Fish farming and its appeal to common bottlenose dolphins: modelling habitat use in a Mediterranean embayment

Abstract

1. Common bottlenose dolphins Tursiops truncatus interact with fish farms in the Mediterranean Sea. These interactions were investigated in a Greek bay by incorporating multiple geographic, bathymetric, oceanographic, and anthropogenic variables. 2. Generalized additive models (GAMs) and generalized estimation equations (GEEs) were used to describe dolphin presence. Visual surveys were conducted over 2909 km under favourable viewing conditions that included 54 dolphin group follows for 457 km. Sea surface temperature (SST) and chlorophyll‐a (Chl‐a) data were obtained from remote sensing imagery, and distances to sources of human influences including fish farms, a ferro‐nickel plant, and a slag disposal area were calculated within a geographic information system (GIS). 3. Bottlenose dolphins were encountered mainly in the south‐eastern portion of the study area, and occurrence was not clearly related to SST and Chl‐a, nor the ferro‐nickel plant or nearby slag disposal area. 4. Dolphin occurrence generally increased within 20 km of fish farms, with four farms and dolphins displaying a positive relationship, seven no clear relationship, and two a negative one. 5. While it is likely that uneaten food and other detritus attract dolphin prey, individual farms (or clusters of farms) clearly had a different appeal. The proximity of the ferro‐nickel plant and slag disposal area to ‘attractive’ fish farms could compromise dolphin health, but physiological data are unavailable. 6. The modelling of multiple variables allowed for a description of dolphin habitat use and attraction to some fish farms. More such data analysed in similar manner would be instructive for other areas where marine mammals and fish farms co‐occur. Copyright © 2013 John Wiley & Sons, Ltd.

Modelling dolphin distribution within an Important Marine Mammal Area in Greece to support spatial management planning

“…Because sea conditions can severely affect the probability of encountering dolphins (Buckland et al, ; Evans & Hammond, ), sea state was categorized using a fine‐tuned scale (instead of a standard Beaufort or Douglas scale): S1 (flat), S2 (calm, but rippled), and S3 (non‐breaking wavelets of less than 20‐cm high). All data collected during unfavourable conditions (sea state above S3, observers not looking for dolphins, or navigation under non‐standard conditions) were removed from the analysis to account for inaccuracy under those sampling conditions (Bonizzoni et al, ; Pirotta, Matthiopoulos, MacKenzie, Scott‐Hayward, & Rendell, ). When dolphins were sighted, the research boat slowed down and stayed within 100 m of the animals.…”

“…There is also value in comparing submodels, each with distinct hypotheses (Planque, Loots, Petitgas, Lindstrøm, & Vaz, ). Consequently, four submodels were used (as described in Bonizzoni et al, ), each built with a specific set of explanatory variables: geographic (latitude and longitude), bathymetric (bottom depth and bottom slope), environmental (SST, Chl‐ a , and euphotic depth), and anthropogenic (distance to fish farms, distance to coastal red mud deposit, and distance to offshore red mud deposit). Each submodel included an effort index and sea state (to account for sampling bias), year (to account for any temporal variation among years), and block (to account for autocorrelation within blocks).…”

Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea

“…In order to model this gradient, we included longitude as a variable in the habitat model (Certain 2008). Spatial variables, such as longitude and latitude, though not directly related to the biology or ecology of the species, nor to their prey, can be considered proxies for variables that cannot be measured, such as sub-regional differences in species distribution (Ashe et al 2010, Pirotta et al 2011, Spyrakos et al 2011, Bonizzoni et al 2013). The variables used as predictors are summarized in Table 1.…”

Fine‐scale habitat partitioning of Chilean and Peale's dolphins and their overlap with aquaculture

“…However, most effects probably operate via multiple indirect and possibly synergistic pathways such as exclusion from important habitat, habitat degradation, food web alterations, noise pollution, contamination and spread of disease (Buschmann et al, 2009(Buschmann et al, , 2012Kemper et al, 2003;Markowitz, Harlin, Würsig, & McFadden, 2004;Pearson, Vaughn-Hirshorn, Srinivasan, & Würsig, 2012;Ribeiro, Viddi, Cordeiro, & Freitas, 2007;Watson-Capps & Mann, 2005). Fish farms can also affect dolphin habitat use patterns (Bonizzoni et al, 2014), because farmed fish or cageassociated wild fish can act as attractive food sources (Piroddi, Bearzi, & Christensen, 2011).…”