This study establishes the dynamic biogeographic regions (DBGRs) of the Panama Bight (PB) based on remote-sensing reflectance (R
rs412
and R
rs488
) of the MODIS-Aqua sensor and the sea-surface ...temperature (SST) and the concentration of chlorophyll a (Chl-a) derived from the same sensor. Monthly images with a spatial resolution of 4 km from the MODIS-Aqua sensor were used for July 2002-July 2020. To define the DBGR, the first standardized empirical orthogonal function (SEOF
1
) was calculated. Due to the low spatial variability in the PB, the number of DBGRs found with the analysis of the SST and Chl-a was lower than that found in the analysis of the R
rs
. Therefore, the DBGRs of the PB were delineated based only on the study of the R
rs
. The PB was divided into 11 DBGRs belonging to two provinces: oceanic and coastal. Five of the DBGRs were coastal, the others oceanic, including a transition one. The coastal regions were characterized by the highest averages of Chl-a and high SST. On the other hand, the regions located northeast of the PB (Panama Gulf, PB-transition, and Choco) were characterized by an average SST greater than 28°C and an average Chl-a between 0.8 and 1.0 mg·m
−3
. The DBGRs located further west (west and PB-west) showed an average SST greater than 28°C and the lowest concentrations of Chl-a. The DBGRs PB-east and PB-central, presented average SSTs of 26.7-27.1°C and average Chl-a concentrations lower than 0.5 mg·m
−3
. The south Ecuador DBGR presented the lowest average SST (<25 °C) and average Chl-a of 0.50 mg·m
−3
. Our approach allowed us to generate a dynamic regionalization of the PB, which most of the year has small SST and Chl-a gradients, making this an alternative to the homogeneous areas generated from the thermohaline structure.
The Dengue, Chikungunya and Zika viruses are arboviruses predominantly transmitted to humans through the bite of the female mosquito Aedes aegypti. Currently, the vector represents a potential ...epidemiological risk in several Latin American and Pacific countries. However, little is known about the geographical distribution and bioclimatic suitability of this mosquito in the projected climate change scenarios in Colombia. Using a species distribution model of maximum entropy (MaxEnt) based on presence-only records obtained from Global Biodiversity Information Facility (GBIF), land elevation obtained from Shuttle Radar Topography Mission (SRTM) and bioclimatic variables (WorldClim), we produced environmental suitability maps of this mosquito vector for present and future geographic distribution. The future distribution were constructed based on the Community Climate System Model (CCSM4) for the years 2050 and 2070, projected according to the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 described by the Intergovernmental Panel on Climate Change (IPCC). For the current conditions, Colombia has ~140,612.8 square km of areas with the possible presence of the vector; however, for the future, this will be reduced by more than 30%. For the future conditions, the suitable areas for A. aegypti decreased compared to the present, mainly for the year 2070 under RCP scenarios 4.5 and 8.5, however, the probability of mosquito occurrence increases in some departments of Colombia. Areas susceptible to the presence of A. aegypti are affected by climate change. The Caribbean and Andean regions have a high probability of mosquito distribution; therefore, control and epidemiological surveillance are required in these areas. The results can serve as an input to define preventive and control measures, especially in areas with a higher risk of contracting the virus.
Mathematical modeling; Climate change; Viral vector; Public health; Environmental change; Aedes aegypti; Environmental suitability; Geographical distribution; MaxEnt; Colombia
The District of Buenaventura is in the west of Colombia and of vital importance for the country in many aspects. Apart from being a robust economic contributor to the region, and the country, because ...of its ports, tourists and biodiversity, it is the urban nucleus with the largest population in the Colombian Pacific. It has a varied coastline with several types of the geomorphological elements ranging from hills with cliffs, extensive mangrove plains to barriers islands with beach fronts forming most of the coastline. This study applied the coastal vulnerability index (CVI) using eight variables, three physical/hydrodynamical, three geological/geomorphological, and two socioeconomic variables, which are as follows: Rate of Sea Level Rise, Mean Tidal Range, Significant Wave Height, Shoreline Change Rate, Geomorphology, Regional Coastal Slope, Land Use and Coverage, Population – coastal settlements and economic activities, respectively. The variables considered are parameters of relative vulnerability and are evaluated using the geographic information system (GIS) and remote sensing procedures, employing a geospatial approach. The littoral was classified into five ranges of relative vulnerability, evaluating 276 sections; 19.57% (54 km) is in the range of very high vulnerability, 19.93% (55 km) in the high vulnerability range, 19.20% (53 km) in the moderate vulnerability range, 21.01% (58 km) in the low vulnerability range, and 20.29% (56 km) in the very low vulnerability range. These results can be used as an essential tool allowing decision-makers to establish management measures aiming to reduce risks and for communities to adapt to future changes in the face of the relative rise in sea level.
•Calculation of a Coastal Vulnerability Index (CV) for the Buenaventura District.•The coastal vulnerability ranges are in close percentages.•The first CVI study for the Colombian Pacific coast.•Installation of tide gauges and their continuous monitoring long the coast is recommended.•Decision-makers can learn about the particular conditions of littoral studied.
Some projections predict that fishery resources in tropical areas will be negatively affected by climate change, resulting in the displacement of species and reducing their availability for fishing. ...In this study, the potential geographic distribution of Scomberomorus sierra under current conditions in the Colombian Pacific Ocean was simulated using maximum entropy (MaxEnt) modeling software, based on species presence data and satellite-derived environmental variables (Sea Surface Temperature (SST), Chlorophyll-a and bathymetry). The future distributions of S. sierra in 2020s (short term) and 2080s (long term) were projected under the RCP 2.6 and 8.5 scenarios for four ensembled global circulation models (GCM) obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The current and future geographical distributions were modeled for the species' fishing months (November to April), and pixel-wise change distribution and core shift were determined. The results indicated good performance for the distribution models in the present and future scenarios (AUC > 0.9). The RCP 8.5 scenario, in both, the short and long term, indicated the highest adverse changes in the species distribution. The distribution core shift indicates that under RCP 2.6 in the 2020s for November and December, the shift is towards the central zone of the Colombian Pacific. In the 2080s (long term), the distribution centroid tends to move towards the central zone, further from the coastline. Results also showed the same change tendency for RCP 8.5 in both the 2020s and 2080s. This is one of the first studies that elucidate the effects of climate change on a commercial species in the Colombian Pacific. The results give an insight into future management strategies for seerfish fisheries, which can also be used as a reference for studying other species.
•A synoptic view on the potential impact of climate change on seerfish along the Colombian Pacific coast.•RCP 8.5 in both the 2020s and the 2080s indicated the highest adverse impact on seerfish habitat distribution.•The seerfish distribution tends to move away from the Colombian coastline.•First study along the Colombian Pacific on the effect of climate change on fish species distribution.
The current capacity in the Caribbean region to enhance the knowledge about algal blooms and harmful algal blooms has several logistical constraints. This work aimed to explore the detection of ...possible algal blooms using Moderate Resolution Imaging Spectroradiometer (MODIS) Fluorescence Line Height (FLH) data in the Colombian Caribbean Sea between 2003 and 2013. Monthly FLH images with 4 km of spatial resolution were processed and classified. The relationship between the Sea Surface Temperature (SST) and the FLH were explored using a Geographically Weighted Regression. The results showed three areas identified as having possible persistent blooms: the Urabá Gulf (UG), Magdalena Rivermouth (MRM), and Guajira Peninsula (GP). The SST does not have any considerable influence on the variation in the FLH. The supply of nutrients during the rainy season may be causing the frequent massive algae growth. MODIS fluorescence was useful as a screening tool to identify risk areas for potential algal blooms.
•A synoptic view of possible algal blooms•Climatic factor seems to influence the frequent possible algal blooms.•MRM, UG, GP with the recurrence of possible algal blooms in the Colombian Caribbean Sea•Magdalena River Mouth area with highly recurrent possible algal blooms
Fish populations respond to environmental change with daily, monthly, and annual time delays, depending on each species life cycle. However, these delays are rarely included in Generalized Additive ...Models (GAMs) for species distribution that uses time-series data. Therefore, the predictions of these models entirely rely on assumptions of immediate fish response to oceanic factors. Spatial autocorrelation is also an issue for GAMs because datasets of fish occurrence usually exhibit this property, and though it has been progressively considered for modeling, it is still frequently ignored. These problems cause low model performance, unstable predictions and more importantly, wrong conclusions for fisheries management. We built and applied Generalized Additive Models with spatial terms and delayed effects of oceanic covariates (SDE-GAMs) to investigate model performance and prediction power for the spatiotemporal distribution of the skipjack (Katsuwonus pelamis), a species of commercial importance across the Exclusive Economic Zone (EEZ) of the Colombian Pacific Ocean. We used satellite-derived Surface Sea Temperature (SST), Sea Level Anomaly (SLA), and Chlorophyll-a (CHLA) as predictors for the Catch Per Unit of Effort (CPUE), considering monthly delayed covariate effects and spatial terms at intra-annual cycles. We evaluated performance improvement of SDE-GAMs compared to that of traditional GAMs (T-GAMs: only immediate covariate effects) and spatial GAMs (S-GAMs: immediate covariate effects plus spatial terms). The model performance of SDE-GAMs was on average 25.4% higher, while its prediction error was on average 43% lower. One, two and three-month delayed SST effects were the primary drivers of CPUE throughout the intra-annual cycle across the EEZ. SDE-GAMs were able to predict both general patterns and smaller details of the spatiotemporal distribution of skipjack, capturing sub-regional differentiation with high importance for management and decision making.
•Habitat predictions improved including delayed effects and spatial autocorrelation.•Model performances of SDE-GAMs were 25.4% higher than for traditional GAMs.•SDE-GAM’s predicted both general patterns and smaller details of the distribution.•SST with 1, 2 and 3 month delays were primary driver of intra-annual changes.•SDE-GAM’s captured sub-regional differentiation for regional management.
The Colombian Pacific Coast is renowned for its exceptional biodiversity and hosts vital mangrove ecosystems that benefit local communities and contribute to climate change mitigation. Therefore, ...estimating mangrove aboveground biomass (AGB) in this region is crucial for planning and managing these coastal forest covers, ensuring the long-term sustainability of the essential environmental services provided by the Colombian Pacific Coast (CPC). This study employed a spatial estimation approach to assess mangrove AGB, evaluating various parametric and non-parametric models using a multisensor combination and machine learning on the Google Earth Engine (GEE) platform within the CPC. Synthetic aperture radar (SAR) satellite imagery (ALOS-2/PALSAR-2, SRTM, NASADEM, and ALOSDSM) and optical data (Landsat 8) were utilized to quantify mangrove AGB in 2022 across the four departments of the CPC. The Random Forest model exhibited superior predictive performance compared to the other models evaluated, achieving values of R2 = 0.783, RMSE = 38.239 Mg/ha, MAE = 27.409 Mg/ha, and BIAS = 0.164. Our findings reveal that the mangrove AGB map for the CPC exhibits a mean ± standard deviation of 181.236 ± 28.939 Mg/ha across eight classes, ranging from 88.622 Mg/ha to 378.21 Mg/ha. This research provides valuable information to inform and strengthen various management strategies and decision-making processes for the mangrove forests of the CPC.
•Modeling revealed spatio-temporal mismatches between Piangua distribution and mangroves.•Piangua and mangroves don’t distribute equally under climate change, causing mismatches.•High overlap of ...Piangua and mangroves in Nariño, Cauca, S. Valle del Cauca, and Chocó.•Climate change drives future mismatches between Piangua and mangroves, especially under SSP5.•Management strategies needed for Piangua and mangroves based on environmental suitability.
The Piangua Anadara tuberculosa and Anadara similis are bivalve species thrive among mangrove roots and are crucial for vulnerable human communities, providing both economic and nutritional support. In the Colombian Pacific coast, significant efforts have been directed towards understanding the abundance and population structure of these mangrove bivalves. However, the impact of climate change on the spatiotemporal relationship between the potential distribution of these bivalves and their potential habitat, Rhizophora mangle, remains underexplored. We developed distinct Ecological Niche Models (ENMs) for both bivalve species and their potential habitat based on species presence-pseudo-absence data, soil physicochemical attributes, and bioclimatic variables projected for the present and future in the Colombian Pacific coast. The projections for 2030 and 2050 were formulated using the optimistic (Shared Socioeconomic Pathways –SSP1), intermediate (SSP2), and pessimistic (SSP5) climate change scenarios as proposed by the Intergovernmental Panel on Climate Change (IPCC) in its sixth report. Currently, there is a significant correlation between the potential distribution of the Piangua species and the mangroves on the Colombian Pacific coast. However, this relationship is expected to undergo spatiotemporal changes due to future climate shifts, especially by 2050 under the most pessimistic climate scenario (SSP5). Our findings offer valuable insights for the management and conservation of both the Piangua and the mangroves in the Colombian Pacific coast. Conservation efforts for the Piangua species should prioritize areas that are likely to remain suitable for both the species and its associated habitat, the mangroves.
Seer fish (Scomberomorus sierra) and mullet (Mugil cephalus) are some of the most important marine fishery resources along the Colombian Pacific Ocean. The objective of this study was to forecast the ...landings of seer fish and mullet based on data from time-series annual landings reported by the Food and Agriculture Organization of the United Nations (FAO) from 1971 to 2014. The study considered autoregressive integrated moving-average (ARIMA) processes to forecast the landings of the species. The ARIMA model (5,1,5) for seer fish and ARIMA model (2,2,1) for mullet showed good agreement concerning the observed data on landings based on the Akaike information criterion. The results show the ARIMA model to be a suitable method for analyzing statistics. In data-poor fisheries situations, this method can support potential evaluations of fishery production for decision making and management.
Considering that fishing grounds could be locations where repetitive fishing efforts are carried out to guarantee economic gain, we analyzed the spatial distribution of the standardized effort ...associated with the foreign purse seine tuna fleet that operated in eastern tropical sector of the Eastern Pacific Ocean between 2009 and 2015, monitored by the Colombian Fisheries Observer Program. The standardization of the fishing power and fishing effort of 17 tuna vessels began with a principal component analysis applied to a correlation matrix calculated for purse seine length, height, and area, carrying capacity, vessel length, tonnage, and beam. The first principal component explained 72.58% of the variance and was used to develop a multiple linear regression model from which the mathematical expression to standardize fishing power (FP) was derived: FPvessel=1+1.84⋅12.10−1⋅C1. Fishing effort was standardized by multiplying the fishing power by the nominal effort of each vessel. Maps of the spatial distribution of these efforts as indicators of fishing grounds were then created. The highest concentration of fishing effort occurred between 2° and 4° N, and from the coastline to 82° W, coinciding with fishing grounds known as “Banco Tumaco”, “Pasacaballos”, and “Banco Colombia” in the southeastern part of the colombian Exclusive Economic Zone.
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