Aim: The Irrawaddy dolphin (Orcaella brevirostris) is an endangered cetacean found throughout Southeast Asia. The main threat to this species is human encroachment, led by entanglement in fishing gear. Information on this data-poor species’ ecology and habitat use is needed to effectively inform spatial management.

Location: We investigated the habitat of a previously unstudied group of Irrawaddy dolphins in the eastern Gulf of Thailand, between the villages of Laem Klat and Khlong Yai, in Trat Province. This location is important as government groups plan to establish a marine protected area.

Methods: We carried out boat-based visual line transect surveys with concurrent oceanographic measurements and used hurdle models to evaluate this species’ patterns of habitat use in this area.

Results: Depth most strongly predicted dolphin presence, while temperature was a strong predictor of group size. The highest probability of dolphin presence occurred at around 10.0 m with an optimal depth range of 7.50 to 13.05 m. The greatest number of dolphins was predicted at 24.93^oC with an optimal range between 24.93 and 25.31^oC. Dolphins are most likely to occur in two primary locations, one large region in the center of the study area (11^o54’18”N to 11^o59’23”N) and a smaller region in the south (11^o47’28”N to 11^o49’59”N). Protections for this population will likely have the greatest chance of success in these two areas.

Main Conclusions: The results of this work can inform management strategies within the immediate study area by highlighting areas of high habitat use that should be considered for marine spatial planning measures, such as the creation of marine protected areas. Species distribution models for this species in Thailand can also assist conservation planning in other parts of the species’ range by expanding our understanding of habitat preferences.

Data were collected during line transect boat surveys using the following equipment:

We added distance to river mouths for each environmental data point via measurement in ArcGIS. Analyses were carried out in R version 2.13.1. We removed outliers and binned both turbidity and chlorophyll-a into high, medium, and low using Jenks natural breaks classification. We employed hurdle models using a negative binomial distribution to analyze the count data (binary data are modeled using a binomial distribution). 

Data availability was uneven across years, so we separated the data into 5 smaller datasets. Datasets 1-4 include all data points for each year, so variables with missing values were left out. The fifth dataset contains only points for which all variables were measured, so it is smaller. Please see Data_Key .docx file for numbers used to indicate location and year within datasets.