Assessments may underestimate the annual global water scarcity, driven by seasonal water availability and consumption distribution heterogeneity

Systematic conservation planning (SCP) deals with a delicate interplay of competing interests and has far-reaching impacts for all stakeholders and systems involved. While SCP has traditionally attempted to conserve ecosystem services that benefit ecological systems, public perceptions of conservation initiatives influence their ultimate feasibility and sustainability. In an attempt to balance ecological integrity, social utility, and urban development, this study develops a framework that applies four popular models to represent these competing factors, including two ecosystem services models—InVEST (Integrated Valuation of Environmental Services and Tradeoffs) for biophysical services (BpS), and SolVES (Social Values for Ecosystem Services) for social values (SV); a land use and land cover (LULC) suitability model; and Zonation for delimiting high priority areas. We also analyze a number of conservation scenarios that consider varying levels of urban development. While BpS are distributed with considerable spatial variability, SV spatially overlap. Approximately 6% of the area was identified as having both high BpS and SV, whereas a further 24.5% of the area was identified as either high BpS low SV or vise-versa. Urban development scenarios affected the conservation area selection drastically. These results indicate tradeoffs and potential synergies between development, SV, and BpS. Our findings suggest that the information provided by the proposed framework can assist in finding solutions to social-ecological planning complexities that serve multiple stakeholders.

Hydrologic ecosystem services are greatly affected by the changing climate. In this study, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to quantify hydrologic ecosystem services. Five general circulation models (GCMs) and two representative concentration pathways (RCPs) were selected to estimate hydrologic ecosystem services. The Local Indicators of Spatial Association (LISA) index was used to identify hydrologic ecosystem hotspots. The hotspots were used to evaluate the impact of climate change on the services. Results indicate that annual water yields vary from −17% to 8%, with significant intra-year fluctuation. Compared to baseline data, the CESM1-CAM5 predicts an increase of 45% in June, but HadGEM2-AO predicts a drop to only 12% in January. Sediment export results show a similar trend to water yield, with sediment export increasing significantly under RCP 8.5, and monthly sediment export increases concentrated from June and October. Nitrogen and phosphorous exports both show less significant changes but obvious intra-year variations. The CESM1-CAM5 predicts strong seasonal and spatial variation of the hydrologic ecosystem services. Our proposed approach successfully identifies annual and monthly hotspot spatial changes of hydrologic ecosystem services under climate change.

Sustainable conservation aims to ensure the sustained conservation of landscape multi-functionality which in turn requires ensuring ecosystem service (ES) and habitat quality (HQ) sustainability with inclusive landscape-scale conservation planning. This study proposes a landscape conservation planning (LCP) framework for landscape-scale ES-HQ conservation and sustainability. Spatially explicit hotspots for five ESs and HQs are identified via InVEST and LISA software. Spatiotemporal changes in ES-HQ hotspots, in terms of stability and resilience, are delineated. The Zonation technique is applied to prioritize areas for conservation based on ES-HQ hotspot stability and resilience maps. High priority conservation areas are identified and are used as reserve area inputs for land use modeling with CLUE-S software to simulate future land use change under climate change scenarios. This study reports that varied rainfall and climate are major driving factors of ES-HQ sustainability disturbance in the study area. Furthermore, our proposed conservation Strategy 2 demonstrates that a larger extent of landscape multi-functionality can be sustained when the existing conservation area includes the total area of identified ES-HQ resilient hotspots. This study effectively identifies the stability and resiliency of ES-HQ hotspot areas affected by disturbances for high priority landscape conservation requirements to ensure ES-HQ sustainability and landscape multi-functionality in the study area.

In order to guarantee high-quality agricultural products and food safety, efforts must be made to manage and maintain healthy agricultural environments under the myriad of risks that they face. Three central system components of sustainable agricultural management schemes are real-time monitoring, decision-making, and remote access. Information and Communications Technology (ICT) systems are a convenient means of providing both these and other functions, such as wireless sensor networking, mobile phone applications, etc., to agricultural management schemes. ICT systems have significantly improved in recent years and have been widely used in many fields, including environmental monitoring and management. Moreover, ICT could benefit agricultural environment management by providing a platform for collaboration between researchers and stakeholders, thereby improving agricultural practices and environments. This article reviews and discusses the way in which ICT can efficiently improve monitoring systems and risk assessments of agricultural environment monitoring, as well as the technological and methodological improvements of ICT systems. Finally, we develop and apply an ICT system, referred to as the agricultural environment protection system—comprised of a cloud, six E-platforms, three mobile devices, automatic monitoring devices, indigenous wireless sensor nodes, and gateways in agricultural networks—to a case study in the Taoyuan irrigation district, which acts as a pilot area in Taiwan. Through the system, we use all available information from the interdisciplinary structured cloud database to classify the focal area into different agricultural environmental risk zones. We also conducted further analysis based on a hierarchical approach in order to classify the agricultural environments in the study area, to allocate additional sampling with resin packages and mobile devices, as well as to assist decision makers and stakeholders. The main contributions that the system provides include a technical innovation platform (suitable for integrating innovations), economic benefits, and societal benefits.

The purpose of this study is to increase the number of species occurrence data by integrating opportunistic data with Global Biodiversity Information Facility (GBIF) benchmark data via a novel optimization technique. The optimization method utilizes Natural Language Processing (NLP) and a simulated annealing (SA) algorithm to maximize the average likelihood of species occurrence in maximum entropy presence-only species distribution models (SDM). We applied the Kruskal–Wallis test to assess the differences between the corresponding environmental variables and habitat suitability indices (HSI) among datasets, including data from GBIF, Facebook (FB), and data from optimally selected FB data. To quantify uncertainty in SDM predictions, and to quantify the efficacy of the proposed optimization procedure, we used a bootstrapping approach to generate 1000 subsets from five different datasets: (1) GBIF; (2) FB; (3) GBIF plus FB; (4) GBIF plus optimally selected FB; and (5) GBIF plus randomly selected FB. We compared the performance of simulated species distributions based on each of the above subsets via the area under the curve (AUC) of the receiver operating characteristic (ROC). We also performed correlation analysis between the average benchmark-based SDM outputs and the average dataset-based SDM outputs. Median AUCs of SDMs based on the dataset that combined benchmark GBIF data and optimally selected FB data were generally higher than the AUCs of other datasets, indicating the effectiveness of the optimization procedure. Our results suggest that the proposed approach increases the quality and quantity of data by effectively extracting opportunistic data from large unstructured datasets with respect to benchmark data.