shubhi sharma

This project aims to investigate the effect of climate and forest structure on forest recruitment using Forest Inventory Analysis (FIA) data. The aims of this analysis are (1) to answer the question of whether there have been observed changes in forest recruitment in the last 40 years, (2) build a model by empirically fitting data using climate and forest structure covariates in order to understand the drivers of change in recruitment (3) to predict change in recruitment under climate change scenarios and finally, to establish the result of a change in recruitment on a forest population.

(Code & paper not available yet)

The loss of bi-modality in self-organizing landscapes indicates a loosening of positive and negative feedback loops that govern the formation and regulation of landscape and are ultimately indicative of losing resilience. However, bi-modality detection is contingent on sample size and effect size. To understand the link between successful bi-model detection rate and required statistical power, we computed Monte Carlo approximations of successful proportion under different scenarios of sampling intensity, spatial autocorrelation, sampling error and effect size.

(Code & paper not available yet)

Entity resolution or record linkage is the process by which databases with incomplete data records are probabilistically clustered where the same records in multiple databases are clustered together. This record linkage model is a joint random partition model with a downstream task of time series regression on records in a way that allows exact error propagation of the record linkage uncertainty into the downstream task. The downstream task may be generic.

(Code not available yet)

Using a species distribution model, we jointly model the 100 most abundant species in North America using climate and abiotic covariates to predict the habitat suitability under climate change scenarios RCP 4.5 and 8.5 for the time periods 2049-2069 and 2079-2099. In addition to predicting individual tree species, we clustered tree species into communities to project community shifts under climate change scenarios.

Website: https://pbgjam.env.duke.edu/products

Code :https://github.com/shubhi124081/FIA-Modeling

With the Nicholas Institute for Policy Solutions, I built two theoretical Ecosystem Service Models for oyster bay restoration projects in North Carolina and mangrove restoration projects in Florida. Apart from conducting workshops and taking multi-stakeholder input, we did extensive literature reviews of the existing research landscape for each "node" in the ecosystem service models. To explore the models and/or go through evidence libraries, click below!

Website: https://nicholasinstitute.duke.edu/sites/default/files/gems/Evidence_library_061219.pdf