klocation of 0.97, 0.95 and 0.97, respectively. The future projections indicate that water bodies, barren land and built-up areas will increase while agricultural land, wetlands and forest land will substantially decrease by 2025 and 2035 respectively. According to the transition probability matrix, almost 94.8%, 97.6% and 95.7% of water bodies, agricultural land and barren land will more likely remain stable by 2025. In contrast, forest land exhibits the highest probability of change of 64.8% and 85.9% by 2025 and 2035 respectively. Results also indicate that the majority of the forest areas will be converted to barren land with a probability of 60.8% and 79.6% by 2025 and 2035, respectively. These findings serve as an important benchmark for planners, natural resource managers and policy-makers in the studied landscape to consider in pursuit of holistic sustainable development policies/strategies/guidelines for sustainable natural resource management.

Catchment characteristics, water quality, and cyanobacterial blooms in Washington and Oregon Lakes

Rose VJ, Forney WM, Norton RA, Harrison JA. 2018. Catchment characteristics, water quality, and cyanobacterial blooms in Washington and Oregon Lakes. Lake Reserve Manage. 35:51–63A cyanobacterial bloom (CB) model specifically developed as a screening tool for at-risk and affected lakes would benefit natural resource managers and scientists seeking cost-effective monitoring and mitigation measures to prevent blooms. We tested whether water quality, land use/land cover (LULC), and lake morphometry data could explain CB occurrence in a dataset containing 60 lakes and reservoirs in the Pacific Northwest United States using univariate and multiple logistic regressions. Of the explanatory variables evaluated by univariate regression, chlorophyll a concentration was most strongly associated with CB occurrence (pseudo-r2 = 0.39). Other factors significantly related to CB occurrence included modeled dissolved inorganic phosphorus (DIP) yield, total phosphorus concentration (TP), “mixed forest,” “developed – open space,” and “developed – low intensity” (positive correlations), and water clarity, “evergreen forest,” and “barren land” (negative correlations). The best multiple regression model evaluated included the following independent variables: TP, “drainage area,” and “developed – open space” (AIC = 47.67, modified R2 = 0.53). We selected the best performing univariate model to test for lakes at risk of developing CBs to highlight how simple models based on water quality and LULC factors may be valuable tools for conducting preliminary screenings as part of a monitoring program. Consistent with other studies, our findings suggest an important role for phosphorus in controlling CBs in the Pacific Northwest. Our study indicates that, in addition to more traditionally used water quality data, LULC variables and modeled DIP yield are strongly associated with CBs and may be valuable in the development of predictive models.

SUPERORDER LILIANAE IN THE HERBARIUM OF AL-FARABI KAZAKH NATIONAL UNIVERSITY, THE STATE AND PROSPECTS

For the first time, the authors analyzed representation of the superorder Lilianae in the scientific herbarium of the largest university of the Republic of Kazakhstan, Al-Farabi Kazakh National University. Of the five orders, 13 families, 46 genera and 304 species of Lilianae known in Kazakhstan, the herbarium keeps specimens from 11 families, 30 genera and 146 species. Thus, 84.5 % of the families, 65.2 % of the genera and 47.7 % of the species of Lilianae are represented in the herbarium. Most of the specimens were collected in the first half of the XX century. Among the collectors there are famous florists, geobotanists, and natural resource scientists including Members of the Russian Academy of Sciences N.V. Pavlov and B.A. Bykov, and well known botanistsV.P. Goloskokov, L.F. Demidovskaya, L.A. Demchenko V.S. Kornilov, V.P. Mikhailova, N.T. Ageeva and many others. The absence in the herbarium of specimens representing more than ten genera and half of the species of Lilianae draws attention to the need to continue scientific work and to increase collecting effort through targeted field work.

EDNA Sampler: A fully integrated environmental DNA sampling system

Abstract Species monitoring using environmental DNA (eDNA) is a powerful new technique for natural resource scientists and the number of research groups employing eDNA detection is growing rapidly. However, current eDNA sampling technologies consist mainly of do‐it‐yourself solutions, and the lack of purpose‐built sampling equipment is limiting the efficiency and standardization of eDNA studies. Here, we describe the first fully integrated sampling system (Smith‐Root eDNA Sampler) designed by a team of molecular ecologists and engineers for high‐throughput eDNA sample collection. It consists of a backpack portable pump that integrates sensor feedback, a pole extension with remote pump controller, custom‐made filter housings in single‐use packets for each sampling site and onboard sample storage. The system is optimized for sampling speed and replicability, while minimizing risk of contamination. We present an example pilot study designed to identify optimal eDNA Sampler system parameter values (i.e. pump pressure, flow rate, filter pore size, sample volume) in a new sampling environment. We identified a peak in filtration efficiency at a flow rate threshold of 1.0 L/m, and found that 5 μm filters captured significantly more target eDNA than 1 μm filters. Results also suggest that high filtration pressures may reduce eDNA retention, which implies that pressure should be standardized to avoid biasing detection data. Similar to the technological evolution of backpack electrofishers, eDNA sampling technology is in the process of transitioning from a nascent phase to professionally engineered research tools. Such innovations will be essential as eDNA monitoring becomes one of the industry standard methods used for species detection and management.

Exploring spatial patterns of sudden cardiac arrests in the city of Toronto using Poisson kriging and Hot Spot analyses.

IntroductionOur study looked at out-of-hospital sudden cardiac arrest events in the City of Toronto. These are relatively rare events, yet present a serious global clinical and public health problem. We report on the application of spatial methods and tools that, although relatively well known to geographers and natural resource scientists, need to become better known and used more frequently by health care researchers.Materials and methodsOur data came from the population-based Rescu Epistry cardiac arrest database. We limited it to the residents of the City of Toronto who experienced sudden arrest in 2010. The data was aggregated at the Dissemination Area level, and population rates were calculated. Poisson kriging was carried out on one year of data using three different spatial weights. Kriging estimates were then compared in Hot Spot analyses.ResultsSpatial analysis revealed that Poisson kriging can yield reliable rates using limited data of high quality. We observed the highest rates of sudden arrests in the north and central parts of Etobicoke, western parts of North York as well as the central and southwestern parts of Scarborough while the lowest rates were found in north and eastern parts of Scarborough, downtown Toronto, and East York as well as east central parts of North York. Influence of spatial neighbours on the results did not extend past two rings of adjacent units.ConclusionsPoisson kriging has the potential to be applied to a wide range of healthcare research, particularly on rare events. This approach can be successfully combined with other spatial methods. More applied research, is needed to establish a wider acceptance for this method, especially among healthcare researchers and epidemiologists.

Hearts, Minds, and Touchscreens

Of the many challenges awaiting natural resource scientists in the next decade, the ones that give me the longest pause all have to do with how young Americans experience, understand, and appreciate nature. Although climate change literacy has increased markedly in the last decade, younger Americans are simply getting outside less than they ever have before. Heads of families working longer hours, school and intramural sports budget cuts, and the omnipresence of touchscreens—to name but a few issues—have all helped make today’s youth the most sedentary American generation ever. This generation, that physically visits the natural world at historically low rates, will shortly be tasked with studying and managing the very landscapes they so rarely visit for the benefit of themselves and their descendants.

Leveraging Natural Resource Science: Responding, Translating, and Participating

The biggest challenge that natural resource scientists face—especially those of us “still in training”—is not doing science but instead leveraging science in what ought to be a discourse between scientists and resource managers, policy makers, and stakeholders. Particularly in an era of rapid and widespread environmental change, we must not remain cloistered in a closed loop of peer review and research institutions. Instead, we need to (1) respond to management and policy relevant research needs, (2) translate findings (including those pesky uncertainties) into useable frameworks, and (3) actively participate in conversations surrounding decision making in natural resources. These checkboxes of leveraging science all entail knowledge about and engagement with the natural and human facets of the systems of interest. Hit the trails, and take a manager out to lunch, as a mentor always advises.

Why is Data Sharing in Collaborative Natural Resource Efforts so Hard and What can We Do to Improve it?

Increasingly, research and management in natural resource science rely on very large datasets compiled from multiple sources. While it is generally good to have more data, utilizing large, complex datasets has introduced challenges in data sharing, especially for collaborating researchers in disparate locations ("distributed research teams"). We surveyed natural resource scientists about common data-sharing problems. The major issues identified by our survey respondents (n=118) when providing data were lack of clarity in the data request (including format of data requested). When receiving data, survey respondents reported various insufficiencies in documentation describing the data (e.g., no data collection description/no protocol, data aggregated, or summarized without explanation). Since metadata, or "information about the data," is a central obstacle in efficient data handling, we suggest documenting metadata through data dictionaries, protocols, read-me files, explicit null value documentation, and process metadata as essential to any large-scale research program. We advocate for all researchers, but especially those involved in distributed teams to alleviate these problems with the use of several readily available communication strategies including the use of organizational charts to define roles, data flow diagrams to outline procedures and timelines, and data update cycles to guide data-handling expectations. In particular, we argue that distributed research teams magnify data-sharing challenges making data management training even more crucial for natural resource scientists. If natural resource scientists fail to overcome communication and metadata documentation issues, then negative data-sharing experiences will likely continue to undermine the success of many large-scale collaborative projects.

Choosing and Using Climate‐Change Scenarios for Ecological‐Impact Assessments and Conservation Decisions

Increased concern over climate change is demonstrated by the many efforts to assess climate effects and develop adaptation strategies. Scientists, resource managers, and decision makers are increasingly expected to use climate information, but they struggle with its uncertainty. With the current proliferation of climate simulations and downscaling methods, scientifically credible strategies for selecting a subset for analysis and decision making are needed. Drawing on a rich literature in climate science and impact assessment and on experience working with natural resource scientists and decision makers, we devised guidelines for choosing climate-change scenarios for ecological impact assessment that recognize irreducible uncertainty in climate projections and address common misconceptions about this uncertainty. This approach involves identifying primary local climate drivers by climate sensitivity of the biological system of interest; determining appropriate sources of information for future changes in those drivers; considering how well processes controlling local climate are spatially resolved; and selecting scenarios based on considering observed emission trends, relative importance of natural climate variability, and risk tolerance and time horizon of the associated decision. The most appropriate scenarios for a particular analysis will not necessarily be the most appropriate for another due to differences in local climate drivers, biophysical linkages to climate, decision characteristics, and how well a model simulates the climate parameters and processes of interest. Given these complexities, we recommend interaction among climate scientists, natural and physical scientists, and decision makers throughout the process of choosing and using climate-change scenarios for ecological impact assessment. Selección y Uso de Escenarios de Cambio Climático para Estudios de Impacto Ecológico y Decisiones de Conservación.

Making Good Use of Adaptive Management

Making Good Use of Adaptive Management By CPR Member Scholars Holly Doremus, William L. Andreen, Alejandro Camacho, Daniel A. Farber, Robert L. Glicksman, Dale Goble, Bradley C. Karkkainen, Daniel Rohlf, A. Dan Tarlock, and Sandra B. Zellmer, and CPR Executive Director Shana Jones and Policy Analyst Yee Huang

Using Unmanned Helicopters to Assess Vegetation Cover in Sagebrush Steppe Ecosystems

Abstract Evaluating vegetation cover is an important factor in understanding the sustainability of many ecosystems. Remote sensing methods with sufficient accuracy could dramatically alter how biotic resources are monitored on both public and private lands. Idaho National Laboratory (INL), in conjunction with the University of Idaho, evaluated whether unmanned aerial vehicles (UAVs) are sufficiently accurate and more efficient than the point-frame field method for monitoring vegetative cover and bare ground in sagebrush steppe ecosystems. These values are of interest to land managers because typically there are limited natural resource scientists and funding for comprehensive ground evaluations. In this project, unmanned helicopters were used to collect still-frame imagery to determine vegetation cover during June and July 2005. The images were used to estimate percent cover for six vegetative cover classes (shrub, dead shrub, grass, forbs, litter, and bare ground). Field plots used to collect imagery and on-the-ground measurements were located on the INL site west of Idaho Falls, Idaho. Ocular assessments of digital imagery were performed using SamplePoint, and the results were compared with field measurements collected using a point-frame method. The helicopter imagery evaluation showed a high degree of agreement with field cover class values for grass, litter, and bare ground and reasonable agreement for dead shrubs. Shrub cover was often overestimated, and forbs were generally underestimated. The helicopter method took 45% less time than the field method. This study demonstrates that UAV technology provides a viable method for monitoring selective types of cover on rangelands and could save time and resources.

Users’ perspectives on validity of a simulation model for natural resource management

Managers of agro-ecosystems trade off food production and livelihood strategies against environmental services. They need tools to prospect a wide range of external conditions. Integrated simulation models allow stakeholders to discuss the plausible behaviour of agro-ecosystems and to evaluate dynamic trade-offs, as a basis for planning and policy making in agriculture and natural resource management. However, simulation models need to gain stakeholders’ acceptance before they will be utilized. Gaining stakeholders’ acceptance likely requires salience, credibility and legitimacy. We surveyed the perceptions and expectations of 122 potential model users in four countries, prioritizing these model attributes. A possible shift in user perception was assessed during a participatory model evaluation of a resource management model (FALLOW) for post-tsunami development in West Aceh (Indonesia). Potential model users, comprising natural resource managers, policy makers, lecturers and scientists, ranked salience as the most important characteristic for an integrated simulation model, followed by credibility and legitimacy. Model users’ occupation, prior exposure and interest in using a simulation model did not have a statistically significant influence on users’ perceptions of model attributes.

Australian Aboriginal Peoples' Seasonal Knowledge: a Potential Basis for Shared Understanding in Environmental Management

Natural resource scientists and managers increasingly recognize traditional ecological knowledge (TEK) for its potential contribution to contemporary natural resource management (NRM) and, through this, to more resilient social-ecological systems. In practice, however, inadequate cross-cultural means to organize and communicate TEK can limit its effective inclusion in management decisions. Indigenous seasonal knowledge involving temporal knowledge of biota, landscapes, weather, seasonal cycles, and their links with culture and land uses is one type of TEK relevant to this issue. We reviewed the literature on Australian Aboriginal seasonal knowledge to characterize contemporary and potential applications to NRM. This knowledge was often documented through cross-cultural collaboration in the form of ecological calendars. Our analysis revealed a variety of basic and applied environmental information in Aboriginal seasonal descriptions and calendars that can contribute directly to NRM. Documented applications have been limited to date, but include fire management, inclusion as general material in NRM plans, and interpretative information about environments. Emerging applications include water management and climate change monitoring. Importantly, seasonal knowledge can also contribute indirectly to NRM outcomes by providing an organizing framework for the recovery, retention, and crosscultural communication of TEK and linking to its broader cultural and cosmological contexts. We conclude that by facilitating the combination of experiential with experimental knowledge and fostering complementarity of different knowledge systems, Aboriginal seasonal knowledge can increasingly contribute to more resilient social-ecological outcomes in NRM. Nevertheless, the seasonal framework should augment, rather than override, other approaches to cross-cultural NRM such as those with spatial and/or social-ecological emphasis.

Back to the Future? Planning for environmental outcomes and the new Caring for our Country program

The Australian government Caring for our Country (CfoC) program, first announced in 2008, has sought to build on and enhance previous experience with Australian natural resource management policy. This paper critically examines the initial design, planned delivery, and first round of competitive funding outcomes of the new program. This analysis shows that the initial design and delivery of the CfoC model was ad hoc, politicised, and lacking in transparency. The paper concludes that a more systematic and transparent approach for achieving sustainable environmental outcomes is required in order for this new program to achieve its intended objectives. These implications are of interest to Australian and international geographers, natural resource planners, scientists and policy makers concerned with the design, delivery and outcomes of large natural resource and environmental management programs.