A Probability Co-Kriging Model to Account for Reporting Bias and Recognize Areas at High Risk for Zebra Mussels and Eurasian Watermilfoil Invasions in Minnesota

Modeling the risk of aquatic species invasion spread through boater movements and river connections

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision-support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal.

Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics

AIM

Availability of uniformly collected presence, absence, and abundance data remains a key challenge in species distribution modeling (SDM). For invasive species, abundance and impacts are highly variable across landscapes, and quality occurrence and abundance data are critical for predicting locations at high risk for invasion and impacts, respectively. We leverage a large aquatic vegetation dataset comprising point-level survey data that includes information on the invasive plant Myriophyllum spicatum (Eurasian watermilfoil) to: (a) develop SDMs to predict invasion and impact from environmental variables based on presence-absence, presence-only, and abundance data, and (b) compare evaluation metrics based on functional and discrimination accuracy for presence-absence and presence-only SDMs.

LOCATION

Minnesota, USA.

METHODS

Eurasian watermilfoil presence-absence and abundance information were gathered from 468 surveyed lakes, and 801 unsurveyed lakes were leveraged as pseudoabsences for presence-only models. A Random Forest algorithm was used to model the distribution and abundance of Eurasian watermilfoil as a function of lake-specific predictors, both with and without a spatial autocovariate. Occurrence-based SDMs were evaluated using conventional discrimination accuracy metrics and functional accuracy metrics assessing correlation between predicted suitability and observed abundance.

RESULTS

Water temperature degree days and maximum lake depth were two leading predictors influencing both invasion risk and abundance, but they were relatively less important for predicting abundance than other water quality measures. Road density was a strong predictor of Eurasian watermilfoil invasion risk but not abundance. Model evaluations highlighted significant differences: Presence-absence models had high functional accuracy despite low discrimination accuracy, whereas presence-only models showed the opposite pattern.

MAIN CONCLUSION

Complementing presence-absence data with abundance information offers a richer understanding of invasive Eurasian watermilfoil's ecological niche and enables evaluation of the model's functional accuracy. Conventional discrimination accuracy measures were misleading when models were developed using pseudoabsences. We thus caution against the overuse of presence-only models and suggest directing more effort toward systematic monitoring programs that yield high-quality data.

Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

Lessons Learned From the Stakeholder Engagement in Research: Application of Spatial Analytical Tools in One Health Problems

Stakeholder engagement in research is widely advocated as a tool to integrate diverse knowledge and perspectives in the management of health threats while addressing potential conflicts of interest. Although guidelines for stakeholder engagement exist in public health and environmental sciences, the feasibility of actionable decisions based on scientific analyses and the lessons learned from the stakeholder engagement in the process co-creation of knowledge have been rarely discussed in One Health literature and veterinary sciences. Risk maps and risk regionalization using spatiotemporal epidemiological/analytical tools are known to improve risk perception and communication. Risk maps are useful when informing policy and management decisions on quarantine, vaccination, and surveillance intended to prevent or control threats to human, animal, or environmental health interface (i.e., One Health). We hypothesized that researcher-stakeholder engagement throughout the research process could enhance the utility of risk maps; while identifying opportunities to improve data collection, analysis, interpretation, and, ultimately, implementation of scientific/evidence-based management and policy measures. Three case studies were conducted to test this process of co-creation of scientific knowledge, using spatiotemporal epidemiological approaches, all related to One Health problems affecting Minnesota. Our interpretation of the opportunities, challenges, and lessons learned from the process are summarized from both researcher and stakeholder perspectives. By sharing our experience we intend to provide an understanding of the expectations, realizations, and “good practices” we learned through this slow-moving iterative process of co-creation of knowledge. We hope this contribution benefits the planning of future transdisciplinary research related to risk map-based management of One Health problems.

Retrospective and Predictive Investigation of Fish Kill Events

Fish kill investigations are critical to understanding threats to aquatic ecosystems and can serve as a measure of environmental disruption as well as an early indicator of emerging disease. The goal of this study was to analyze historical data related to such events among wild fish populations in Minnesota in order to assess the quality and completeness of the data and potential trends in fish kills. After excluding events with incomplete data (e.g., in which the location was not reported), we analyzed 225 unique fish kills from 2003 to 2013 that were recorded in two Minnesota Department of Natural Resources databases. The most reported fish kills occurred during 2007 (n = 41) and during the month of June (n = 81) across all years. Centrarchid species were present in the most fish kills (138), followed by cyprinid and ictalurid species, which were present in 53 and 40 events, respectively. Environmental factors were the most common cause of death reported. Models of environmental factors revealed that the maximum nighttime land surface temperature was the most critical factor in fish mortality, followed by changes in primary productivity and human disturbances. During the course of this study, data gaps were identified, including underreporting, inconsistent investigation, and the lack of definitive diagnoses, making interpretation of our results challenging. Even so, understanding these historical trends and data gaps can be useful in generating hypotheses and advancing data collection systems for investigating future fish kills. Our study is a primer investigation of fish kills providing information on the plausible areas, seasons, and fish groups at risk that can guide active environmental monitoring and epidemiological surveillance of fishes.

Regional trade of medicinal plants has facilitated the retention of traditional knowledge: case study in Gilgit-Baltistan Pakistan

BACKGROUND

The ethnic groups in Gilgit-Baltistan have been utilizing local resources in their centuries-old traditional healing system. Most tribes within these ethnic groups still rely on traditional healing systems. We aim to understand the current status, uses, and abundance of medicinal plants, associated traditional knowledge, and trade.

MATERIALS AND METHODS

The study incorporated over 300 local community members (70% men and 30% women) in focused group discussions, semi-structured interviews, and homework assignments for 8th to 12th grade students to document traditional knowledge (TK) in six districts in Northeast Pakistan. We calculated various indices such as informant consensus factor, use value, relative frequency of citation, and CoKriging. These indices, along with repetitively used medicinal plants, were used to analyze differences in studied locations.

RESULTS

Most of the community members still rely on traditional medication in the study areas. However, we found the highest number of medicinal plants used in Skardu and Gilgit compared to other districts and these two districts also represent trade centers and a highly populated area regarding medicinal plants. Results indicate connection amongst the surveyed villages signifying mixing of knowledge from different sources, with certain areas more influenced by traditional Chinese medicine and others more by Ayurveda and Unani.

CONCLUSION

TK is mostly retained with elder community members; however, those directly linked with market value chain retain rich knowledge on traditional use of the medicinal plants from the region. Major trade centers in the region also coincide with a high density of medicinal plant occurrence, knowledge, and higher utilization. Therefore, with the increasing trade in medicinal plant in the region, there is potential for rejuvenation of this knowledge and of plant use in the region.