Using predictive modeling and biogeography to protect remote and
underrepresented habitats
The protection of marine habitats across the globe has increased significantly in
the past century. Much of this protection has been centered on more convenient areas
such as coastal environments, or aesthetically pleasing and popular areas such as coral
reefs. Environments that are remote and hard to access (out of sight) or that do not hold
the same charismatic value as certain species and habitats (out of mind) have been
severely neglected in regards to conservation development and management. Hackmann,
1995, eloquently summed this dilemma up writing that when selecting areas to protect,
terrestrial or aquatic, our choice has been driven “more by opportunity than design,
scenery rather than science” (Lourie & Vincent 2004).
Remote and inhospitable areas such as the deep sea and polar seas are
increasingly becoming recognized for their ecological and economical importance
(Davies, Roberts, & Hall-Spencer, 2007; Griffiths, 2010; Ramirez-Llodra et al., 2011).
Researchers are becoming more aware of the interdependence among marine ecosystems,
and the need for ecoregional planning (Ramirez-Llodra et al. 2011). However, these
habitats are still so poorly understood that it is incredibly hard to develop efficient and
sound conservation plans regarding these critical areas. In the Antarctic, most species
known are quite rare (with records of only one or two specimen per species), and the
numbers and status of these organisms are not known (Griffiths, 2010). Coupling this
with the massive size of Antarctic ecosystems it becomes incredibly hard to comment on
human impact in the area and even more difficult to attempt to mitigate it (Griffiths,
2010). Similarly, despite deep-sea habitats taking up an estimated 62% of the surface of
the Earth, we understand very little about it and the majority of species are yet to be
discovered (Davies, Roberts, & Hall-Spencer, 2007; Ramirez-Llodra et al. 2011). The
few long term time-series studies that have occurred in the deep sea show that climate
change and anthropogenic impacts are already being seen in these habitats, meaning we
could be loosing species and ecosystems before we even know that they exist or
understand their contribution to the global ocean (Ramirez-Llodra et al. 2011).
In order to better understand these remote habitats and begin developing reliable
and efficient conservation policies, such as Marine Protected Areas, in these critical
habitats we need to better understand the habitats themselves. Using biogeography and
predictive habitat mapping to better understand these remote habitats could lead to the
identification of priority areas for conservation action as well as efficient mitigation
strategies for these areas ((Lourie & Vincent, 2004)). Biogeography has been shown to
strengthen conservation planning by providing the tools necessary to:
analyze/communicate relevant information, ways in which to relate biological
distributions with the spatial and temporal scales in which they are operating (by use of
oceanographic data), maps of biological distribution at varying spatial scales (i.e. species,
genes, ecosystems), and the use of surrogates to model future conditions (Lourie &
Vincent, 2004). By combining field data, satellite/ aerial images, GIS data, and
mathematical modeling, Lourie and Vincent (2004), argued biogeography produces
conservation strategies and management plans that are more defensible and appropriate
and priorities that are more scientifically credible. Watling, Guinotte, Clark, and Smith
(2013) published A proposed biogeography of the deep ocean floor, delineating 14 lower
bathyal and 14 abyssal provinces, based on high-resolution hydrographic and organic
matter flux data incorporated with the Global Open Ocean and Deep Sea classifications
developed in 2009 (Watling et al., 2013). The authors created global models for
biological communities in these areas that can be paired with quantitative analysis (e.g.
predicative habitat modeling data) to mitigate exploitation in these remote habitats and
promote the development of efficient marine protected areas. Predictive habitat modeling
gives researchers the ability to provide complete coverage maps of species distributions
and better estimate the extent of listed habitats and MPA effectiveness (Ross & Howell,
2013). Ross & Howell 2013 took three deep-sea habitats listed as vulnerable marine
ecosystems in the NE Atlantic and MaxEnt modeled them to generate habitat model
maps. These maps were validated by using the presence/absence evaluation library on R
and by using the area under the receiver-operating curve (AUC) (Ross & Howell, 2013).
Threshold-dependent model evaluation indices were used to assess model reliability
(Ross & Howell, 2013). The generated models were considered fair to excellent, with one
model ranking quite high and receiving validation from literature (Ross & Howell, 2013).
Ross & Howell, 2013 showed some of their sights as being more properly protected and
others in need of increased protection and suggest some future direction for future MPA
creation.
Overall, studies indicate that there is an urgent need for more research into these
remote habitats and that marine policy formation should be centered on these
underrepresented areas of our oceans. By using biogeography and predictive habitat
modeling scientists can get a better picture of this hard to study area from which more
informed decisions can be made. Recent work has shown that by using biogeography and
predicative habitat modeling, together, the most accurate information possible can be
garnered from models generated and the most scientifically credible conservation
priorities and resource management plans can be developed.
References:
Davies, A. J., Roberts, J. M., & Hall-Spencer, J. (2007). Preserving deep-sea natural
heritage: Emerging issues in offshore conservation and management.
Biological Conservation, 138(3-4), 299-312. doi:
10.1016/j.biocon.2007.05.011
Griffiths, H. J. (2010). Antarctic marine biodiversity--what do we know about the
distribution of life in the Southern Ocean? Plos One, 5(8), e11683. doi:
10.1371/journal.pone.0011683
Lourie, S. A., & Vincent, A. C. (2004). Using biogeography to help set priorities in
marine conservation. Conservation Biology, 18(4), 1004-1020.
Ramirez-Llodra, E., Tyler, P. A., Baker, M. C., Bergstad, O. A., Clark, M. R., Escobar, E., .
. . Van Dover, C. L. (2011). Man and the last great wilderness: human impact
on the deep sea. Plos One, 6(8), e22588. doi: 10.1371/journal.pone.0022588
Ross, R. E., & Howell, K. L. (2013). Use of predictive habitat modelling to assess the
distribution and extent of the current protection of ‘listed’ deep-sea habitats.
Diversity and Distributions, 19(4), 433-445. doi: 10.1111/ddi.12010
Watling, L., Guinotte, J., Clark, M. R., & Smith, C. R. (2013). A proposed biogeography
of the deep ocean floor. Progress in Oceanography, 111, 91-112. doi:
10.1016/j.pocean.2012.11.003