MPA modelling in a spatiotemporal perspective

Image credit: Isaac Chou on Unsplash

Oceans as a dynamic system

The physical and chemical conditions of the ocean are extremely variable across locations. This high environmental variability induces a “patchy” distribution of habitats and species(1,2) and, while some flexibility in adapting to sub-optimal conditions exists, the distribution of every animal and vegetal species is determined by their physiological limits for parameters like temperature, pH, and salinity.

The physio-chemical characteristics of marine ecosystems are not only characterized by spatial variation, but also by periodic fluctuations. Seasonality is a significant cause of this temporal variability and generates changes both on a global and local scale. Factors that change across seasons include, among others: temperature, light intensity, salinity, and current velocity(3). The magnitude of these seasonal changes increases with the latitude, and it is minimal at the equator.

The seasonal variation in ocean conditions is associated with one of the most important biological phenomena: the phytoplankton bloom(2). This event occurs when, with the increased light and temperature in spring, the phytoplankton grows thanks to the nutrients carried in the upper ocean layer during winter water mixing(2,4). The bloom is an important direct food resource food multiple species, many small fishes and crustaceans for example, and the base of the food chain of other predatory species, like sharks and mysticetes.

Phytoplankton bloom: nutrients and sunlight trigger the bloom, the base of many food chains (graphic by: Alessandra Sellini)

Considering both the seasonal, recurrent large-scale fluctuations in physical conditions and food availability, it stands to reason that marine mammals and seabirds living and evolving at temperate latitudes move where the most suitable conditions of each life stage are. In other words, they display migration patterns. This phenomenon is referred to as the annual cycle in distribution, and it is associated with vital functions such as reproduction, migration, and feeding(2). Migratory species have in common wide geographic ranges and cyclical movements, of variable distance, between distinct geographical areas. This also implies high site fidelity(2,5), meaning the tendency to return to a place they occupied before(6). For example, females of humpback whales, along with their calves, return to summer feeding grounds off the Gulf of Maine, eastern Canada, west Greenland, and the eastern North Atlantic(7).

Map of migratory routes of different cetacean species (image credit: WWF)

Under the Convention on Migratory Species, migratory species are defined as “the entire population or any geographically separate part of the population of any species or lower taxon of wild animals, a significant proportion of whose members cyclically and predictably cross one or more national jurisdictional boundaries”(8).

While each species fills its own ecological niche and may exhibit site fidelity in specific areas, they often overlap in distribution and habitat use. Aggregations of different species, particularly those adapted to feed on aggregate prey like plankton, can be found in areas with the greatest food availability(5).

Many of these migratory species are apex predators feeding at the top of the food chain and play a fundamental role in preserving the balance of the entire ecosystem. Their removal, for example as a consequence of fishing or habitat destruction, can therefore have significant impacts on the ecosystem(5). Yet, many of these migratory species are endangered and in need of urgent protection. According to the IUCN Red List, 21% of all marine migratory species are classified as threatened, including, 27% of seabirds and 15% of marine mammals(5). Several species, particularly marine mammals(42%), are considered Data Deficient, making threat assessments challenging(5).

MPAs, a static tool for highly mobile species

The conservation of marine migratory species is is both urgent and problematic

The susceptibility of these species to anthropogenic threats is driven by their movements, and they have to face both site-specific threats, changing throughout their range (e.g. fishing and bycatch, habitat destruction), and widely distributed threats (e.g. climate change)(5). The threats have a cumulative effect, meaning that the consequences of human activities “build up” in space and time.

Examples of global and local threats marine migratory species have to face throughout their range; local threats are location-specific (graphic by: Alessandra Sellini)

Conservation solutions should address and manage both types of threats with ocean-based and area-based approaches; MPAs are part of the area-based approaches.

While MPAs might be effective for the protection of marine migratory species, the current network of MPAs does not cover enough key areas used by these species(5), and, being highly mobile, they often move beyond the stationary boundaries of protected areas(9).

MPAs are static tools generally established as permanent closures. They are placed in (some) key areas for marine migratory species. Still, the evaluation of MPAs' importance doesn’t, or rarely does, take into account the fluctuations of the conditions within the borders. This means that a protected area might be effective in summer, but not in winter.

The inclusion of the physical and biological fluctuations in MPA designing becomes even more problematic if anthropogenic impacts are incorporated. Climate change and overfishing, just to name a few, are among the main drivers of ecosystem conditions. For example, some protected areas are established in feeding grounds but, with the changing temperatures, prey distribution might change(10). Similarly, fisheries may result in prey being too limited to meet the needs of the entire predator population. To follow the food source or use an alternative one, species might change their behaviors and movements(11), finding themselves outside the borders of protected areas and ultimately decreasing their effectiveness.

The limitations of MPAs become even clearer when evaluating their effectiveness. Protected areas are often designed for conservation reasons other than migratory species(5) and, therefore, their protection is rarely included in the evaluation of the MPA effectiveness. Few studies assessed the relevance of protected areas for migratory species, and mixed results were obtained(5), and it is even harder to find studies that report the changing effectiveness of MPAs for migratory species in the different seasons (if seasonality was included in the MPA design, to begin with).

These considerations suggest that the implementation of protected areas should consider both the location of key areas for migratory species and when they are used, what environmental features explain the presence of the species, what threats the migratory species might face, and possible solutions(5).

To establish effective protected areas and define conservation priorities, it is fundamental to combine the knowledge of a species’ ecology (habitats, behaviours, scales of movement) with ocean features and its persistence over time(9).

Most of this information can be derived from modelling. To know more about the variables involved in MPA modelling check my other blog post:

Why do MPAs need species distribution modelling based on wildlife monitoring?

Models allow us to understand the relationships between environmental conditions and the presence/absence of the species, and to easily track changes over time.

How can we change our approach to conservation?

The complicated relationship between marine migratory species and protected areas suggests the application of two different approaches:

• The creation of an effective MPAs network, and
• A dynamic approach to MPAs design

A network is a collection of protected areas that “cooperates” to meet the conservation objective(s) that a single MPA cannot meet(9). Protected movement corridors can also be established between MPAs when the migratory species show fidelity also for movement routes(9). On the other hand, mobile MPAs have boundaries that shift at spatial and temporal scales that coincide with dynamic biological and oceanic processes(12).

For both solutions, tracking the migratory species is a fundamental step. Complementing these data with environmental variables (for example, satellite data about temperature and chlorophyll concentration) give a complete overview of species distribution and physiological needs inside and outside protected areas, and for how long the MPAs meet their protection purpose.

The information can be used to better evaluate MPAs' efficacy, and identify new key areas to either temporarily expand pre-existent protected areas (dynamic approach), or to create new ones (network approach).

An example of such an application is the implementation of “Wildlife Tracker v0.3” for the “Galapagos Whale Shark Project”. Aimed at supporting the management of the Galapagos Marine Reserve and the protection of endangered marine species and pristine ecosystems, it shows how environmental data can be correlated with tracking data of an individual — of a young whale shark, named Sky, in this example.

The following two images map the Sea Surface Temperature (SST) and, on the top right, the border of the Galapagos Marine Reserve. The data of the first picture refer to March 2022, while the second to November 2022. The track covers a 5 months period between September 2021 and February 2022.

Image by GIS4 Wildlife. Sky the whale shark. Galapagos Whale Shark Project. March 2022/November 2022

It is also possible to customize the map with different ocean monitoring data, for example with Chlorophyll-a concentration (a good proxy for phytoplankton and zooplankton productivity), according to the specific conservation objectives of the protected area. The following example shows the same track of Sky in relation to Chl-a concentration in the same area and months as the previous example.

Image by GIS4 Wildlife. Sky the whale shark. Galapagos Whale Shark Project. Chl-a March 2022/November 2022 (the empty area has no significant Chl-a concentration, values close to 0)

By determining what percentage of the whale shark track falls within the borders of the protected area, we can also expand, in each specific season, the borders of the MPA to cover the areas where the whale sharks prefer to visit. For example, when the phytoplankton starts blooming, the interested area might be added for protection while whale sharks are foraging. Additionally, the zones with warmer water can be monitored as a (potential) protected area based on whale sharks' known habitat and environmental preferences derived from such analyses. Therefore, we can sum up season-specific “conservation functions” unique for each species.

You can read more on how eco-geographical variables influence the movements of whale sharks here:

The influence of eco-geographical variables on Whale Shark Movements

Another potential use of the model is the inclusion of human activities, such as fishing and shipping routes. This would, in turn, assist in the seasonal zoning of protected areas. Zoning, a key management tool for multi-purpose MPAs, allows definite regions to be “set aside” for specific activities, such as habitat protection, fishing, and tourism(13). Since every zone has differing restrictions, if correctly implemented, zoning might prove another useful tool to protect migratory species from detrimental activities.

“The goal is to protect the migration pathway of marine wildlife straighforwarly in the relevant season”

Given the worrying conservation status of marine migratory species, they should be considered a key indicator of the effectiveness of protected areas and therefore become an integral part of the management and design process of new policies and MPAs that are driven by data and influenced by the activities of coastal communities.

Image credit: Wildlife Tracker. Based on Arturo Rivera from Unsplash

References

1. Barry, J.P., & Dayton, P.K. (1991). Physical Heterogeneity and the Organization of Marine Communities. In: Kolasa, J., Pickett, S.T.A. (eds) Ecological Heterogeneity. Ecological Studies, vol 86. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3062-5_14
2. Lambert, C., Laran, S., David, L., Dorémus, G., Pettex, E., Van Canneyt, O., & Ridoux, V. (2017). How does ocean seasonality drive habitat preferences of highly mobile top predators? Part I: the north-western Mediterranean Sea. Deep Sea Research Part II: Topical Studies in Oceanography, 141, 115–132. https://doi.org/10.1016/j.dsr2.2016.06.012
3. Gill, A. E., & Niller, P. P. (1973). The theory of the seasonal variability in the ocean. In Deep Sea Research and Oceanographic Abstracts (Vol. 20, №2, pp. 141–177). Elsevier. https://doi.org/10.1016/0011-7471(73)90049-1
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6. Greenwood, P. J. (1980). Mating systems, philopatry and dispersal in birds and mammals. Animal behaviour, 28(4), 1140–1162. https://doi.org/10.1016/S0003-3472(80)80103-5
7. Brown, D. M., Robbins, J., Sieswerda, P. L., Ackerman, C., Aschettino, J. M., Barco, S., … & Wiedenmann, J. (2022). Site fidelity, population identity and demographic characteristics of humpback whales in the New York Bight apex. Journal of the Marine Biological Association of the United Kingdom, 102(1–2), 157–165. doi:10.1017/S0025315422000388
8. Art I, 1, a: Convention on the conservation of migratory species of wild animals
9. Gilmour, M. E., Adams, J., Block, B. A., Caselle, J. E., Friedlander, A. M., Game, E. T., … & Wegmann, A. (2022). Evaluation of MPA designs that protect highly mobile megafauna now and under climate change scenarios. Global Ecology and Conservation, 35. https://doi.org/10.1016/j.gecco.2022.e02070
10. Meyer-Gutbrod, E. L., Greene, C. H., & Davies, K. T. (2018). Marine species range shifts necessitate advanced policy planning: the case of the North Atlantic right whale. Oceanography, 31(2), 19–23. https://doi.org/10.5670/oceanog.2018.209.
11. Cohen, L. A., Pichegru, L., Grémillet, D., Coetzee, J., Upfold, L., & Ryan, P. G. (2014). Changes in prey availability impact the foraging behaviour and fitness of Cape gannets over a decade. Marine Ecology Progress Series, 505, 281–293. https://doi.org/10.3354/meps10762
12. Maxwell, S. M., Hazen, E. L., Lewison, R. L., Dunn, D. C., Bailey, H., Bograd, S. J., … & Crowder, L. B. (2015). Dynamic ocean management: Defining and conceptualizing real-time management of the ocean. Marine Policy, 58, 42–50. https://doi.org/10.1016/j.marpol.2015.03.014
13. Marine Mammals Management Toolkit, Zoning and Permitting