Biology subjects

Guzman Q., J. A.

Publications and source records attributed to Guzman Q., J. A..

3 recordsLinked to original sources

bioRxiv2024

Seasonal structural stability promoted by forest diversity and composition explains overyielding

The stability of forest productivity over time is a widely studied parameter often associated with benefits of forest diversity. Yet, the structural stability (SS) through the season of forest communities and its relationship to diversity, composition, and productivity remains poorly understood. Using a large-scale (10 ha) young tree diversity experiment, we evaluated how forest structure and multiple dimensions of diversity and composition affect remotely-sensed structural metrics and their stability through the growing season. We then studied the impact of SS across the season (April-October) on the net biodiversity effects of annual wood productivity (i.e., overyielding) of forest mixtures. We surveyed experimental tree communities eight times at regular intervals from before bud-break to after leaf senescence, using an UAV-LiDAR to derive metrics associated with canopy height heterogeneity, gap probability, and forest structural complexity (i.e., fractal geometry). The inverse coefficient of variation of these metrics through the season was used as descriptors SS. These metrics along with their SS were then coupled with annual tree inventories to evaluate their relationships. Our findings indicate that plot wood volume and, to some extent, multiple dimensions of diversity and composition (i.e., taxonomic, phylogenetic, and functional) influence remotely-sensed metrics of forest structure and stability over time. We found that increases in plot wood volume as well as functional and phylogenetic diversity and variability (a measure of diversity independent of species richness), are linked to higher structural stability of forest complexity over time. We further found that higher stability of forest structural complexity and tree cover (i.e., 1 - gap probability) increases net biodiversity effects in forest mixtures through species complementarity. Structural equation models indicate that structural stability explains more the variation among plots in net biodiversity effects than multiple dimensions of diversity or variability, highlighting it as a measure that integrates several contributors to net biodiversity effects. Our results provide evidence that diversity and composition promote temporal stability of remotely-sensed forest structure and, in turn, enhanced productivity. The study highlights the potential to integrate remote sensing and ecology to disentangle the role of forest structural stability into ecological processes.

ecology↗

bioRxiv2023

Mapping oak wilt disease using phenological observations from space

Protecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt --a disease caused by a pathogenic fungus (Bretziella fagacearum)-- is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3,872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80-82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can predict healthily (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders in locating disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and their importance for monitoring changes in biodiversity at large scales.

ecology↗

bioRxiv2021

Why can we detect lianas from space?

Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing offers a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global specificity of spectral signals from liana-infested tree crowns and forest stands, examining the underlying mechanisms. We compiled a database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signature at canopy and stand scales across sites. This signature mainly arises at the canopy level due to direct effects of leaf angles, resulting in a larger apparent leaf area, and indirect effects from increased light scattering in the NIR and SWIR regions, linked to lianas less costly leaf construction compared to trees. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits identified are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life-history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas adaptive similarities across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. Open data statementOf the 17 datasets used, 10 are published and publicly accessible, with links provided in this submission (Appendix S1: Section S1). Upon acceptance, remaining seven datasets will be provided via Smithsonians Dspace. The open-source model code is available as R-package ccrtm (https://cran.r-project.org/web/packages/ccrtm/index.html) and on github (https://github.com/MarcoDVisser/ccrtm). Code will be archived in Zenodo should the manuscript be accepted for publication

plant biology↗