Research

1.    Dispersal ecology

I’m interested in a broad range of questions about the ecological, evolutionary, and conservation implications of larval dispersal in marine organisms. I’m particularly fascinated by (1) measuring dispersal over a range of scales, and (2) investigating how large-scale, complex dispersal patterns govern the ecology and bioeconomics of fisheries.

Measurement: Larval dispersal is difficult to observe and expensive to measure, and so we need analytic tools that can make sense of limited empirical data. I’ve spent the past couple of years developing statistical methods for fitting kernels to genetic parentage data, but doing so requires a large number of uncomfortable, simplifying assumptions: isotropy, spatial homogeneity, etc. My current goal is to fit biophysical dispersal models (i.e., models which incorporate both oceanography and biological components) to this parentage data. More: to discriminate between multiple competing larval dispersal models using limited genetic parentage assignments.

  • Bode M, Williamson D, Harrison H, Outram N, Jones G (2016). Estimating dispersal kernels using genetic parentage data. BioRxiv doi: https://doi.org/10.1101/044305.
  • Pinsky M, Saenz-Agudelo P, Salles O, Almany G, Bode M, Berumen M, Andréfouët S, Thorrold S, Jones G, Planes S (2017). Marine dispersal scales are congruent over evolutionary and ecological time. Current Biology 27: 1-6.
  • Almany GR, Hamilton RJ, Bode M, Matawai M, Potuku T, Saenz-Agudelo P, Planes A, Berumen ML, Rhodes KL, Thorrold SR, Russ GR, Jones GP. (2013). Dispersal of grouper larvae drives local resource sharing in a coral reef fishery. Current Biology 23: 1–5.

Implications: Larval dispersal is spatially complex and temporally variable, and it is therefore difficult to understand how it will affect the broader socio-ecological system. Using metapopulation and metacommunity models, we can get some idea of how regional substructure and asymmetry in dispersal patterns affects how populations fluctuate, and how they interact with each other.

  • Bode M, Bode L, Armsworth PR (2011). Different dispersal abilities allow reef fish to coexist. Proceedings of the National Academy of Sciences, USA, 108 (39): 16317-16321.
  • Bode M, Bode L, Armsworth PR (2006). Larval dispersal reveals regional sources and sinks in the Great Barrier Reef. Marine Ecology Progress Series, 308:17-25.

Larval dispersal also complicates the management of exploited marine species. Dispersal creates variable connections between disparate populations, making it difficult to allocate effort efficiently across space. It also links populations that fall within the management boundaries of different communities. This creates a classical economic externality, where one agent’s actions (e.g., over-harvesting) have unavoidable consequences for another agent. This exchange therefore has potential implications for whether different groups would be willing to engage in sustainable (or unsustainable) behaviour.

  • Bode M, Sanchirico J, Armsworth PR. (2016) Returns from aligning management resolution with ecological variation in a coral reef fishery. Proceedings of the Royal Society of London B: Biological Sciences 283: 20152828.

2. Conservation

We spend a lot of time in conservation science developing new optimisation methods and incorporating ever-more high resolution spatial data. At best, these new methods squeeze a few percentage points of efficiency out of our optimal solution. Instead, I think we should focus on more rich and realistic descriptions of the conservation system. Specifically, I’m interested in formulating and analysing theoretical conservation problems that explicitly consider (1) uncertainty, (2) dynamics, and (3) multiple and diverse conservation actors.

Uncertainty: How do we make efficient decisions when we know very little about many aspects of the conservation problem? What do the key stakeholders want to achieve? How effective are our proposed interventions, and how will the ecological system respond? How much funding do we have, and how long will it last for? Fundamental uncertainties make quantitative approaches to conservation science much harder, but also much more vital.

  • Bode M, Baker C, Plein M (2015). Eradicating down the food chain: optimal multispecies eradication schedules for a common invaded island ecosystem motif. Journal of Applied Ecology 52: 571-579.

Dynamics: Conservation systems are built over long periods of time, funds are metered out in yearly doses, and ecosystems change at multiple timescales. However, most of our quantitative methods imagine that conservation plans are rolled out overnight, and that the results are delivered with equal rapidity. How do we plan efficient actions in such a responsive system?

  • Wilson KA, McBride M, Bode M, Possingham HP (2006). Prioritising global conservation efforts. Nature, 440: 337-340.
  • Bode M et al. (2008). Cost-effective global conservation spending is robust to taxonomic group. Proceedings of the National Academy of Sciences, USA, 105 (17): 6498-6501

Multiple actors: Quantitative conservation science generally makes top-down, authoritarian assumptions. Conservation plans are implemented by a central manager, threatened species are managed by a single authority, protected areas are inviolable. In reality, the conservation ecosystem involves large numbers of independent and partially-independent actors. As well as independent non-conservation actors (e.g., land developers, mining companies), the conservation sector itself is diverse and independent. How does this reality affect conservation outcomes? How should planning proceed when there are multiple loci of action? How do we encourage cooperation between multiple conservation NGOs with divergent goals and separate funding streams?

  • Bode M, Probert W, Turner W, Wilson K, Venter O (2010). Conservation planning with multiple organizations and objectives. Conservation Biology, 25, 295-304.
  • Iacona G, Bode M, Armsworth P (2017). Limitations of outsourcing on-the-ground biodiversity conservation. Conservation Biology.
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