The gap between experts, farmers and non-farmers on perceived environmental vulnerability and the influence of values and beliefs.

Science has played a mixed role in guiding conservation and sustainability-oriented decision-making by individuals, policymakers, institutions, and governments. Not all science-based conservation and sustainability initiatives that address issues facing humanity and ecosystems and global problems have gained public support. Conservation decisions and policy prescriptions are and may be based on perceptions about and experiences with the environment, local land use, and ecosystems that may not align with or be grounded in science or evidence from experts in the field. Values, beliefs, and perceptions associated with nature play a critical role in how individuals view biodiversity conservation, sustainability, and natural resource management. This study first examines the gap between experts (scientists and other field experts) and the public (farmers and non-farmers) about the state of water and land resources, wildlife and associated habitats, and aquatic biodiversity in the Smoky Hill River Watershed in western Kansas. Second, the study examines the role that values and beliefs play in shaping environmental perceptions for farmers and non-farmers. Analysis confirms that a gap between experts and farmers/non-farmers does exist, especially with respect to the state of the Ogallala Aquifer, playas, rivers and streams, lakes and reservoirs, native grasslands, wildlife habitats, farmland, native fish populations, and wildlife species. Ordered-logistic regression analyses, meanwhile, indicate that farmer and non-farmer perceptions about the state of the local environment are influenced by traditional and self-interested values, as well as environmental values and beliefs, but less so by religiosity and political ideology. Despite broad takeaways, results exhibited heterogeneity across the farmer and non-farmer subpopulations. If environmental professionals cannot align ecological data, stakeholders' values/perceptions, and policies, then the existing body of technical research and management on sustainability in natural and social sciences may be of little value.

Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data.

Estimating vulnerability is critical to understand human-induced influenceimpacts on the environmental system. The purpose of the current study was to integrate machine learning algorithm and Twitter data to estimate environmental vulnerability in the Brazilian Cerrado for the years 2011 and 2016. We first selected six exposure indicators and five sensitivity indicators to build an environmental vulnerability model and applied an Autoencoder algorithm to find the representation of exposure and sensitivity, respectively. Then the Displaced Ideal method was used to estimate environmental vulnerability. Finally, related historical Twitter data was mined from these two years to validate the results. The findings showed that the percent of land classified as areas of low, medium and high environmental vulnerability were 6.72%, 34.85%, and 58.44% in 2011 and 3.45%, 33.68% and 62.87% in 2016, respectively and most high environmental vulnerability areas were in the Southern Cerrado. Moreover, the Twitter data results showed that more than 85% of tweets occurred in the areas considered as high environmental vulnerability class. The work revealed that the Autoencoder algorithm can be used for environmental assessment, and the social media data has potential to effectively analyze the relationship between human activity and the environment. Although the study provided a novel perspective to estimate environmental vulnerability at a regional scale, it was necessary to develop a more comprehensive indicator system that can improve model performance in the future.

Model approaches to estimate spatial distribution of bee species richness and soybean production in the Brazilian Cerrado during 2000 to 2015.

Agricultural expansion as a main human activity has affected pollinator's habitat, causing spatial distribution changes. Meanwhile, pollinators still provide pollination service to improve crop production. However, their spatial response is unclear because of environmental changes. This study sought to estimate spatial distribution of crop production and pollinator's richness, which can provide insights as to how they interact with the environment. We acquired environmental variables from remote sensing images and used a stacked species distribution model to predict selected bee species richness and a crop simulation model to simulate and calculate soybean production at a regional scale in the Cerrado for the period 2000-2015. Then, we analyzed their potential relationship. The results showed that higher selected bee species richness and higher soybean production occurred in the southern Cerrado. From 2000/08 to 2008/15 period, the selected bee species richness significantly decreased in the western part of the state of Bahia, the state of Goiás, and the northern region of the state of Minas Gerais; while soybean production increased in the states of Mato Grosso, Goiás, Bahia, and Tocantins. Correlation results of selected bee species richness and soybean production showed that they do not follow a linear relationship during the study period. Our findings indicate that the modeling method we proposed is robust to estimate spatial distribution of bee species richness and soybean production in the Cerrado at the regional scale and that the environment has a stronger influence on selected bee species richness than on soybean production. Moreover, climate effects and agricultural expansion are the main factors that affect their spatial distribution and interaction. Finally, our methodology provides a novel spatial perspective to analyze the relationship between pollinator and agricultural expansion corresponding with the environment, but future work is needed to collect a more comprehensive data set to improve model results.

Testing Luminescence Dating Methods for Small Samples from Very Young Fluvial Deposits.

The impetus behind this study is to understand the sedimentological dynamics of very young fluvial systems in the Amazon River catchment and relate these to land use change and modern analogue studies of tidal rhythmites in the geologic record. Initial quartz optically stimulated luminescence (OSL) dating feasibility studies have concentrated on spit and bar deposits in the Rio Tapajós. Many of these features have an appearance of freshly deposited pristine sand, and these observations and information from anecdotal evidence and LandSat imagery suggest an apparent decadal stability. The characteristics of OSL from small (~5 cm) sub-samples from ~65 cm by ~2 cm diameter vertical cores are quite remarkable. Signals from medium-sized aliquots (5 mm diameter) exhibit very high specific luminescence sensitivity, have excellent dose recovery and recycling, essentially independent of preheat, and show minimal heat transfer even at the highest preheats. These characteristics enable measurement of very small signals with reasonable precision and, using modified single-aliquot regenerative-dose (SAR) approaches, equivalent doses as low as ~4 mGy can be obtained. Significant recuperation is observed for samples from two of the study sites and, in these instances, either the acceptance threshold was increased or growth curves were forced through the origin; recuperation is considered most likely to be a measurement artefact given the very small size of natural signals. Dose rates calculated from combined inductively coupled plasma mass spectrometry/inductively coupled plasma optical emission spectrometry (ICP-MS/ICP-OES) and high-resolution gamma spectrometry range from ~0.3 to 0.5 mGya-1, and OSL ages for features so far investigated range from 13 to 34 years to several 100 years. Sampled sands are rich in quartz and yields of 212-250 µm or 250-310 µm grains indicate high-resolution sampling at 1-2 cm intervals is possible. Despite the use of medium-sized aliquots to ensure the recovery of very dim natural OSL signals, these results demonstrate the potential of OSL for studying very young active fluvial processes in these settings.

Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky Hill River Watershed, Kansas.

Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.

Economic linkages to changing landscapes.

Many economic processes are intertwined with landscape change. A large number of individual economic decisions shape the landscape, and in turn the changes in the landscape shape economic decisions. This article describes key research questions about the economics of landscape change and reviews the state of research knowledge. The rich and varied economic-landscape interactions are an active area of research by economists, geographers, and others. Because the interactions are numerous and complex, disentangling the causal relationships in any given landscape system is a formidable research challenge. Limited data with mismatched temporal and spatial scales present further obstacles. Nevertheless, the growing body of economic research on these topics is advancing and shares fundamental challenges, as well as data and methods, with work in other disciplines.