Harnessing citizen science for marine conservation in Malta: a comparative analysis of GAM and MaxEnt models in bottlenose dolphin habitat mapping
Background Species distribution models (SDMs) are powerful tools for informing conservation, particularly for highly mobile marine species such as common bottlenose dolphins (Tursiops truncatus). In Maltese waters, the limited availability of data on this species has constrained the effectiveness of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-08-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/19804.pdf |
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| Summary: | Background Species distribution models (SDMs) are powerful tools for informing conservation, particularly for highly mobile marine species such as common bottlenose dolphins (Tursiops truncatus). In Maltese waters, the limited availability of data on this species has constrained the effectiveness of conservation efforts. Despite the designation of offshore Special Areas of Conservation (SACs), key coastal regions need more detailed spatial studies to support evidence-based management. Methods In this study, we analyzed and compared the outputs of a generalized additive model (GAM) and a maximum entropy (MaxEnt) model to assess summer habitat suitability for bottlenose dolphins within a coastal SAC in Malta. The models were informed by presence-only data collected through systematic surveys and a citizen science campaign, integrated with environmental and anthropogenic predictors including chlorophyll-a concentration, sea surface temperature anomaly, slope, and distance to aquaculture sites. Results Both modeling approaches identified high habitat suitability in shallow, nearshore regions, with chlorophyll-a concentration and proximity to aquaculture sites emerging as the most important predictors. Slope and sea surface temperature anomaly contributed less substantially. The two models showed spatial agreement in highlighting these nearshore areas as core habitats, though GAM predicted a broader extent of suitable habitat, whereas MaxEnt results were more spatially restricted. Both models demonstrated strong predictive performance (AUC > 0.85), reinforcing the ecological relevance of the identified drivers. Conclusion This study demonstrates the potential of integrating opportunistic data with SDMs to support habitat assessments in data-limited contexts. The use of complementary modeling approaches provides robust insights into species–environment relationships. These results aim to guide spatial planning and future assessments of conservation priorities in Maltese coastal waters. |
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| ISSN: | 2167-8359 |