Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms.

Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate. For prediction, we used a set of fifteen causative factors as a cross-function of soil, site and stand-related predictors. A total of 80 sample plots, including 1134 affected trees, were surveyed and used for the modeling process at the 5600-ha landscape level of the southern Zagros, Iran, where the disease occurs in roughly 25% of forest lands. Ten machine learning algorithms were explored and the performance of each algorithm to predict oak disease outbreak was evaluated. The modeling framework used maximum entropy to remove the least influential variables and build the status-quo management scenario to which the results of the prediction models were compared. Results showed that the random forests algorithm (AUC = 0.96: Precision = 0.71: Accuracy = 0.90: F-Measure = 0.70) achieved significantly better results than the status-quo management (Precision = 0.13: Accuracy = 0.67: F-Measure = 0.12) and any other algorithm. Soil chemical properties (NPK, organic carbon and EC) and landform predictors (slope, distance to roads, and TWI) were major forecasters of oak disease outbreak identified by the random forest algorithm. Geostatistical analysis enabled the creation of a map that identified sites at higher risk of infestation, allowing epidemiologists and forest managers to find sites likely to be infested. Consequently, financial resources can be allocated and management practices such as sanitation felling treatments applied across large forest landscapes to minimize the risk of spread and severity to uninfested high-value trees on nearby or adjacent land zones that are in the early stage of epidemics.

Vulnerability of coastal communities to climate change: Thirty-year trend analysis and prospective prediction for the coastal regions of the Persian Gulf and Gulf of Oman.

This study relates changes in social vulnerability of 20 counties on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) over a 30-year period (1988-2017) to changing socio-economic conditions and environmental (climate) hazard. Social vulnerability in 2030, 2040 and 2050 is predicted based on the RCP8.5 climate change scenario that projects drought intensities and rising sea levels. Social vulnerability was based on the three dimensions of sensitivity, exposure, and adaptive capacity using 18 socio-economic and five climate indicators identified by experts. All but one indicator related very strongly to the dimension it sought to represent. Despite improvements in adaptive capacity over time, social vulnerability increased between 1988 and 2017 and rates of change accelerated after change point years that occurred between 1998 and 2002 in most counties. Extrapolating past changes of each indicator over time enabled forecasts of social vulnerability in the future. While social variability decreased between 2017 and 2030, it increased again between 2030 and 2050. The lowest future social vulnerability is expected along the eastern PG coast, the greatest along the western PG and the GO. The worsening of socio-economic indicators contributed to increased sensitivity, and increased drought intensities plus the expected rise in sea levels will lead to social vulnerabilities in 2050 comparable to present levels. Between 1.4 and 1.7 M people will live in areas that are likely submerged by water in the future. About 80% of these people live in six counties with variable social vulnerabilities. While counties with lower social variabilities might be better able to cope with the challenges posed by climate change, adaptation programs to enhance the resilience of the residents in these and the remaining counties along the PG and the GO need to be implemented soon to avoid uncontrolled mass migration of millions of people from the region.

Spatial modeling of exposure of mangrove ecosystems to multiple environmental hazards.

Determining the level of ecosystems exposure to multiple environmental hazards or risk factors is of paramount importance for developing, adopting, and planning management strategies to minimize the harmful effects of these hazards. We quantified the level of exposure of mangroves on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) between 1986 and 2019 to eight environmental hazards, i.e., drought, maximum temperatures, rising sea levels, change of freshwater inflows to coasts, extreme storm surges, significant wave height (SWH), seaward edge retreat in the mangroves, and fishery intensity. Based on expert opinion, fuzzy weights were used to integrate these exposures into a single index (EI) for the region. Experts gave the greatest weight/importance to the risks posed by sea-level rise and seaward retreat of mangroves and the lowest risk to significant wave height and fishery intensity in coastal waters. The overall EI and six of eight individual variables (except fishery intensity and maximum temperatures) pointed to exposure levels of mangroves that increased from the coasts of the PG (EI 0.69) to the GO (EI 6.69). Since these hazards are expected to continue in the future, local/regional management responses should focus on minimizing regional anthropogenic threats and halt conversion of natural areas to agricultural and open areas to maintain freshwater inputs to coastal areas, particularly on the GO. Further, uplands that may serve as future refugia into which mangroves may expand over time as sea levels continue to rise should be protected from development. This was the first study that used an analytic framework to compute a mangrove exposure index to a suite of physical and socio-economic hazards across a region. This framework may provide insights into cost-effective resilience-based design and management of socio-ecologically coupled ecosystems in an era of increasing types and intensities of environmental hazards.

Multi-hazards vulnerability assessment of southern coasts of Iran.

Coastal vulnerability assessment has become one of the most important tools for decision making and providing effective managerial solutions to reduce adverse socio-economic impacts of multiple environmental hazards on coupled social-ecological systems of coastal areas. The aim of this study was to assess the vulnerability of the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) in the Hormozgan province of Iran. Nine variables of vulnerability that included the rate of coastline change, relative sea level rise, coastal slope, mean tidal range, coastal geomorphology, significant wave height (SWH), extreme storm surge, population density, and fishing intensity were weighted, mapped, and combined into the Coastal vulnerability index (CVI). Experts viewed sea level rise, shoreline change and extreme storm surge as most important for imparting vulnerabilities on the northern coasts of PG and GO. Socio-economic variables (i.e., population density and fishery intensity) were considered least important. Of the total length of the provincial shoreline, 27% were classified into the very low vulnerability class, 31% into the low, 17.4% into the moderate, 15.4% into the high, and 9.2% into the very high vulnerability class. About 1295 km (58%) of shorelines were classified into the low and very low vulnerability classes (CVI value ≤ 8.32) and mainly consisted of shorelines on the western coast along the PG. In contrast, 553 km (24.6%) of shorelines were classified into the high and very high vulnerability classes (CVI values > 13.39) and were located along the central coasts (especially in the Qeshm Island and Strait of Hormuz) and on the east coasts of the GO. At least a quarter of all shorelines in the province have high and very high vulnerability to environmental hazards that are the harbingers of climate change.

Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran.

Leaf Area Index (LAI; as an indicator of the health) of the mangrove ecosystems on the northern coasts of the Persian Gulf and the Gulf of Oman was measured in the field and modeled in response to observed (1986-2017) and predicted (2018-2100) drought occurrences (quantified using the Standardized Precipitation Index [SPI]). The relationship of LAI with the normalized difference vegetation index (NDVI) obtained from satellite images was quantified, the LAI between 1986 and 2017 retrospectively estimated, and a relationship between LAI and SPI developed for the same period. Long-term climate data were used as input in the RCP8.5 climate change scenario to reconstruct recent and forecast future drought intensities. Both the NDVI and the SPI were strongly related with the LAI, indicating that realistic LAI values were derived from historic satellite data to portray annual changes of LAI in response to changes in SPI. Our findings show that projected future drought intensities modeled by the RCP8.5 scenario increase more and future LAIs decreased more on the coasts of the Gulf of Oman than the coasts of the Persian Gulf in the coming decades. The year 1998 was the most significant change-point for mean annual rainfall amounts and drought occurrences as well as for LAIs and at no time between 1998 and 2017 or between 2018 and 2100 are SPI and LAI values expected to return to pre-1998 values. LAI and SPI are projected to decline sharply around 2030, reach their lowest levels between 2040 and 2070, and increase and stabilize during the late decades of the 21st century at values similar to the present time. Overall, this study provides a comprehensive picture of the responses of mangroves to fluctuating future drought conditions, facilitating the development of management plans for these vulnerable habitats in the face of future climate change.