DLNMs

Precipitation and habitat fragmentation impact population dynamics of a tropical understory plant

Selected References: Scott, E.R., Uriarte, M., Bruna, E.M., 2022. Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments. Global Change Biology 28, 463–479.

Delayed effects and habitat fragmentation: are we underestimating the effects of anthropogenic change?

Anthropogenic climate change is causing an increased frequency and severity of extreme precipitation events (i.e. droughts and unusually wet conditions). At the same time, human development and land conversion interrupts and fragments previously …

Habitat-dependent delayed effects of climate on demographic vital rates in a fragmented Amazonian landscape

**Background** Deforestation is a major threat to species biodiversity in the Amazon rainforest. Deforestation results in loss of habitat, but also often leaves remaining forest habitat highly fragmented, with remnants of different sizes embedded in …

Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments

Deforestation often results in landscapes where remaining forest habitat is highly fragmented, with remnants of different sizes embedded in an often highly contrasting matrix. Local extinction of species from individual fragments is common, but the …

Differing non‐linear, lagged effects of temperature and precipitation on an insect herbivore and its host plant

1. Multivariate climate change is expected to impact insect densities and plant growth in complex, and potentially different, ways. Tea (Camellia sinensis) is a unique crop system where the increase in quality from chemical defences induced by …

Fitting a DLNM is simple without the {dlnm} package

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. Since I’ve been working so much with GAMs for this project, I decided to read sections of Simon Wood’s book, Generalized additive models: an introduction with R more thoroughly.

DLNMs: hypothesis tests and p-values

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. A major goal of my postdoc project is to determine whether drought has an effect on plant vital rates (growth, survival, reproduction, recruitment).

Speeding up DLNMs with bam()

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. DLNMs themselves may not be that computationally expensive, but when combined with random effects and other smoothers, and a large-ish dataset, I’ve noticed gam() being painfully slow.

DLNM marginal basis functions

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. Choosing marginal function to construct a crossbasis According to Gasparrini et al.

Distributed lag non-linear models

Welcome / Disclaimer This is the beginning of a series of blog posts where I publicly stumble my way through figuring out some confusing, complicated, and, frankly, cutting-edge modeling and statistics.