Ecosystem Monitoring: Collective Species and Environmental Information

Coastal marine ecosystems worldwide are increasingly affected by tide alterations and anthropogenic disturbances affecting the water quality and leading to frequent algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impacts on ecological processes and services, such as carbon cycling and sequestration. The exploration of eco-environmental feedback and algal bloom patterns remains challenging and poorly investigated, mostly due to the paucity of data and lack of model-free approaches to infer universal bloom dynamics. Florida Bay, taken as an epitome for biodiversity and blooms, has long experienced algal blooms in its central and western regions, and, in 2006, an unprecedented bloom occurred in the eastern habitats rich in corals and vulnerable habitats. With global aims, we analyze the occurrence of blooms in Florida Bay from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoint the source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to assess the environmental impacts of ecological imbalances and target the prevention and control of algal blooms; and (3) the topological co-evolution of biogeochemical and spreading networks to quantify ecosystem stability and the likelihood of ecological shifts toward endemic blooms in the long term. Here, we propose the transfer entropy (TE) difference to infer salient dynamical inter actions between the spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spread as well as environmental effects. A Pareto principle, defining the top 20% of areal interactions, is found to identify bloom spreading and the salient eco-environmental interactions of CHLa associated with endemic and epidemic regimes. We quantify the spatial dynamics of algal blooms and, thus, obtain areas in critical need for ecological monitoring and potential bloom control. The results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factors, in particular, about how blooms affect temperature locally. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrient overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently, the occurrence of blooms increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable habitats, such as tidal flats, salt-marshes and mangroves. The probabilistic distribution of CHLa is found to be indicative of endemic and epidemic regimes, where the former sets the system to higher energy dissipation, larger instability and lower predictability. Algal blooms are important ecosystem regulators of nutrient cycles; however, chlorophyll-a outbreaks cause vast ecosystem impacts, such as aquatic species mortality and carbon flux alteration due to their effects on water turbidity, nutrient cycling (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising the local water quality, other socio-ecological services are also compromised at large scales, including carbon sequestration, which affects climate regulation from local to global environments. Yet, ecological assessment models, such as the one presented, inferring bloom regions and their stability to pinpoint risks, are in need of application in aquatic ecosystems, such as subtropical and tropical bays, to assess optimal preventive controls. Full article

Mapping the spatial and temporal dynamics of species distributions is necessary for biodiversity conservation land-use planning decisions. Recent advances in remote sensing and machine learning have allowed for high-resolution species distribution modeling that can inform landscape-level decision-making. Here we compare the performance of three popular Sentinel-2 (10-m) land cover maps, including dynamic world (DW), European land cover (ELC10), and world cover (WC), in predicting wild bee species richness over southern Norway. The proportion of grassland habitat within 250 m (derived from the land cover maps), along with temperature and distance to sandy soils, were used as predictors in both Bayesian regularized neural network and random forest models. Models using grassland habitat from DW performed best (RMSE = 2.8 ± 0.03; average ± standard deviation across models), followed by ELC10 (RMSE = 2.85 ± 0.03) and WC (RMSE = 2.87 ± 0.02). All satellite-derived maps outperformed a manually mapped Norwegian land cover dataset called AR5 (RMSE = 3.02 ± 0.02). When validating the model predictions of bee species richness against citizen science data on solitary bee occurrences using generalized linear models, we found that ELC10 performed best (AIC = 2278 ± 4), followed by WC (AIC = 2367 ± 3), and DW (AIC = 2376 ± 3). While the differences in RMSE we observed between models were small, they may be significant when such models are used to prioritize grassland patches within a landscape for conservation subsidies or management policies. Partial dependencies in our models showed that increasing the proportion of grassland habitat is positively associated with wild bee species richness, thereby justifying bee conservation schemes that aim to enhance semi-natural grassland habitat. Our results confirm the utility of satellite-derived land cover maps in supporting high-resolution species distribution modeling and suggest there is scope to monitor changes in species distributions over time given the dense time series provided by products such as DW. Full article

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is of major value to regional ecological management and conservation. Using several data sets, including the SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density and others, combined with trend analysis, stability analysis, and geographic detectors, we studied the pattern of temporal and spatial variation in the NDVI and its stability across Chongqing from 2000 to 2019, and quantitatively analyzed the relative contribution of 18 drivers (natural or human variables) that could influence vegetation dynamics. Over the 20-year period, we found that Chongqing region’s NDVI had an annual average value of 0.78, and is greater than 0.7 for 93.52% of its total area. Overall, the NDVI increased at a rate of 0.05/10 year, with 81.67% of the areas undergoing significant expansion, primarily in the metropolitan areas of Chongqing’s Three Gorges Reservoir Area (TGR) and Wuling Mountain Area (WMA). The main factors influencing vegetation change were human activities, climate, and topography, for which the most influential variables respectively were night light brightness (NLB, 51.9%), annual average air temperature (TEM, 47%), and elevation (ELE, 44.4%). Furthermore, we found that interactions between differing types of factors were stronger than those arising between similar ones; of all pairwise interaction types tested, 92.9% of them were characterized by two-factor enhancement. The three most powerful interactions detected were those for NLB ∩ TEM (62.7%), NLB ∩ annual average atmospheric pressure (PRS, 62.7%), and NLB ∩ ELE (61.9%). Further, we identified the most appropriate kind or range of key elements shaping vegetation development and dynamics. Altogether, our findings can serve as a timely scientific foundation for developing a vegetative resource management strategy for the Yangtze River basin that duly takes into account local climate, terrain, and human activity. Full article

The evaluation and monitoring of the ecological environment quality of heritage sites can help provide sustainable and healthy development strategies for heritage management organizations. In this study, an ecological evaluation model based on the remote sensing ecological index (RSEI) was used to measure the ecological environment of the Shibing Karst World Heritage Site and its buffer zone and the Moran index and geographic probe model were combined to quantify the ecological environment. The results show that, (1) from 2013 to 2020, the ecological environment quality of the heritage site and buffer zone was moderate to high and the mean RSEI values in the three periods studied were 0.720, 0.723 and 0.742, showing an overall upward and improving trend; (2) ecological environment quality grades of moderate and good accounted for more than 70% of the area, the distribution pattern of ecological environment quality is significantly better at the heritage site than in the buffer zone and the southwest is better than the northeast; (3) the Moran index increased from 0.600 in 2013 to 0.661 in 2020, residing in the first and third quadrants, respectively, with significantly spatial aggregation; and (4) greenness and humidity were shown to play a positive feedback role on the ecological environment quality and the spatial influence ability of humidity and dryness was greater. Overall, the RSEI is an effective method of evaluating and monitoring the ecological environment quality of heritage sites, the ecological environment quality of the Karst heritage site in Shibing is in a steady state of improvement and the relevant departments of heritage conservation need to further coordinate the relationship between conservation and development to promote the sustainable development of the heritage site and provide effective solutions for the monitoring of other Karst World Heritage sites. Full article

The objective assessment of ecological systems forms the basis of solving ecological environmental problems. Evaluating the ecosystem status of each county through the gross ecosystem product (GEP) can reveal the value of each ecosystem. In this study, we used the eco-economic method to calculate the GEP and the green gold index (GGI) of 13 counties in Hulunbuir City between 2015 and 2020. The results show that: (1) The GEP of Hulunbuir City in 2020 was 980.025 billion yuan. The GGI was 8.36, which was much higher than the national average. (2) Forestry and pastoral regions were the main contributors to the regulation service. (3) Hulunbuir City had the largest forest value, while the farmland value was the lowest. The most important sources of forest, grassland, wetland, water, and farmland value were Oroqen, Xin Right Banner, Xin Left Banner, Xin Right Banner, and Morin Banner, respectively. Based on our analysis, we found significant results through the transformation of the “Two Mountains” in Erguna, Genhe, and Zhalantun. The other counties in our study must optimize ecological research with respect to the traditional economic model. Our results provide a scientific reference for the application of the “Two Mountains” base in each county. Full article

Areas of grassland improvement and degradation were mapped and assessed to identify the driving forces of change in vegetation cover in the Three Rivers headwater region of Qinghai, China. Based on linear regression at the pixel level, we analyzed the vegetation dynamics of the grasslands of this region using MODIS NDVI data sets from 2000 to 2010. Correlation coefficients were computed to quantitatively characterize the long-term interrelationship between vegetation NDVI and precipitation/temperature variability during this period. The use of time series residuals of the NDVI/precipitation linear regression to normalize the effect of precipitation on vegetation productivity and to identify long-term degradation was extended to the local scale. Results showed that significant improvements occurred in 26.4% of the grassland area in the Three Rivers Headwater region between 2000 and 2010. The study area, which represents about 86.4% of the total grassland area of this headwater region, showed a general trend of improvement with no obvious trend of degradation. Full article

A specific set of environmental conditions characterizes plant species patterns and distribution on Earth. Similarly, riparian vegetation can be impacted by anthropogenic activities like mining practices involving the removal of vegetation cover, which destroys the structure and diversity of the habitat, adversely affecting the ecosystem services. In this study, we explored the role of environmental variables and biotic intervention in deriving spatial patterns and distribution of riparian vegetation at mining and non-mining sites along the most depleted Panjkora River basin in NW Pakistan. Vegetation data and its determining factors at 28 mining and non-mining sites (14 each) were sampled using 10 m × 10 m (100 m²) systematic plots at 50 m intervals along transects in a downstream direction from the upper catchments to the bottom junction with the Swat River. We recorded 186 species in both mining and non-mining sites, belonging to 70 families comprising 174 angiosperms, 3 gymnosperms, and 9 Pteridophytes. Results show that annual or perennial therophytic life forms predominated in the Panjkora River system, indicating anthropogenic disturbances. At the same time, the aggressively invasive species, such as Xanthium strumarium and Cannabis sativa, further heightened plant community disturbances. Generally, the species diversity was higher in non-mining sites and may be attributed to habitat fragmentation. Likewise, the Canonical Correspondence Analysis (CCA-ordination) revealed that geographic coordinate (i.e., latitude r = 0.80; longitude r = 0.75) and elevation (r = 0.95) were more meaningful predictors than soil texture (i.e., silt%, r = −0.30), nutrients (i.e., potassium, r = −0.35; phosphorus, r = 0.38) and soil pH (r = −0.50) in shaping the spatial pattern and vegetation structure. Our result implies that the present vegetation composition and spatial assemblages are due to heavy anthropogenic interventions, especially mining activities. Therefore, the heavily degraded fragile riparian system of the Panjkora River and its tributaries needed to be conserved and restored by predicting the composition of communities in response to changing climatic conditions. Full article

Habitat suitability (HS) describes the ability of the habitat to support living organisms. There are several approaches to estimate habitat suitability. These approaches are specific to a species or habitat or estimate general HS broadly across multiple species or habitats. The objectives of the study were to compare the approaches for estimating HS and to provide guidelines for choosing an appropriate HS method for conservation. Three HS estimation methods were used. Method 1 scores the suitability based on the naturality of the habitat. Method 2 uses the average of HS values found in the literature. Method 3 uses the species richness as an indicator for HS. The methods were applied to a case study in the Choctawhatchee River Watershed. GIS applications were used to model the suitability of the watershed. The advantages and disadvantages of the HS methods were then summarized. The multiple HS maps created using the three methods display the suitability of the watershed. The highest suitability occurred in the southern parts of the region. Finally, a decision support tool was developed to help determine which approach to select based on the available data and research goals. Full article

Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991–2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management. Full article

The Sierra Nevada region has experienced substantial wildfire impacts. Uncertainty pertaining to fire risk may be reduced by better understanding how air temperature (Ta: °C) influences wildfire ignitions independently of other factors. We linked lightning-ignited wildfires to Ta patterns across the region from 1992 to 2015 and compared monthly high- and low-air-temperature patterns between ignition and non-ignition locations at local scales (4 km). Regionally, more ignitions occurred in springs with a greater number of high-Ta months and fewer cool Ta months (analyzed separately) and in summers with fewer cool Ta months. Locally, summer ignition locations experienced warmer summer months on a normalized scale than non-ignition locations. The probability of a wildfire ignition was positively associated with a greater number of high-Ta months during and prior to fire seasons. Regionally, springs with a greater number of high-Ta months had more wildfire ignitions. Locally, as individual locations in the region experienced a greater number of high-Ta months preceding and including the fire season, they exhibited substantial increases in spring (+1446%), summer (+365%), and fall (+248%) ignitions. Thus, the frequent occurrence of high-Ta months is positively associated with lightning-ignited wildfires in the Sierra Nevada region. Full article

Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra ($R_{rs}\left(\lambda \right)$). The results indicate that even when substantial vegetation growth is observed, the variation in $R_{rs}\left(\lambda \right)$ is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area. Full article

Industrial and other human activities in Canada’s oil sands region (OSR) influence the environment. However, these impacts can be challenging to separate from natural stresses in flowing waters by comparing upstream reference sites to downstream exposure locations. For example, health indicators of lake chub (Couesius plumbeus) compared between locations in the Ells River (Upper and Lower) in 2013 to 2015 and 2018 demonstrated statistical differences. To further examine the potential sources of variation in fish, we also analyzed data at sites over time. When fish captured in 2018 were compared to pooled reference years (2013–2015), results indicated multiple differences in fish, but most of the differences disappeared when environmental covariates were included in the Elastic Net (EN) regularized regression models. However, when industrial covariates were included separately in the EN, the large differences in 2018 also disappeared, also suggesting the potential influence of these covariables on the health of fish. Further ENs incorporating both environmental and industrial covariates along with other variables which may describe industrial and natural influences, such as spring or summer precipitation and summer wind speeds and distance-based penalty factors, also support some of the suspected and potential mechanisms of impact. Further exploratory analyses simulating changes from zero and the mean (industrial) activity levels using the regression equations respectively suggest effects exceeding established critical effect sizes (CES) for fish measurements may already be present or effects may occur with small future changes in some industrial activities. Additional simulations also suggest that changing regional hydrological and thermal regimes in the future may also cause changes in fish measurements exceeding the CESs. The results of this study suggest the wide applicability of the approach for monitoring the health of fish in the OSR and beyond. The results also suggest follow-up work required to further evaluate the veracity of the suggested relationships identified in this analysis. Full article

Unmanned aerial vehicles (UAVs) have contributed considerably to forest monitoring. However, gaps in the knowledge still remain, particularly for natural forests. Species diversity, stand heterogeneity, and the irregular spatial arrangement of trees provide unique opportunities to improve our perspective of forest stands and the ecological processes that occur therein. In this study, we calculated individual tree metrics, including several multispectral indices, in order to discern the spectral reflectance of a natural stand as a pioneer area in Mexican forests. Using data obtained by UAV DJI 4, and in the free software environments OpenDroneMap and QGIS, we calculated tree height, crown area, number of trees and multispectral indices. Digital photogrammetric procedures, such as the ForestTools, Structure from Motion and Multi-View Stereo algorithms, yielded results that improved stand mapping and the estimation of stand attributes. Automated tree detection and quantification were limited by the presence of overlapping crowns but compensated by the novel stand density mapping and estimates of crown attributes. Height estimation was in line with expectations (R² = 0.91, RMSE = 0.36) and is therefore a useful parameter with which to complement forest inventories. The diverse spectral indices applied yielded differential results regarding the potential vegetation activity present and were found to be complementary to each other. However, seasonal monitoring and careful estimation of photosynthetic activity are recommended in order to determine the seasonality of plant response. This research contributes to the monitoring of natural forest stands and, coupled with accurate in situ measurements, could refine forest productivity parameters as a strategy for the validity of results. The metrics are reliable and rapid and could serve as model inputs in modern inventories. Nevertheless, increased efforts in the configuration of new technologies and algorithms are required, including full consideration of the costs implied by their adoption. Full article

To facilitate better implementation of flood control and risk mitigation strategies, a model for evaluating the flood defense capability of China is proposed in this study. First, nine indicators such as slope and precipitation intensity are extracted from four aspects: objective inclusiveness, subjective prevention, etc. Secondly, the entropy weight method in the multi-attribute decision making (MADM) model and the improved three-dimensional technique for order preference by similarity to ideal solution (3D-TOPSIS) method were combined to construct a flood defense capacity index evaluation system. Finally, the receiver operating characteristic (ROC) curve and the Taylor plot method were innovatively used to test the model and indicators. The results show that nationwide, there is fine flood defense performance in Shandong, Jiangsu and room for improvement in Guangxi, Chongqing, Tibet and Qinghai. The good representativity of nine indicators selected by the model was verified by the Taylor plot. Simultaneously, the ROC calculated area under the curve (AUC) was 70%, which proved the good problem-solving ability of the MADM-GIS model. An accurate assessment of the sensitivity of flood control capacity in China was achieved, and it is suitable for situations where data is scarce or discontinuous. It provided scientific reference value for the planning and implementation of China’s flood defense and disaster reduction projects and emergency safety strategies. Full article

The middle and lower reaches of the Yangtze River are the most important areas for geese to overwinter in the East Asian–Australasian Flyway, where about 180,000 geese fly to overwinter each year. Over the past 20 years, the region has experienced extensive and rapid land cover changes that may have exceeded the adaptability of geese, and have led to suitable goose habitat area loss, thereby, reducing the stability of the geese population. In order to identify the suitable goose habitat areas in this region, based on ensemble modeling and satellite tracking data, in this study, we simulated the spatial distribution changes in the suitable goose habitat areas over the past 20 years. The results showed that the suitable goose habitat areas had suffered varying degrees of loss, among which, the lesser white-fronted goose had the greatest suitable goose habitat area loss of over 50%. Moreover, we found that wetlands, lakes, and floodplains were the key components of suitable goose habitat areas, and the categories (land use) showed significant differences in different periods (p < 0.01). This may be one of the main reasons for the decrease in suitable goose habitat areas. The results of this study provide an important reference for the adaptive management and protection of geese in the middle and lower reaches of the Yangtze River. Full article

Sustainable land management (SLM) practices are key for achieving land degradation neutrality, but their continued implementation lag behind the progression of various forms of land degradation. While many scholars have assessed the drivers of SLM uptake for restoring land affected by desertification, drought, and floods (SDG 15.3 and partly SDG 2.4), little is known about the implication of SLM implementation on invasive alien species (IAS) management. This study aimed at understanding the challenges and proposing solutions for the uptake of SLMs with respect to the management of the invasive tree, Prosopis juliflora, in Baringo County, Kenya. Data were collected with semi-structured questionnaires, the responses were coded into themes, and c-coefficient tables were used to determine code linkages. Our results show that the availability of incentives is the main motivation for invasion management. Thus, management efforts have often focused on private parcels, while communally shared lands tended to be neglected despite their vulnerability to invasion. We conclude that sustainable IAS management lies at a landscape scale, and thus the national IAS management strategies should adopt a collective approach by empowering local actors to engage in SLM implementation. Full article

The southern beech (genus Fuscospora and Lophozonia) forest in New Zealand periodically has “mast” years, during which very large volumes of seeds are produced. This excessive seed production results in a population explosion of rodents and mustelids, which then puts pressure on native birds. To protect the birds, extra pest controls, costing in the order of NZD 20 million, are required in masting areas. To plan pest control and keep it cost-effective, it would be helpful to have a map of the masting areas. In this study, we developed a remote sensing method for the creation of a national beech flowering map. It used a temporal sequence of Sentinel-2 satellite imagery to determine areas in which a yellow index, which was based on red and green reflectance (red-green)/(red + green), was higher than normal in spring. The method was used to produce national maps of heavy beech flowering for the years 2017 to 2021. In 2018, which was a major beech masting year, of the 4.1 million ha of beech forest in New Zealand, 27.6% was observed to flower heavily. The overall classification accuracy of the map was 90.8%. The method is fully automated and could be used to help to identify areas of potentially excessive seed fall across the whole of New Zealand, several months in advance of when pest control would be required. Full article