Statistical Inference on Tree Swallow Migrations, Using Random Forests

10/26/2017
by   Tim Coleman, et al.
0

Species migratory patterns have typically been studied through individual observations and historical records. In this work, we adopt a data driven approach to modelling the presence of the North American Tree Swallow, Tachycineta bicolor, throughout the Eastern United States, using data collected through the eBird project at Cornell University's Lab of Ornithology. Preliminary models suggest a qualitatively different pattern in Tree Swallow occurrence between the years of 2008 to 2009 and 2010 to 2013. We implement a global hypothesis test based on the functional predictions of Random Forests (RFs) to evaluate whether this effect is significant or not. In order to better understand the effect of climate change, we also conduct a test evaluating the effect of daily maximum temperature anomaly in predicting tree swallow occurrence. We implement a local test using the asymptotic normality of the predictions of a modified RF, which relies on subsampled trees. This test is conducted at 6 locations in space throughout the northeastern U.S. Finally, we present visual evidence that maximum temperature is affecting the predictions of RF models via a heat map of the differences in RF predictions. We also demonstrate that there is a spatial pattern in the effect using Moran's I statistic.

READ FULL TEXT

page 3

page 13

research
09/01/2020

LoRaWAN Temperature Sensors for Local Government Asset Management

The purpose of this project is to investigate the suitability of using L...
research
07/03/2018

Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC

In this paper, we perform Bayesian Inference to analyze spatial tree cou...
research
10/19/2021

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

In traditional extreme value analysis, the bulk of the data is ignored, ...
research
02/27/2023

Random forests for binary geospatial data

Binary geospatial data is commonly analyzed with generalized linear mixe...
research
10/15/2019

Breadth-first, Depth-next Training of Random Forests

In this paper we analyze, evaluate, and improve the performance of train...
research
11/20/2019

Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore

The rising air temperature caused by Urban Heat Island (UHI) effect has ...
research
10/09/2018

Data-driven competitive facilitative tree interactions and their implications on nature-based solutions

Spatio temporal data are more ubiquitous and richer than even before and...

Please sign up or login with your details

Forgot password? Click here to reset