Exploring the relationship between dolphins and fisheries: uncovering the spatial and temporal patterns that influence potential conflicts along Portugal's north coast

This study analyzes spatiotemporal patterns of common dolphin sightings and fishing activity along Portugal's north coast using AIS data and a Generalized Additive Model to identify overlapping areas and environmental predictors, revealing that dolphins frequently inhabit fishing grounds likely due to shared habitat preferences rather than avoidance.

The Gist

Imagine the ocean off the north coast of Portugal as a bustling, underwater highway. On one side of this highway, you have dolphins (specifically the common dolphin) cruising around looking for a snack. On the other side, you have fishing boats zooming along, nets in tow, hunting for the exact same snack: sardines.

This paper is essentially a traffic report for that underwater highway. The researchers wanted to know: Where do the dolphins and the fishing boats cross paths? Is it a friendly meeting, or a dangerous collision waiting to happen?

Here is the story of their findings, broken down simply:

1. The Setup: A Shared Lunchbox

Think of the ocean as a giant cafeteria. The dolphins and the fishermen are both hungry for the same dish: sardines.

• The Dolphins: They are the "Nearly Threatened" regulars of this cafeteria. They are everywhere, but they are getting a bit scarce, so we need to be careful with them.
• The Fishermen: They use big nets (like purse seines) to scoop up sardines.
• The Problem: When two hungry groups go for the same food at the same time, they might bump into each other. Sometimes, a dolphin gets accidentally caught in a fishing net (this is called bycatch). This study wanted to map out exactly where these "bumps" are most likely to happen.

2. The Detective Work: How They Tracked the Traffic

The researchers acted like ocean detectives using two main tools:

• The Dolphin Watchers: They sailed out on boats (the ATLANTIDA project) for four years, keeping a log of where they saw dolphins. They only counted sightings when the weather was good and they were actively looking.
• The Boat Tracker: They used a digital system called AIS (like a GPS tracker for ships) to see where the fishing boats were working. It's like looking at a heat map of where the fishing "traffic" is heaviest.

3. The Findings: When and Where They Meet

The researchers found some interesting patterns, almost like a seasonal dance:

• The Summer Rush: The dolphins were most visible in the summer. Why? Because the water gets cooler due to a natural phenomenon called "upwelling" (cold, nutrient-rich water rising from the deep). This is like a "buffet opening" for sardines. When the sardines are fat and happy, the dolphins show up to eat, and the fishermen show up to catch them.
• The Overlap: The maps showed that the dolphins and the fishing boats are often in the same neighborhood. In fact, the fishing effort was almost the same in areas where dolphins were seen as in areas where they weren't.
  • The Analogy: This is like finding that people who go to a specific park for a picnic are just as likely to be there as people who go to the park to jog. The dolphins aren't avoiding the fishermen; they are actually choosing the same spot because that's where the food is.

4. The "Traffic Report" (The Data)

The scientists used a fancy computer model (a "Generalised Additive Model," or GAM) to predict the traffic. Here is what the model told them:

• Time: Dolphins are most active in summer.
• Location: They like specific latitudes (specific strips of the coast) and specific water depths.
• Temperature: They prefer cooler water (which usually means more food).
• Fishing: Interestingly, the model suggested that when fishing effort gets too high, dolphin sightings might drop slightly. This doesn't mean the dolphins are scared away; it might mean the fishermen are working so hard in one spot that they are cleaning out the food, or perhaps the dolphins are just moving to a different part of the buffet.

5. The Catch (Limitations)

The authors were honest about the holes in their map:

• The "Ghost" Boats: The GPS data they used only tracks big boats (over 12 meters). It misses the tiny, local fishing boats. It's like trying to map traffic in a city but only counting trucks and ignoring all the cars and motorcycles. These small boats might be interacting with dolphins too, but the data doesn't show it.
• The "Ghost" Dolphins: Dolphins spend most of their time underwater. If they aren't at the surface when the researchers look, they don't get counted. It's like trying to count birds in a forest but only looking when they are singing; you might miss the ones that are silent.
• No "Crash" Data: They mapped where the dolphins and boats overlap, but they didn't have data on actual accidents (bycatch). So, they know the risk is high, but they don't know exactly how many dolphins have been hurt yet.

The Bottom Line

This paper is like a warning sign placed on the underwater highway. It tells us:

"Hey, in the summer, along the northern coast of Portugal, the dolphins and the fishing boats are all heading to the same spot to eat. This is a high-risk zone."

Why does this matter?
By knowing exactly where and when these overlaps happen, managers can create rules to keep everyone safe. Maybe they can tell fishermen to slow down in certain areas during summer, or set up "no-go zones" for nets when the dolphins are feeding. It's about making sure the dolphins and the fishermen can share the ocean without anyone getting hurt, ensuring the "buffet" stays open for everyone in the future.

Technical Summary

1. Problem Statement

The study addresses the critical issue of bycatch and interactions between cetaceans and fisheries, specifically focusing on the short-beaked common dolphin (Delphinus delphis) and fishing activities along the northern coast of mainland Portugal (Espinho to Caminha).

• Context: The region is a high-productivity zone supporting both abundant dolphin populations and intense fishing activity (particularly purse seining targeting sardines, Sardina pilchardus).
• Risk: The overlap in habitat and prey resources creates a high risk of unintentional capture (bycatch), which is a primary extinction risk for small cetaceans.
• Gap: While interactions are known to occur, there is a need for a comprehensive, data-driven assessment of the spatiotemporal patterns of these interactions to identify specific "hotspots" of conflict and inform conservation management.

2. Methodology

The researchers employed a multi-faceted approach combining field observation, remote sensing, and statistical modeling.

• Study Area: Northern Portuguese coast (2–12 nautical miles from shore), characterized by a broad continental shelf and seasonal upwelling regimes.
• Data Sources:
  • Dolphin Sightings: Data from the ATLANTIDA project (2021–2024). Surveys included long campaigns (twice yearly) and short campaigns (year-round) using visual monitoring protocols. Only "on-effort" data (active surveying) was used to minimize detection bias.
  • Fishing Effort: Automatic Identification System (AIS) data from Global Fishing Watch (GFW). Apparent fishing events were aggregated into a grid to calculate fishing effort (hours/km²).
  • Environmental Variables: Static (depth) and dynamic oceanographic data (Sea Surface Temperature [SST], Chlorophyll-a [CHL], Sea Surface Salinity [SSS]) sourced from GEBCO and Copernicus Marine Environmental Monitoring Service (CMEMS).
• Analytical Techniques:
  • Encounter Rate Analysis: Calculated as sightings per km surveyed, stratified by season.
  • Spatial Overlap Mapping: Creation of binary grids for Fishing Effort (FE) and Encounter Rate (ER) to identify cells where both were above seasonal averages (Overlap = 2).
  • Ecological Niche Modeling: A Generalised Additive Model (GAM) with a negative binomial distribution was used to predict dolphin occurrence.
    • Response Variable: Encounter rate.
    • Predictors: Year, season, latitude, longitude, depth, SST, CHL, SSS, and fishing effort.
    • Selection: Backward selection based on Akaike Information Criterion (AIC) and Chi-squared tests.

3. Key Results

A. Encounter Rates and Seasonality

• Total Data: 108 sightings recorded over 51 survey days (4,423 km surveyed).
• Seasonal Peak: Summer (July–September) showed the highest encounter rate (3.662 sightings/km), followed by spring, autumn, and winter.
• Correlation: High summer encounter rates align with increased survey effort but also suggest biological drivers (prey availability and warmer waters).

B. Spatial Overlap and Fishing Effort

• Co-occurrence: There is a significant spatial overlap between fishing grounds and dolphin hotspots, particularly near the coast.
• Effort Comparison: Fishing effort was nearly identical in areas with sightings (2.00 h/km²) and areas without sightings (1.62 h/km²).
• Implication: Dolphins do not appear to actively avoid fishing areas; rather, they frequent them due to shared habitat preferences (likely driven by prey).
• Risk Hotspots: Summer had the highest number of overlapping grid cells (13), indicating the highest probability of interaction during this season.

C. Generalised Additive Model (GAM) Findings

The best-fitted model included Year, Season, Latitude, Fishing Effort, SST, and Depth.

• Significant Predictors (p < 0.05):
  • Year: Encounter rates have increased over the study period (2021–2024).
  • Season: Summer and Winter showed significant differences compared to the baseline.
  • Latitude: Encounter rates fluctuated, peaking around 41.0°N and 41.3°N.
  • SST: A negative relationship was observed; encounter rates decreased as SST increased (suggesting a preference for cooler, upwelling-influenced waters).
  • Fishing Effort: Showed a descending trend (negative correlation), though the confidence intervals were broad, limiting the strength of this inference.
• Non-Significant Variables: Chlorophyll-a (CHL) and Sea Surface Salinity (SSS) were not retained in the final model.

4. Key Contributions

1. Baseline Establishment: Provides the first integrated assessment of dolphin-fishery conflict patterns using AIS and visual survey data specifically for the northern Portuguese coast (2021–2024).
2. Behavioral Insight: Challenges the assumption that dolphins avoid fishing gear; instead, evidence suggests they are attracted to fishing zones due to shared prey resources (sardines).
3. Temporal Risk Identification: Identifies summer as the critical window for potential bycatch, driven by the convergence of high dolphin presence, intense purse seine activity, and sardine spawning/recruitment cycles.
4. Ecological Drivers: Confirms that SST and Year (likely linked to sardine stock recovery following management plans) are stronger predictors of dolphin distribution than fishing effort itself.

5. Limitations

• Detection Bias: Visual surveys cannot detect submerged animals; passive acoustic monitoring or eDNA could improve detection.
• AIS Data Gaps: AIS data primarily covers vessels >12m. Small-scale fisheries (<12m), which are prevalent in the area and highly relevant to dolphin interactions, are often underrepresented.
• Lack of Direct Bycatch Data: The study infers risk based on spatial overlap but lacks direct observer data on actual bycatch events to validate the interaction.
• Resolution: Remote sensing data may lack the fine-scale resolution needed to capture micro-habitat variability in this narrow shelf area.

6. Significance and Conclusion

The study underscores the necessity for mitigation strategies that account for the ecological and socio-economic overlap between fisheries and cetaceans.

• Management Implications: Conservation efforts should focus on summer months and specific latitudinal bands (41.0°N–41.3°N) where the risk of interaction is highest.
• Future Directions: The authors recommend integrating real-time fishing data, observer programs, and stranding network data to validate risk models.
• Overall Impact: The findings provide a crucial scientific baseline for developing coexistence strategies, ensuring the protection of the "Near Threatened" common dolphin population while supporting sustainable fisheries in the Iberian Atlantic.