AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this - the 'Automated Interactive Monitoring System (AIMS) for Wildlife'. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Landscape fires disproportionally affect high conservation value temperate peatlands, meadows, and deciduous forests, but only under low moisture conditions

Landscape fires are a natural component of the Earth System. However, they are of growing global concern due to climate change exacerbating their multiple impacts on biodiversity, ecosystems, carbon storage, human health, economies, and wider society. Temperate regions are predicted to be at greatest risk of increasing fire activity due to climate change, where fires can seriously impact important ecosystems for biodiversity and carbon storage, such as peatlands and forests. There is insufficient literature on the background prevalence, distribution, and drivers of fires in these regions, especially within Europe, to assess and mitigate their risks. Using a global database of fire patches based on the MODIS FireCCI51 product, we address this knowledge gap by quantifying the current prevalence and size of fires in Polesia, a 150,000 km² area comprising a mosaic of peatland, forest, and agricultural habitats in northern Ukraine and southern Belarus. Between 2001 and 2019, fires burned 31,062 km² of land, and were most frequent in spring and autumn. Although most fires started in agricultural land, fires disproportionately affected natural and semi-natural land cover types, particularly in protected areas. Over one fifth of protected land burned. Coniferous forests were the most common land cover type in protected areas, but fires mostly occurred in meadows, open peatlands (especially fen and transition mires), and native deciduous forests. These land cover types were highly susceptible to fires under low soil moisture conditions, but the risk of fire was low under average or higher soil moisture conditions. Restoring and maintaining natural hydrological regimes could be an effective nature-based solution to increase the resilience of fire-vulnerable ecosystems and support global biodiversity and carbon storage commitments under the United Nations Framework Conventions on Climate Change and Convention on Biological Diversity.

Predictability and transferability of local biodiversity environment relationships

Background

Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures-such as species richness or abundance-are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted.

Methods

Here I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within ("predictability") and across similar ("transferability") biodiversity surveys.

Results

I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question-particular for transferability-our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.

Monitoring spatiotemporal variation of groundwater level and salinity under land use change using integrated field measurements, GIS, geostatistical, and remote-sensing approach: case study of the Feija aquifer, Middle Draa watershed, Moroccan Sahara

The cultivation of watermelons has been a fast growing agriculture industry in the arid, desert regions of Morocco, relying on groundwater pumping and transformation of rangelands to farms due to growing demand for the fruit in national and international markets. This study aims to measure the impact of watermelon expansion on groundwater resources in the Feija Basin, which is one of the largest watermelon cultivation areas in Southern Morocco. Field measurements, statistics, Kriging interpolation, and regression methods were used to measure the temporal variations in the groundwater level (GL) and salinity between 2013 and 2018 to determine the correlation between different parameters. Remote sensing data was also used to monitor the watermelon cultivation expansion. Results show a rapid expansion of agricultural areas from just 185.11 ha in 2007 to 2560.1 ha in 2018. The groundwater level declined rapidly by about 10 m below ground level during the 5 years of the study period. Additionally, the decline was accompanied by a significant increase in electrical conductivity (salinity) values over the same time interval from 1077.55 to 1211.9 µS/cm. As a consequence of the continuous overexploitation and unsustainable management, a lot of wells have run dry and there have been drinking water shortages in the city of Zagora, the closest city nearby. Results can help target efforts to improve the implementation of conservation strategies to ensure the sustainability of water use and food production in this region of Morocco.

Generating indicator species for bird monitoring within the humid forests of northeast Central America

The use of indicator species can simplify bird monitoring by reducing the level of specialized skills needed, which increases the potential pool of participants and reduces training costs and complexity. To facilitate monitoring in the humid forests of northeast Central America, we conducted point count surveys for birds across gradients of disturbance in the Cockscomb Basin Wildlife Sanctuary, Belize, and analyzed the association of bird species with remotely sensed metrics of forest condition and anthropogenic disturbance using indicator species analysis. Twenty species exhibited significant associations with one or more of these metrics. We propose six species as indicators for anthropogenic disturbance based on our criteria of being associated with anthropogenically disturbed sites, or anthropogenically disturbed and riparian sites with no explicit mention in the literature of an obligate association with riparian habitats, or association of remotely sensed metrics that appeared to reflect disturbance: yellow-olive flycatcher, red-legged honeycreeper, dusky antbird, blue ground dove, buff-throated saltator, and brown jay. We propose the keel-billed motmot as an indicator of undisturbed forest based on its association with forested sites in our analyses. Green shrike vireo, collard trogon, rufous-tailed jacamar, and rufous piha were associated with a specific elevational range but not associated with disturbance, so upward shifts in elevation that might indicate response to climate change would not be confounded with habitat disturbance or degradation. This exercise yielded a much-reduced list of monitoring targets, which will greatly reduce the cost and complexity of forest bird monitoring in the region, as well as reducing barriers to participation.

Use of stable nitrogen isotopes to track plant uptake of nitrogen in a nature-based treatment system

In nature-based treatment systems, such as constructed wetlands, plant uptake of nutrients can be a significant removal pathway. Current methods for quantifying plant uptake of nitrogen in constructed wetlands, which often involve harvesting biomass and assuming that all nitrogen stored in plants was derived from wastewater, are inappropriate in pilot- and full-scale systems where other sources of nitrogen are available. To improve our understanding of nitrogen cycling in constructed wetlands, we developed a new method to quantify plant uptake of nitrogen by using stable isotopes and a mixing model to distinguish between nitrogen sources. We applied this new method to a pilot-scale horizontal levee system (i.e., a subsurface constructed wetland) over a two-year monitoring period, during which 14% of nitrogen in plants was wastewater-derived on average and the remaining plant nitrogen was obtained from the soil. Analysis of nitrogen isotopes indicated substantial spatial variability in the wetland: 82% of nitrogen in plants within the first 2 m of the slope came from wastewater while less than 12% of plant nitrogen in the remainder of the wetland originated from wastewater. By combining these source contributions with remote-sensing derived total biomass measurements, we calculated that 150 kg N (95% CI = 50 kg N, 330 kg N) was taken up and retained by plants during the two-year monitoring period, which corresponded to approximately 8% of nitrogen removed in the wetland. Nitrogen uptake followed seasonal trends, increased as plants matured, and varied based on design parameters (e.g., plant types), suggesting that design decisions can impact this removal pathway. This new method can help inform efforts to understand nitrogen cycling and optimize the design of nature-based nutrient control systems.

Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

East Nusa Tenggara Province is one of the most vulnerable regions in Indonesia to drought. Drought prediction is definitely needed as a mitigation action to minimize the risk of drought. However, a sparse dataset has led to difficulties in accurately predicting future droughts in areas without meteorological stations, and hence a dataset with a finer resolution is required. This research investigates the performance of a 3-month Standardized Precipitation Index (SPI) derived from the Tropical Rainfall Measuring Mission (TRMM) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) to predict drought. CART and Random Forest are applied as the classification methods. Using several predictors, the analysis finds that CART has lower predictability than Random Forest. The average accuracy of the prediction using Random Forest reaches 100% with an average Area Under Curve (AUC) of about 0.8. The analysis also shows that predictions using the MERRA-2 dataset lead to higher accuracy and AUC than those using the TRMM. Therefore, using the MERRA-2 dataset predicted by Random Forest can be an optimal way to predict drought in East Nusa Tenggara. The methods confirmed that average soil surface temperature (day and night), Multivariate ENSO Index (MEI), Arctic Oscillation Index (AOI) and Normalized Difference Vegetation Index (NDVI) are strong predictors of drought. The performance of CART and Random Forest is improved with the Synthetic Minority Over-Sampling Technique (SMOTE).

The techniques described:

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translate drought information and predictors of drought into a base classifier that optimizes the AUC;

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allow drought to be predicted for many grid points efficiently and with high accuracy; and

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are computationally efficient and easy to implement.

Attitudes towards acceptance of an innovative home-based and remote sensing rehabilitation protocol among cardiovascular patients in Shantou, China

BACKGROUND

Cardiac rehabilitation (CR) protocols have diversified to include home-based cardiac tele-rehabilitation (HBCTR) as an alternative to hospital-based or center-based CR. To adopt the use of home-based cardiac tele-rehabilitation, it is necessary to assess cardiac patients' attitudes towards acceptance of such e-health technology, especially in China where knowledge of such technology is deficient.

METHODS

Interviews were conducted in the First Affiliated Hospital of Shantou University Medical College, Shantou, China. After percutaneous coronary interventional (PCI) surgery, patients completed the survey.

RESULTS

Among the 150 patients, only 13% had ever heard of HBCTR. After an introduction of our HBCTR program, 60% of patients were willing to participate in the program. From our multivariate analysis of questionnaire data, age (OR: 0.92, 95% CI: 0.86-0.98; P = 0.007), average family monthly income (OR: 0.13, 95% CI: 0.05-0.34; P < 0.001), education level (OR: 0.24, 95% CI: 0.10-0.59; P = 0.002) and physical exercise time (OR: 0.19, 95% CI: 0.06-0.56; P = 0.003) were independent predictors for acceptance of HBCTR. From the reasons for participation, patients selected: enhanced safety and independence (28.3%), ability to self-monitor physical conditions daily (25.4%), and having automatic and emergency alert (23.1%). Reasons for refusal were: too cumbersome operation (34.3%) and unnecessary protocol (19.4%).

CONCLUSIONS

Most patients lacked knowledge about HBCTR but volunteered to participate after they have learned about the program. Several personal and life-style factors influenced their acceptance of the program. These indicate that both improvement of technology and better understanding of the program will enhance active participation.

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers.