Species distribution models (SDMs) are extensively used to project habitat suitability of species in stream ecological studies. Owing to complex sources of uncertainty, such models may yield projections with varying degrees of uncertainty. To better understand projected spatial distributions and the variability between habitat suitability projections, this study uses five SDMs that are based on the outputs of a two-dimensional hydraulic model to project the suitability of habitats and to evaluate the degree of variability originating from both differing model types and the split-sample procedure. The habitat suitability index (HSI) of each species is based on two stream flow variables, including current velocity (V), water depth (D), as well as the heterogeneity of these flow conditions as quantified by the information entropy of V and D. The six SDM approaches used to project fish abundance, as represented by HSI, included two stochastic models: the generalized linear model (GLM) and the generalized additive model (GAM); as well as three machine learning models: the support vector machine (SVM), random forest (RF) and the artificial neural network (ANN), and an ensemble model (where the latter is the average of the preceding five models). The target species Sicyopterus japonicas was found to prefer habitats with high current velocities. The relationship between mesohabitat diversity and fish abundance was indicated by the trends in information entropy and weighted usable area (WUA) over the study area. This study proposes a method for quantifying habitat suitability, and for assessing the uncertainties in HSI and WUA that are introduced by the various SDMs and samples. This study also demonstrated both the merits of the ensemble modeling approach and the necessity of addressing model uncertainty.

In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate data for Taiwan from the district scale to a 1 km × 1 km grid scale. Local cluster analysis (LCA) of OC mortality rates was then performed to identify OC mortality rate hot spots based on the downscaled and the p-field-simulated OC mortality maps. The relationship between OC mortality and land use was studied by overlapping the maps of the downscaled OC mortality, the LCA results, and the land uses. One thousand simulations were performed to quantify local and spatial uncertainties in the LCA to identify OC mortality hot spots. The scatter plots and Spearman’s rank correlation yielded the relationship between OC mortality and concentrations of the seven metals in the 1 km cell grid. The correlation analysis results for the 1 km scale revealed a weak correlation between OC mortality rate and concentrations of the seven studied heavy metals in soil. Accordingly, the heavy metal concentrations in soil are not major determinants of OC mortality rates at the 1 km scale at which soils were sampled. The LCA statistical results for local indicator of spatial association (LISA) revealed that the sites with high probability of high-high (high value surrounded by high values) OC mortality at the 1 km grid scale were clustered in southern, eastern, and mid-western Taiwan. The number of such sites was also significantly higher on agricultural land and in urban regions than on land with other uses. The proposed approach can be used to downscale and evaluate uncertainty in mortality data from a coarse scale to a fine scale at which useful additional information can be obtained for assessing and managing land use and risk.

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.

Tree species in mountainous areas are expected to shift their distribution upward in elevation in response to climate change, calling for a potential redesign of existing protected areas. This study aims to predict whether or not the distributions of two high-mountain tree species, Abies (Abies kawakamii) and Tsuga (Tsuga chinensis var. formosana), will significantly shift upward due to temperature change, and whether current protected areas will be suitable for conserving these species. Future temperature change was projected for 15 different future scenarios produced from five global climate models. Shifts in Abies and Tsuga distributions were then predicted through the use of species distribution models (SDMs) which included occurrence data of Abies and Tsuga, as well as seasonal temperature, and elevation. The 25 km × 25 km downscaled General Circulation Model (GCMs) data for 2020–2039 produced by the Taiwan Climate Change Projection and Information Platform was adopted in this study. Habitat suitability in the study area was calculated using maximum entropy model under different climatic scenarios. A bootstrap method was applied to assess the parameter uncertainty of the maximum entropy model. In comparison to the baseline projection, we found that there are significant differences in suitable habitat distributions for Abies and Tsuga under seven of the 15 scenarios. The results suggest that mountainous ecosystems will be substantially impacted by climate change. We also found that the uncertainty originating from GCMs and the parameters of the SDM contribute most to the overall level of variability in species distributions. Finally, based on the uncertainty analysis and the shift in habitat suitability, we applied systematic conservation planning approaches to identify suitable areas to add to Taiwan’s protected area network.

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.

Remediation prioritization frequently falls short of systematically evaluating the underlying ecological value of different sites. This study presents a novel approach to delineating sites that are both contaminated by any of eight heavy metals and have high habitat value to high-priority species. The conservation priority of each planning site herein was based on the projected distributions of eight protected bird species, simulated using 900 outputs of species distribution models (SDMs) and the subsequent application of a systematic conservation tool. The distributions of heavy metal concentrations were generated using a geostatistical joint-simulation approach. The uncertainties in the heavy metal distributions were quantified in terms of variability among 1000 realization sets. Finally, a novel remediation decision-making approach was presented for delineating contaminated sites in need of remediation based on the spatial uncertainties of multiple realizations and the priorities of conservation areas. The results thus obtained demonstrate that up to 42% of areas of high conservation priority are also contaminated by one or more of the heavy metal contaminants of interest. Moreover, as the proportion of the land for proposed remediated increased, the projected area of the pollution-free habitat also increased. Overall uncertainty, in terms of the false positive contamination rate, also increased. These results indicate that the proposed decision-making approach successfully accounted for the intrinsic trade-offs among a high number of pollution-free habitats, low false positive rates and robustness of expected decision outcomes.