Integrating distribution models and habitat classification maps into marine protected area planning

Abstract

Effective conservation planning requires biotic data across an entire region. In data-poor ecosystems conservation planning is informed by using environmental surrogates (e.g. temperature) predominantly in two ways: to develop habitat classification schemes (1) or develop species distribution models (2). We test the utility of both approaches for conservation planning of marine ecosystems, and rank environmental surrogates, such as depth and distance from shore, according to their power to predict the distribution and abundance of biotic species. Specifically, we compared a habitat classification scheme; based on coarse levels of habitat types derived from depth and distance from shore; against species distribution models, which predict fish abundance and prevalence as a function of environmental surrogates (depth, distance from shore, latitude, reef area, zoning, and several metrics of habitat structural complexity). We consistently set conservation target levels to 21% of each conservation feature, following global standards and a sensitivity analyses. Thus when running scenarios to protect fish species we aimed to protect at least 21% of each species, and when running scenarios of habitat classes, we aimed to protect at least 21% of each habitat class. We found that when aiming to protect 21% of the chosen conservation targets, distribution models protected 21% of the predicted abundance/occurrence of all modelled species and functional groups, but did not protect most habitats. Contrastingly, using a habitat classification scheme protected 21% of all habitat types and 34% of all species and functional groups, but required protecting three times more area. Thus, using only distribution models as targets in data-poor ecosystems could be a risky conservation planning strategy. Ultimately the best conservation outcomes were achieved by incorporating local knowledge to synthesize the conservation outcomes of both scenarios.

Optimising Sampling Strategies in Coral Reefs Using Large-Area Mosaics

“…To date, the main limitations of traditional sampling approaches consist in the fact that they have been restricted to relatively small spatial scales because of the high-cost associated with getting high-resolution data over large areas [6,34]; or they used remote sensing techniques resulting in coarser spatial resolution data provision [35]. The application of close-range photogrammetry based on SfM algorithms presented here, along with techniques recently developed (e.g., autonomous and semi-autonomous underwater vehicles), can help fill the need for wider areal coverage (kilometre scales) and high spatial resolution (meter to sub-centimetre scale) data provision [5,11,12,36] to ensure current and comprehensive benthic composition mapping [37]. Moreover, to overcome the sampling limitation of assessing the true value of the parameter being quantified for a given area, high-resolution large-area imaging allows the acquisition of data (high-resolution digitisation of cover) that can be used as a baseline or 'truth' against which cover estimates would be compared [20].…”

Mapping habitat protection priority over a marine ecoregion under information gaps

“…Ecosystem-based MCP at an ecoregion scale always faces challenges from incomplete data on biodiversity distributions, and hence adopts surrogates, assuming they will provide effective protection over key biodiversity patterns (Ferrari et al, 2018;Rodrigues & Brooks, 2007;Ward et al, 2019). A good surrogate selection strategy include adopting biological features with some ecological overlaps between surrogates and target features, reference to information extrapolated from field data (Rodrigues & Brooks, 2007), and associating habitat characteristics with defined species niches (Barton et al, 2020).…”

Biogeographical affinities, trophodynamics, and fisheries pressure in the fish community of the Laguna Madre Tamaulipas

“…Thus, because effective management must be based on the incorporation of scientific and local knowledge (Ferrari et al, ; Urra et al, ), the present study propose: (a) bilateral management plans of the LMT and Laguna Madre Texas; (b) focal plans of use, management and conservation for ecosystems under fishing pressure (e.g. mangroves and oyster bank); (c) the establishment of a monitoring agenda of the LMT to propose precise habitat‐season‐plans of prevention and management of invasive species; and (d) environmental education and consultative meetings with fishermen.…”

“…Thus, because effective management must be based on the incorporation of scientific and local knowledge (Ferrari et al, 2018;Urra et al, 2018), the present study propose: These aspects may provide scientific based and locally discussed, understood and agreed plans, that result in the adaptation of the policies for monitoring, management, use, and conservation of this highly important and sensitive ecosystems.…”