Finally, we connect landscape and habitat heterogeneity by introducing spatial correlation between nearby habitats, using models of aquatic and terrestrial landscapes. Correlations increase the total population and strengthen the buffering effect due to the spatial clustering of niches

8/9

Then, we add landscape heterogeneity by introducing complex dispersal networks! We find that the fraction of coexisting species increases with habitat heterogeneity, and at the same time landscape heterogeneity allows for more species to coexist while increasing the total population

6/9

We first study a "mean-field case" where all habitat patches are connected to focus on the effect of habitat heterogeneity alone. If it's large enough, coexistence of all species becomes possible thanks to the emergence of ecological niches in different habitats - species start to "localize"

5/9

To quantify habitat heterogeneity, we measure the balance between colonization and extinction via a local “landscape-mediated fitness”: species with large colonization and low extinction rates in a patch have high fitness, and vice-versa. This is akin to the classical metapopulation capacity

3/9

Without heterogeneity, when all habitats are equivalent, we find that the only possible solution is monodominance - the fittest species overtakes the entire ecosystem, regardless of the underlying dispersal network! But what happens if not all habitats are equally good for different species?

2/9

We start by generalizing the seminal metapopulation model by Hanski and Ovaskainen. Species explore and settle on a complex landscape described by a network of habitat patches. Each habitat has a finite amount of colonizable space, generating an effective competition term between species

1/9

Take-at-home message: we study a general model of spatially extended ecosystems where species compete for spaces in different habitat patches. We find that spatial heterogeneity is the key mechanism allowing the coexistence of a large number of species, and fosters the emergence of ecological niches

And finally a bonus for reading the whole thread: another cool video of metapop dynamics using a DEM from the Himalayas!

A cool application is elevation-based networks obtained from Digital Elevation Maps (DEMs). By imposing that exploring uphill is harder than downward one, our model captures the dendritic features of complex topographic landscapes

9/9

We can study network fragmentation, showing that our model perfectly captures segregation in modular networks and the detrimental effects of landscape fragmentation. Further, we can prove analytically that survival is generally favored in more dense network

7/9

And of course, network topology influences everything - in particular, the survival of the population, quantified by the metapopulation capacity. We find that multiple short paths and hubs in general favor survival and strong localization of the surviving individuals

6/9

The topological contribution of the underlying networks is naturally encoded in the metapopulation kernel, depending on the species' dispersal. Our model can reproduce classical exponential distance-based kernels, but can capture much more complex network structures

5/9

With the core assumption that network exploration is faster than the local dynamics, we obtain an effective fully-connected model for the local population - the metapopulation - which is identical in form to classical metapop models such as the Hanski and Ovaskainen's one

3/9

We attempt to answer this question with ecology in mind. We start from a microscopic model of metapopulation dynamics and make the distinction between
👉 a local settled population on nodes
👉 explorers moving around and colonizing empty sites

2/9

Our model can be applied to any network, including elevation-based ones obtained from DEMs, allowing us to study ecological patterns in real-world scenarios (and make cool videos). Keep reading for all the details!