Biomass Index Research Articles

Unveiling the Hidden Causes: Identifying the Drivers of Human–Elephant Conflict in Nilgiri Biosphere Reserve, Western Ghats, Southern India

Since the human population is growing beyond the earth’s ability to sustain it, more people are being brought into contact with wildlife, leading to increasing human–wildlife conflict. The Asian elephant, a wide-ranging megaherbivore, is being increasingly threatened by human–elephant conflict [HEC]. Its conservation depends on identifying the causes of HEC and implement measure to mitigate the HEC effectively. We studied the drivers of HEC among five forest divisions in Western Ghats, India, that support a high density of elephants across Asia. Comparing the last six years’ data on HEC with 26 ecological covariates in the GLMM framework, we identified spatiotemporal variations and drivers of crop, and property damages and human casualties. Spatially, HEC was highest in the territorial division and lowest in those declared as Protected Areas earlier. The comparison of crop damage with covariates showed that crop damage decreased with the grass biomass index, elephant density, extent of dry-thorn, and deciduous habitats, and forest range area, while it increased with adult male % and forest range perimeter. Similarly, the property damage by elephants increased with crop damage frequency and human settlement/cultivation area, but decreased with grass biomass, forest range area, and deciduous habitat area. Human casualties due to elephants increased with property damage, ambient temperature, and forest range perimeter, but decreased with grass biomass. Overall, the decrease in HEC with grass biomass, and the increase in HEC with human settlement and forest range perimeter indicate that anthropogenic pressure that decreases the grass biomass and degrades the habitat is the likely root cause of HEC, and minimizing it would reduce overall HEC.

Evaluation of the growth, enzymatic activity, electrolyte leakage, and phytoremediation efficiency of Conocarpus erectus under cadmium and lead stress.

Contamination of agricultural soil by heavy metals poses a significant threat to soil quality and crop yields. Using plants as a natural remediation approach attracts researchers' attention around the world. A 16-month pot experiment was conducted using Conocarpus erectus in a randomized complete block design. The growth, enzymatic activity, electrolyte leakage, and remediation potential were estimated under Cd nitrate]40 low (L), 60 medium (M), 80 high (H) mg/kg soil [and Pb nitrate]400 (L), 700 (M), 1,000 (H) mg/kg soil [applied individually and in combination. Conocarpus erectus demonstrated a good tolerance (over 70%) against lower and medium cadmium (Cd) and lead (Pb) levels and a medium resistance against high Cd and Pb levels, with a survival rate of 100% under all the treatments used. The most negative treatment on the growth traits and tolerance of C. erectus was (H) Cd and (H) Pb, which reduced plant height; chlorophyll index; dry weights of the leaves, stems, and roots; root length; and tolerance index of biomass and roots by 25.87%, 48.97%, 50.56%, 47.25%, 58.67%, 50.18%, 51.00%, and 50% in comparison to the respective control, consecutively. Relative to the control, all Cd and Pb applications increased polyphenol oxidase (PPO), peroxidase (POD), and catalase (CAT) activities, and the increment was parallel up to medium Cd and Pb levels and then decreased with their high levels but still higher than the control. Electrolyte leakage (EL) was upheaved by raising the levels of Cd and Pb, and it reached the maximum (52.79%) at the (H) Cd (H) Pb treatment. Cd and Pb in the leaves, stems, and roots were boosted by raising their levels in the treatments. Conocarpus erectus is considered a phytoextractor for the Cd levels used because the bioconcentration factor of the stem (BCFs) and the translocation factor (TF) of Cd were >1, and it is a suitable plant for Pb phytoextraction at (L) Pb, (M) Pb, and (M) Cd (M) Pb levels because its Pb BCFs and bioconcentration factor of the root (BCFr) were <1 and its Pb TF was >1. On the other hand, C. erectus is considered a phytostabilizator for Pb at (H) Pb, (L) Cd, (L) Pb, and (H) Cd (H) Pb levels because its Pb BCFs, BCFr, and TF were <1.

The spatiotemporal standardization of the stock index and reduction of hauls in research surveys (on the example of red king crab in the Barents Sea)

The paper presents the results of the spatiotemporal standardization of the stock biomass index of red king crab in the Russian EEZ of the Barents Sea carried out using the generalized additive mixed effect model (GAMM) which was implemented in the sdmTMB library of the Renvironment. The spatiotemporal approach used in species distribution modelling within GAMM enables taking into account numerous environmental factors and fishing gears, thereby increasing the quality of the assessment especially in areas with data shortage. Additionally, in modelling the stock density for areas with no observations the space-time autocorrelation in between hauls is also taken into consideration.

Towards sustainable exploitation of European pilchard (Sardina pilchardus) stocks off West African waters

European pilchard (Sardina pilchardus), herein referred to as “sardine” or “European sardine”, is the most productive and economically important small pelagic fish in the Northwest African waters. Thus far, very little is known about its stock status in the West African region despite the rapid decadal increase in its catch production. The present study provides avenues vital to improving guidance for sustainable species management. In this region, sardine is divided administratively into three stocks (northern: stock N, central: stock A + B, and southern: stock C). Thus, the present study used two Surplus Production Models (SPM) to evaluate sardine's stock status. Sardine's catch and scientific survey biomass data obtained from FAO-CECAF's (Fishery Committee for the Eastern Central Atlantic (CECAF)) latest reports were used in this analysis to fit the SPMs and investigate the effectiveness of the available catch-per-unit-effort (CPUE) trends in setting the total allowable catch (TAC) for sustaining the different sardine stocks in the region. Results from the SPMs indicate that the sardine stock in the north is fished at optimum levels. Meanwhile, the SPMs output based on the scientific survey biomass indices indicated a healthy state for the central and southern sardine stocks. Similarly, the fishery CPUE indices for the central and southern stocks showed similar biomass trends compared to the survey biomass index, indicating healthy stock status. Therefore, in cases where survey biomass index data is unavailable to run stock assessments, this study suggests that sardine fishery CPUEs could be used in SPMs to monitor biomass trends over time and may also provide stock status proxies. Additionally, with rapid climate and other environmental changes, sardine stocks must be regularly monitored with consistent and comparable scientific methodologies and updated fisheries time series data.

Study on the Design Parameters of Landscape Elements of Urban Tunnel Body Segments in China Based on Biomass Perception

To provide a comprehensive analysis of how an intricate landscape environment comprising various landscape elements influences driving safety and comfort. Beginning with the individual design of the tunnel body section, which includes the roof, sidewalls, road surface, and sidewalk facade markings, this paper references the driver’s biomass index to identify design alterations that satisfy basic safety requirements through simulated driving experiments. Subsequently, employing orthogonal experimental design and semi-supervised clustering algorithms, we ascertain the optimal combination of the four main landscape elements of the tunnel body section that align with both safety and comfort objectives. The experimental findings demonstrate that a safe and comfortable landscape design for urban tunnel sections does not entail a single optimal design; rather, any landscape design conforming to the criterion of “patterned landscapes are set on the side walls and roofs of the tunnel instead of the surface of the road” is the optimal design.

Enhanced phytoremediation of 2,4-DNP-contaminated wastewater by Salix matsudana Koidz with MeJA pretreatment and associated mechanism.

2,4-Dinitrophenol (2,4-DNP) is recognized as an emerging contaminant due to its high toxicity and poor biodegradability, posing a threat to animals, plants, and human health. The efficient removal of 2,4-DNP remains a challenging issue in phytoremediation research, particularly because of its toxic effects on plants. To address this, a hydroponic simulation experiment was conducted to investigate the impact of adding exogenous methyl jasmonate (MeJA) on the tolerance and purification capabilities of Salix matsudana Koidz (S. matsudana) seedlings exposed to 2,4-DNP. The results indicated that the addition of exogenous MeJA mitigated the damage caused by 2,4-DNP to S. matsudana seedlings by enhancing the activity of antioxidant enzymes, reducing excess reactive oxygen species (ROS), lowering membrane lipid peroxidation, and minimizing membrane damage. Notably, the most effective alleviation was observed with the addition of 50 mg·L-1 MeJA. Furthermore, exogenous MeJA helped maintain the biomass indices of S. matsudana seedlings under 2,4-DNP stress and increased the removal efficiency of 2,4-DNP by these seedlings. Specifically, the addition of 50 mg·L-1 MeJA resulted in a removal percentage of 79.57%, which was 11.88% higher than that achieved with 2,4-DNP treatment. In conclusion, exogenous MeJA can improve the plant resistance and enhance 2,4-DNP phytoremediation.

Geographical Environment and Plant Functional Group Shape the Spatial Variation Pattern of Plant Carbon Density in Subalpine-Alpine Grasslands of the Eastern Loess Plateau, China

The carbon density of subalpine-alpine grasslands (SGs) is significantly vital to sustaining the carbon cycle in global terrestrial ecosystems. However, on the Loess Plateau of China, it remains unclear how the geographical environment and plant functional groups affect the spatial variation pattern of plant carbon density in these grasslands. Here, nine typical SGs distributed in the eastern Loess Plateau with elevations ranging from 1720 to 3045 m were investigated. The biomass indices from grassland plants of different functional groups were investigated using plot surveys. The Kriging interpolation method was used to explore the spatial variation pattern of plant carbon density along geographical gradients. We found that (1) the total plant carbon density of SGs was 2676.825 g C/m2 on the eastern plateau, with 37.07%, 37.50%, and 25.43% contributed by the northern, central, and southern areas, respectively. Above- (666.338 g C/m2) and belowground (2010.488 g C/m2) carbon density accounted for 24.9% and 75.11% of the total, respectively. (2) At the horizontal scale, the plant carbon density in the northern SGs was high in the northwest and low in the southeast; in the central SGs, it was low in the northwest and high in the southeast; and in the southern SGs, it was high in the southwest and low in the northeast. At the vertical scale, plant carbon density in all SGs decreased with increasing altitude. (3) The carbon density of grasses, forbs, and sedges was 247.419 g C/m2, 26.073 g C/m2, and 23.471 g C/m2, respectively. With increased latitude, the carbon density of all functional groups (grasses, forbs, and sedges) decreased; the carbon density of forbs and grasses increased with increased longitude, while that of sedges decreased; and with increased altitude, the carbon density of all functional groups increased. In conclusion, the spatial variation pattern of plant carbon density in the SGs was not only influenced by the geographical environment but also by the plant functional groups at the horizontal and vertical scales on the eastern Loess Plateau of China.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

STATUS OF THE TURBOT (Scopthalmus Maeoticus, Pallas, 1814) AGGLOMERATIONS ALONG THE ROMANIAN BLACK SEA COAST

The main commercially valuable species in the Black Sea is turbot (Scophthalmus maeoticus, Pallas, 1814). Thus, turbot stock assessment is vital to ensure its sustainable use, to support economic and ecological stability, to inform science-based management decisions, and to promote the conservation of marine biodiversity. To determine turbot aggregations, the BioIndex routine, developed in R language, was used to estimate different biological indicators related to this target species. In particular, the routine allows the estimation of time series of mean abundance and biomass indices, the inverse CV of abundance indices, mean individual weight and sex ratio at the GSA level. Taking into account the biomass values resulted for the study period (2019-2020) a significant increase was observed in the values from the period 2012-2018 and the maximum yield (RMM) obtained of 329,048 tons for the Romanian area of the Black Sea.

Non-model method for the stock status assessing and catch forecasting of the Flounder 26+28 subdivisions ICES in the Baltic Sea

The aim of this article: to assess the flounder stock state in the 26+28 ICES subdivisions of the Baltic Sea and forecast its catch in the Russian part of the 26th ICES subdivision using non-model methods.Methods used: The qualitative condition of the stock was assessed by length (LBI method — Length Based Indicators). To develop catch recommendations for flounder stock in 26+28 ICES subdivision, as well as in the Russian sea area, was used an approach for a stock category 3 (ICES classification), based on a combined biomass index for fish from bottom trawl surveys. The assessment was based on the ratio of the average values of population indices of the last two years to the previous values for three years. All methods have been successfully tested at the international level within the framework of seminars and Working Groups of the International Council for the Exploration of the Seas (ICES), in which Russian specialists took part.Novelty: In conditions of insufficient completeness and quality of available information to justification the flounder catch, using models of the exploited stock, for the Russian water area of the 26th ICES subdivision of the Baltic Sea, non-model methods for assessing the state and predicting its catch were used for the first time. Result: to solve problems of rational management and conservation of fish stocks, the LBI method, as a non-model assessment tool, allows timely identification of qualitative changes occurring in the stock. The flounder stock is within biologically safe limits. The fishery is aimed at preserving small and large fish (conservation option), the stock is exploited optimally. It is possible to maintain the species’ production level at least at the current level.Practical significance: In modern conditions the current level of information support for forecasting the flounder stock state and catch in the Baltic Sea will make it possible to solve problems related to the management of this resource in the near future.

Using benthic recruitment densities to forecast fisheries recruitment of American lobster in Atlantic Canada

There is limited understanding of the mechanisms underlying the marked spatial and temporal trends in landings of American lobster in Canada since the mid-1980s, and little ability to forecast changes. We built an individual-based model for three Lobster Fishing Areas (LFAs) in Canada that uses the annual density of benthic recruits in nursery grounds to generate an index of exploited biomass 6 to 10 years in the future, using von Bertalanffy growth equations complemented with inter-individual variability in growth, and regional maturity ogives and spawning probabilities to allow exclusion of ovigerous females from the exploitable biomass. We found significant positive relationships between the modelled biomass index and fisheries landings in all three LFAs. Out-of-sample validation in two LFAs (longer time series) using 1-year and 6-year reduced data sets revealed mean annual prediction errors of 11.4% and 11.2%, and 23.7% and 51.7%, respectively. Our findings strongly suggest benthic recruitment indices could help make strategic decisions concerning lobster fishing activities, and they argue for the continued and expanded monitoring of lobster benthic recruitment in Canada.

Alnus glutinosa (Alder) sapling as a phytoremediator for cadmium in contaminated soil of industrial park

Heavy metals can contaminate soil through human activities such as mining, waste disposal, excessive use of chemical fertilizers, and fossil fuels. In Iran, uncontrolled urban growth and lack of waste collection systems have led agricultural lands to become receptors of urban and industrial waste, resulting in the contamination of soil with cadmium, which has negative consequences for the environment and health. Alder sapling may have considerable potential for phytoremediation. The soil was placed in plastic pots of 40 cm in diameter with 7 kg air-dried weight soil per pot, spiked with solutions of cadmium chloride [Cd Cl2.H2O] at rates of 0 (control), 50, 100, 150, and 200 mg/kg in a solution. The analyzed data revealed a significant reduction in vegetative growth (plant height, leaf area, chlorophyll, stem diameter growth, stem dry weight, root dry weight, and leaves dry weight). The means were compared to assess the impact of cadmium accumulation on certain biochemical and physiological characteristics. The mean separation was performed using Duncan's Multiple Range test with a p-value ≤0.05. However, there was a simultaneous significant increase in enzymatic activities (peroxidase and catalase) and an increase in proline compared to control plants. The bioconcentration factor of stem, leaves, and roots was under one. The translocation factor (TF) of cadmium was more than 1, and cadmium contents were more localized in roots compared to leaves and stems, minimizing the risk of contamination through leaves. Alnus glutinosa has defense mechanisms against cadmium up to 19.5 mg/kg soil, as the tolerance index for roots and biomass is more than 0.8. Finally, it is a good candidate for research on phytoremediation and phytoextraction.

Evaluation of Lipid Profile in Patients with Ischemic Heart Disease

Ischemic heart diseases (IHDs), like coronary artery disease, remain a significant global health burden. While established risk factors such as lowdensity lipoprotein cholesterol (LDL) and hypertension are wellrecognized, the role of triglyceride (T.G.) and high-density lipoprotein cholesterol (HDL-c) remains complex and multifaceted. This work was designed to examine the role of the T.G./HDL-c ratio in the IHD group and their effect on the effect of age, gender, and biomass index (BMI). Method: A total of 90 samples, including 60 samples (30 male, 30 female) for the IHD patients group with an age range of 40-85 years, and 30 samples (15 male, 15 female) for the control group with matching age ranges, were randomly selected. Lipid profile parameters (HDL, LDL, VLDL, T.G., Cholesterol) were estimated according to Kit Shenzhen Mindray Bio— Medical Electronics—Germany's manual procedure. Then, the ratio TG/HDL-C was calculated. Results: IHD patients illustrated a significant increase in the mean levels of all lipid profile panels, including [total cholesterol (T.C.), triglyceride (T.G.), LDL, and the ratio of T.G./HDL-c], except HDL, which was lower in patients group compared to the healthy control. The level of T.G./HDLc was increased in IHD patients with increasing BMI associated with the healthy participants; it was also elevated in male IHD patients than in IHD female patients. Conclusion: The ratio of T.G./HDL-c represents a valuable indicator for valuing IHD risk and personalized therapeutic approaches. Future research should delve deeper into the underlying mechanisms and refine strategies for effectively managing T.G./HDL-c for improved IHD prevention and management.

A Lidar Biomass Index of Tidal Marshes from Drone Lidar Point Cloud

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (HPA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R2 = 0.5) than the commonly applied spectral index approaches (R2 = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services.

Biophysical characterization of summer Arctic sea-ice habitats using a remotely operated vehicle-mounted underwater hyperspectral imager

The impact of a rapidly shifting sea-ice cover on climate, ecosystem processes and biophysical habitat properties is not yet fully understood, particularly in the central Arctic Ocean, due to a lack of spatially representative observations. From June to July 2020 during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, leg 4) in the Transpolar Drift, we deployed an underwater hyperspectral imager (UHI) mounted on a remotely operated vehicle (ROV) to characterize the biophysical properties of different sea-ice habitats. We conducted UHI surveys along two transects: i) under level first-year sea ice (FYI), which had a mean sea-ice draft of 1.4 m and was composed of primarily level FYI but also had a relatively shallow ridge (keel depth ∼2.6 m); and ii) under the flank of a ridge, named Jaridge, with a mean ice draft of 1.7 m, which was composed of a mix of level ice and thicker ridge blocks with over 3 m draft. We present a new unsupervised bio-optical quantification algorithm for hyperspectral surveys, the relative ice algal biomass index (RBI), using spectral mixture analysis (SMA). We compare this method to the supervised machine learning habitat classification algorithm, Support Vector Machine (SVM). The RBI showed good agreement to literature-based normalized difference indices (NDI) and PCA analyses, which confirm the RBI as a reliable unsupervised index for ice algal biomass. Our biophysical characterization of the two surveyed regions showed an association of sea-ice algal biomass with sea-ice ridge features. Our surveys also indicate that ice algal spatial distribution may be influenced by ice melt rates, and the formation of under ice meltwater layers and false bottoms. With high spatial coverage (>100 m) at microscale resolution (∼cm) we documented large spatial variability of summer Arctic sea-ice algal biomass and different patterns between adjacent ice habitats. We further demonstrate the need for improved understanding of sea-ice algal spatial variability as a complementary tool for sea-ice biogeochemical sampling using destructive ice core sampling.

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

The former industrial site of the Angarsk Metallurgical Plant (Svirsk, Russia) 10 years later: current geochemical state and interannual change analysis based on Earth remote sensing data

The former industrial site of the Angarsk Metallurgical Plant (Svirsk, Russia) 10 years later: current geochemical state and interannual change analysis based on Earth remote sensing data

Satellite Image Fusion Airborne LiDAR Point-Clouds-Driven Machine Learning Modeling to Predict the Carbon Stock of Typical Subtropical Plantation in China

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR2 = 0.01~0.27, ΔRMSE = 1.88~13.77 m3·hm−2, ΔMAE = 1.17~13.57 m3·hm−2). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R2 is greater than 0.91, while the fitting R2 of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the “airborne LiDAR + satellite remote sensing” data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project.

Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama

En este trabajo usamos Machine Learning (Randon Forest) como herramienta para clasificar la biomasa y calcular los índices de vegetación buscando identificar las características de la cobertura vegetal en la cabecera del desierto Atacama. Se busca establecer la correlación entre los índices de vegetación y la precipitación, a fin de conocer su confiabilidad sobre la climatología en esta región. Fue importante el análisis geoespacial basado en Google Earth Engine (GEE) y el procesamiento de imágenes Landsat 5 ETM y Landsat 8 OLI/TIRS, para el período 1985 - 2022, lo que permitió caracterizar el cambio climático. El NDVI, SAVI, GVI y RVI han sido probados y validados en sistemas áridos. El NDVI responde positivamente a la precipitación en temporada húmeda y en forma débil en la temporada de lluvias invernales. Se confirma que el NDVI alto corresponde al verano, después de una sequía prolongada. Hacia los años 2020 y 2022, se registra un aumento de cobertura vegetal en lugares de mayor temperatura, evidenciando cambio climático y reflejado en los índices de biomasa.

Decadal changes in biomass and distribution of key fisheries species on Newfoundland's Grand Banks.

Canadian fisheries management has embraced the precautionary approach and the incorporation of ecosystem information into decision-making processes. Accurate estimation of fish stock biomass is crucial for ensuring sustainable exploitation of marine resources. Spatio-temporal models can provide improved indices of biomass as they capture spatial and temporal correlations in data and can account for environmental factors influencing biomass distributions. In this study, we developed a spatio-temporal generalized additive model (st-GAM) to investigate the relationships between bottom temperature, depth, and the biomass of three key fished species on The Grand Banks: snow crab (Chionoecetes opilio), yellowtail flounder (Limanda ferruginea), and Atlantic cod (Gadus morhua). Our findings revealed changes in the centre of gravity of Atlantic cod that could be related to a northern shift of the species within the Grand Banks or to a faster recovery of the 2J3KL stock. Atlantic cod also displayed hyperaggregation behaviour with the species showing a continuous distribution over the Grand Banks when biomass is high. These findings suggest a joint stock assessment between the 2J3KL and 3NO stocks would be advisable. However, barriers may need to be addressed to achieve collaboration between the two distinct regulatory bodies (i.e., DFO and NAFO) in charge of managing the stocks. Snow crab and yellowtail flounder centres of gravity have remained relatively constant over time. We also estimated novel indices of biomass, informed by environmental factors. Our study represents a step towards ecosystem-based fisheries management for the highly dynamic Grand Banks.