RIL and co-management with indigenous communities benefit wildlife in tropical forests

Overhunting is degrading the ecological integrity of tropical forests across the globe. I’m pleased to be a co-author on a new paper led by Anand Roopsind, that points to solutions for this bushmeat crisis. Our study quantifies the impacts of reduced-impact logging and indigenous hunting on wildlife occupancy in the Iwokrama rainforest. We found evidence suggesting that wildlife populations were persisting in Iwokrama, a multiple-use forest with some logging and hunting. Iwokrama is fairly unique as a site that  (1) is certified as being responsibly managed for timber production and (2) legally guarantees indigenous communities hunting rights in the forest, while prohibiting hunting by outsiders. Our results provide hope that well-managed logging and hunting could support local livelihoods and conserve biodiversity.

Long time series of satellite data predict soil carbon in pasture

The publicly-available, multi-decadal Landsat archive is an amazing resource. In our new paper, led by Chris Wilson, we show that a 28-year record of Landsat-derived greenness can predict soil carbon in Florida cattle pasture. This work opens up many interesting ecological questions about how vegetation dynamics lead to soil carbon sequestration, in addition to potential applications for spatially-targeted land management.

Check it out:

Wilson, C.C., T.T. Caughlin, S.W. Rifai, E,H. Boughton, M.C. Mack, L.S. Flory. Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland. Ecological Applications. In Press.


Our new paper made the cover in Ecological Applications!


Our study made the cover of Ecological Applications! The image shows tree crowns and landscape features from a hyperspectral aerial image in Panama’s Azuero peninsula.

In the new study, we show how to predict tropical tree growth rates from aerial flyover data. This is like a blood test for trees–a single data-rich measurement that provides insight into future health and performance. Our hope is that this research opens the door for “precision forestry,” including spatially-targeted interventions to promote reforestation. Lots of work went into the paper, from collecting field data in Panama to months of crunching numbers on the computer. So I’m happy to see it out!

A new model to scale up forest restoration from sites to landscapes

Restoring forest to hundreds of millions of hectares of degraded land has become a centerpiece of international plans to sequester carbon and conserve biodiversity. To achieve these ambitious restoration goals, we will need to predict restoration outcomes at landscape and regional scales. However, ecological field studies reveal widely divergent forest recovery rates, challenging our ability to predict reforestation across sites. Mathematical models present an opportunity to synthesize results from different studies for a general understanding of reforestation dynamics. In our new study, we develop a theoretical framework to ask how tree canopy closure, a critical turning point for secondary forest succession, depends on landscape features beyond the scale of most field sites. Our modeling framework predicts the dynamics of reforestation using parameters that are commonly estimated in field studies. You can explore our basic model using the sliders below:

The simple version of our model (above) shows how different survival, growth and seed arrival rates can lead to differences in tree canopy closure within a single site. The model can also be applied to ask how landscape features impact reforestation rate across multiple sites. In particular, seed rain into deforested sites is critical for forest recovery and depends on landscape features that are difficult to measure or replicate. For example, fruit bats, an important seed disperser in degraded habitats of Southeast Asia, can range over tens of kilometers in a single night—an area far larger than most field plots.

Field studies reveal seemingly-contradictory results on the importance of seed rain for reforestation. Some studies find that adding seeds to degraded sites increases seedling abundance, whereas others do not. Similarly, some studies find that landscape features that increase seed availability (such as amount of surrounding forest cover) increase woody stem diversity, whereas others do find these relationships. Our results explain why field studies in sites with different landscape configurations could find different effects of seed limitation on reforestation rate. We predict that in landscapes with either very low seed availability, such as abandoned sugar cane plantations, or very high seed availability, such as primary forest, landscape-scale features will have a minimal impact on reforestation rate. In landscapes with intermediate seed availability, where there is enough seed rain to initiate reforestation yet not enough to guarantee rapid canopy closure, we show that seed availability can have significant impacts on reforestation rate. These results demonstrate how landscape features can lead to divergent forest recovery between otherwise similar patches.

Our new paper illustrates how mathematical models can provide conceptual insight into scaling up tropical forest restoration from sites to landscapes. More realistic models will be required to guide the spatial planning of specific forest landscape restoration projects. Adding realistic but complicating factors to theoretical models, such as fire disturbance and tree species interactions, is an exciting research frontier for restoration ecology. We anticipate that future modeling efforts will require collaboration between field ecologists and model developers to navigate between model realism and mathematical complexity. Ultimately, translating quantitative forecasts into spatially-targeted interventions for forest landscape restoration could help achieve ambitious goals of restoring hundreds of millions of hectares of tropical forest to degraded land.

Image credits: Landscape photo by Trevor Caughlin. Yep, the equation in the background is the primary model in the new paper!

Caughlin, T. T., S. Elliott, and J. W. Lichstein. In Press. When does seed limitation matter for scaling up reforestation from patches to landscapes? Ecological Applications. DOI: doi:10.1002/eap.1410

New paper: Loss of animal seed dispersal increases extinction risk in a tropical tree species

collecting civet seeds off a log

Happy to announce a new publication:

Caughlin T.T., Ferguson J.M., Zuidema, P.A., Levey, D.J., Bunyavejchewin S.,Lichstein J.W. Loss of animal seed dispersal increases extinction risk in a tropical tree species due to pervasive negative density dependence across life stages.In press, Proceedings of the Royal Society B: Biological Sciences

Overhunting directly threatens mammals in tropical forests worldwide and could indirectly threaten trees with seeds dispersed by mammals. Without seed dispersal, seeds remain crowded beneath the parent tree. Using field data and simulation models, we investigated the long-term effects of seed dispersal for a tree species in Thailand. We found negative effects of crowding for growth and survival across the entire tree life cycle, from seeds to adults. Loss of mammalian seed dispersal increased crowding and raised the risk of tree extinction by ten-fold. Our findings suggest that overhunting could lead to cascading extinctions in tropical forests.

This paper is a result of my dissertation research, including a ton of field work (the above photo is me excitedly poking through civet poop during the first year of the project) and a ton of computer programming. So I’m glad to see it finally out in print.




New postdoctoral fellowship!

Excited to announce that I have received an NSF postdoctoral fellowship to research landowner decision-making and landscape-level reforestation under the Science, Engineering and Education for Sustainability (SEES) Fellows program. During the grant, I will be based at the UF School of Forest Resources and Conservation, with Stephanie Bohlman as research mentor, and Dan Brown (U Michigan) as co-mentor.