Predicting the Spatial Distribution of a Seabird Community to Identify Priority Conservation Areas in the Timor Sea

Abstract

Understanding spatial and temporal variability in the distribution of species is fundamental to the conservation of marine and terrestrial ecosystems. To support strategic decision making aimed at sustainable management of the oceans, such as the establishment of protected areas for marine wildlife, we identified areas predicted to support multispecies seabird aggregations in the Timor Sea. We developed species distribution models for 21 seabird species based on at-sea survey observations from 2000-2013 and oceanographic variables (e.g., bathymetry). We applied 4 statistical modeling techniques and combined the results into an ensemble model with robust performance. The ensemble model predicted the probability of seabird occurrence in areas where few or no surveys had been conducted and demonstrated 3 areas of high seabird richness that varied little between seasons. These were located within 150 km of Adele Island, Ashmore Reef, and the Lacepede Islands, 3 of the largest aggregations of breeding seabirds in Australia. Although these breeding islands were foci for high species richness, model performance was greatest for 3 nonbreeding migratory species that would have been overlooked had regional monitoring been restricted to islands. Our results indicate many seabird hotspots in the Timor Sea occur outside existing reserves (e.g., Ashmore Reef Marine Reserve), where shipping, fisheries, and offshore development likely pose a threat to resident and migratory populations. Our results highlight the need to expand marine spatial planning efforts to ensure biodiversity assets are appropriately represented in marine reserves. Correspondingly, our results support the designation of at least 4 new important bird areas, for example, surrounding Adele Island and Ashmore Reef.

Seasonal shifts in foraging distribution due to individual flexibility in a tropical pelagic forager, the Ascension frigatebird

“…Predicted proportion of time of an individual foraging trip (upper panels) and time allocated to a unit area of 1000 km 2 (lower panels) in 10 km distance bands from Ascension Island during either the incubation (left panels) or the chick-rearing stage (right panels) derived from a generalised linear mixed model with individual as a random effect; grey bands indicate 95% confidence intervals distant areas at sea than boobies, which range a maximum of 350 km from Ascension (Oppel et al 2015). In the absence of distinct environmental gradients and substantial interspecific competition, it is plausible that frigatebirds forage very broadly, rendering their distribution very difficult to predict based on readily available environmental variables (Lavers et al 2014). Alternatively, our tracking efforts, which mostly followed individuals outside the peak breeding phase, may not have occurred at the appropriate time to detect environmental relationships.…”

Avian SDMs: current state, challenges, and opportunities

“…General application Examples Specific issues Examples Conservation Identification of priority areas for bird conservation Guisan et al 2013, Frick et al 2014 Seabirds and marine environments Lavers et al 2014 Identifying protected areas to meet specific targets Naoe et al 2015 Identifying no-go areas to reduce human-wildlife conflicts in wind power planning Reid et al 2015 Identifying specific habitats for certain species needs Brambilla and Saporetti 2014 Validating umbrella species to match conservation goals Fourcade et al 2017 Evaluating or forecasting the effect of environmental changes Green et al 2008 Future effectiveness of protected areas over different spatial scales Coetzee et al 2009, Hole et al 2009, Veloz et al 2013, Virkkala et al 2013, Brambilla et al 2015 Kissling 2013, Tracewski et al 2016 Including changes in demography Haché et al 2016 Including nest predation and food limitation Harris et al 2012 Including wind farm construction Bastos et al 2016 Invasive birds Predictions of invasion risk Muñoz and Real 2006, Nyári et al 2006, Real et al 2008, Strubbe and Matthysen 2009, Herrando et al 2010, Di Febbraro and Mori 2015, Fraser et al 2015 Range dynamics under climate change Huntley et al 2007, Reino et al 2009, Graham et al 2011 (Continued) …”

Important foraging areas of seabirds from Anguilla, Caribbean: Implications for marine spatial planning

“…In addition, marine species that demand protection tend to also be highly migratory [10]. The use of seabirds to inform marine spatial planning has been tried and tested [11] with both at-sea survey data [12] and tracking data being used to map areas of high seabird species richness [13,14]. Such areas, in turn, are likely to represent key marine habitats or Contents lists available at ScienceDirect important sites where fish congregate [15].…”

“…Such areas, in turn, are likely to represent key marine habitats or Contents lists available at ScienceDirect important sites where fish congregate [15]. These results have then led to recommendations in the designation of marine protected areas and in the demarcation of marine area zones [12,16,17].…”