Biology subjects

Nunes, M. H.

Publications and source records attributed to Nunes, M. H..

3 recordsLinked to original sources

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↗

bioRxiv2021

Forest structure and degradation drive canopy gap sizes across the Brazilian Amazon

Canopy gaps are openings in the forest canopy resulting from branch fall and tree mortality events. Light reaches the lower layers of the canopy through these gaps, enabling understory trees to grow and maintaining the high heterogeneity and biodiversity of tropical forests. The size-frequency distribution of canopy gaps follows a power-law distribution, and the slope of this power-law () is a key indicator of forest structure and dynamics. We detected canopy gaps using a unique LiDAR data set consisting of 650 transects randomly distributed across the Brazilian Amazon Forest providing an unprecedented perspective on forest structural variation over 2500 km2 of forest. We then investigated how varied with forest structure, elevation, soil fertility, water deficit, wind gust speed and lightning intensity. We found that human modified forests had more large gaps than intact forests. Within the intact forests we observed a large-scale Northwest to Southeast pattern in (more large gaps in the Southeast), which aligns with recent work on tree mortality rates. The two most important variables in predicting were median canopy height and maximum modeled height, which had opposite effects on the number of large gaps. Forests with higher median canopy height contain fewer large gaps but the presence of very tall trees was associated with more large gaps. Environmental variables were of secondary importance in our model, with larger gaps occurring in drier forests with high soil fertility, wind speed, and lightning intensity. The distribution of large gaps in the forest canopy varies substantially over the Brazilian Amazon as a result of canopy structure and mortality rates. We mapped this variation and found more large gaps in human modified forests, forests on fertile soils and those exposed to higher wind, lightning and drought stress. Increasing extreme weather events due to climate change may therefore increase the number of large gaps in currently intact forests, causing them to resemble human modified forests.

ecology↗

bioRxiv2020

Resource availability and availability and disturbance shape maximum tree height across the Amazon

The factors shaping the distribution of giant tropical trees are poorly understood, despite its importance as a link between evolutionary biology and ecosystem biogeochemistry. The recent discovery of clusters of trees over 80 metres tall in the Guiana Shield region of the Amazon rainforest challenges the current understanding of the factors controlling the growth and survival of giant trees. The new discovery led us to revisit the question: what determines the distribution of the tallest trees of the Amazon? Here, we used high-resolution airborne LiDAR (Light Detection and Ranging) surveys to measure canopy height across 282,750 ha of primary old-growth and secondary forests throughout the entire Brazilian Amazon to investigate the relationship between the occurrence of giant trees and the environmental factors that influence their growth and survival. Our results suggest that the factors controlling where trees grow extremely tall are distinct from those controlling their longevity. Trees grow taller in areas with high soil clay content (> 42%), lower radiation (< 130 clear days per year) and wind speeds, avoiding alluvial areas (elevations higher than 40 m a.s.l), and with an optimal precipitation range of 1,500 to 2,500 mm yr-1. We then used an envelope model to determine the environmental conditions that support the very tallest trees (i.e. over 70 m height). We found that, as opposed to the myriad of interacting factors that control the maximum height at a large scale, wind speed had by far the largest influence on the distribution of these sentinel trees, and explained 67% of the probability of finding trees over 70 m in the Brazilian Amazon forest. The high-resolution pan-Amazon LiDAR data showed that environmental variables that drive growth in height are fundamentally different from environmental variables that support their survival. While precipitation and temperature seem to have lower importance for their survival than expected from previous studies, changes in wind and radiation regimes could reshape our forested biomes. This should be carefully considered by policy-makers when identifying important hotspots for the conservation of biodiversity in the Amazon.

ecology↗