Fragmentation disrupts the seasonality of Amazonian evergreen forests

TL;DR: The plant-climate interactions controlling the seasonality of wet Amazonian forests are demonstrated and it is shown that forest fragmentation will aggravate forest loss under a hotter and drier future scenario.

Abstract: Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial, which limits our understanding of future ecosystem function with a changing environment. Here, we use biweekly terrestrial LiDAR surveys spanning wet and dry seasons in Central Amazonia to show that plant phenology of old-growth forests varies strongly across strata but that this seasonality is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we found that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests shed their leaves and branches. By contrast, the understory greens-up with increased light availability driven by the upper canopy loss alongside more sunlight radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures on forest edges exacerbated the upper canopy losses of large trees throughout the dry season, and the understory seasonality in these light-rich environments was disrupted as a result of the altered canopy structure. These findings demonstrate the plant-climate interactions controlling the seasonality of wet Amazonian forests and show that forest fragmentation will aggravate forest loss under a hotter and drier future scenario.

Summary

INTRODUCTION

• Leaf phenology of Amazonian forests is a key component controlling the exchange of energy and trace gaseswater vapour, carbon dioxide and volatile organic compounds -with influences on vegetation feedbacks on the regional and global climates [1] [2] [3] [4] [5] .
• In the past decade, several studies have demonstrated from field data and remote sensing products that a majority of Amazonian forests respond to climatic variations 2, 6 .
• Forest fragmentation can increase the evaporative demand due to higher temperatures and wind exposure, and soil moisture can be lower at fragment edges 27 , which may cause leaves to drop and lead to higher branch turnover 12, 23 .
• Indeed, large uncertainty remains regarding the responses of fragmented forests to climatic seasonality, particularly because some species can benefit from higher solar radiation 29, 30 , drought resistance varies among species [31] [32] [33] and surviving trees may acclimate or be adapted to the drier, hotter conditions near edges 34 .
• The authors use TLS measurements to investigate how forest fragmentation and microclimatic seasonality interact to affect plant area of the understory and the upper canopy.

Seasonal climatic trends in Central Amazonian forests

• Daily precipitation estimates indicate the occurrence of a 4-month period of accumulated rainfall below 200 mm month -1 , and significant reductions in soil moisture between July and September in Central Amazonia (hereinafter referred to as "dry season").
• This dry season was coincident with a period of high Photosynthetic Active Radiation and significant increases in temperature of the understory in the forest interior and fragment edges .
• Forest fragmentation led to higher temperatures in the edges, while water availability in the soil remained virtually unaffected by edge effects.
• Mean daily soil moisture (as volumetric water soil content: cm 3 water/ cm 3 soil) measured in the edges (red) and in the interior of fragments (dark grey).
• Polynomial models of order 5 of the microclimate variables were generated as a smoothing line for visualization purposes.

Seasonal PAI variation and fragmentation effects

• The repeated high-resolution terrestrial LiDAR time-series revealed a strong vertical variability in the timing and magnitude of seasonal changes in the Plant Area Index (PAI) of old-growth forests and forests under edge effects.
• The PAI time-series of forest edges showed significantly distinct patterns in comparison with those observed in forests distant from edges (as the time x edge interaction term improved the model based on AIC; Supplementary Table 1 ).
• Each point represents the mean value predicted by mixed modelling, with the error bars depicting the bootstrapped 95% confidence intervals.
• Dry season is defined when the accumulated monthly rainfall is below 200 mm month -1 with significant reductions in soil moisture.
• The understory of interior forests had sharp decreases in PAI between April and June , a period when soil moisture was still high, and maximum temperatures were relatively low .

DISCUSSION

• Repeat high-density terrestrial LiDAR combined with microclimate measurements in Amazonian forests provided a unique perspective on the seasonal dynamics of vegetation and the interaction with fragmentation.
• Plant area index, as a combination of leaf area index (LAI) and the area of woody components including trunks and branches, showed inverse patterns in the understory versus upper canopy.
• Conversely, the upper canopy (> 15 m aboveground) of these forests maintain their canopy structure throughout most of the dry season, with the greatest losses (8%) in upper canopy PAI occurring from September to mid-October when the microclimate of these forests reaches the lowest soil moisture and the maximum temperatures above 35 °C.
• Edge effects, however, disrupted the seasonal trends in understory plant area and exacerbated upper canopy loss throughout the dry season.
• The study demonstrates the value of repeated terrestrial LiDAR surveys, which allow the detection of fine-scale changes in forests without potential artefacts of passive remote sensing studies 42 and provide a perspective on forest dynamics and its spatial variability that is diff icult to achieve with lower resolution remote sensing approaches.

Seasonal variation in plant area of intact Amazonian forests

• Repeat terrestrial LiDAR measurements of PAI made every 15 days in Central Amazonian forests provide observations of the balance between new leaf development (flush of new leaves, plant growth) and loss to abscission (leaf and branch fall) that could be separated across forest strata.
• The authors PAI time-series indicated that in old-growth forests distant from edges, higher loss in the upper canopy occurs with elevated temperatures, whereas the understory maintains high leaf production under high light availability mediated by the upper canopy dynamics, even during periods of drier soil and atmospheric conditions.
• These findings suggest that if differences between strata are not considered alongside changes in LAI, litterfall production and leaf demography, predictions of the climatic influences on vegetation may be undermined or misleading.
• Recent studies in Amazonian forests have shown that leaf area increases in the understory occur under maximal irradiance conditions when the upper canopy layer is partially deciduous during the dry season 38, 39 , as diffuse and direct solar radiation in the understory can increase linearly with decreasing upper canopy plant area 56 .
• In contrast, canopy trees exhibit lower embolism resistance, high stomatal sensitivity and significant declines in photosynthesis during periods of high atmospheric demand and low soil water availability 69 .

Forest fragmentation disrupts seasonal patterns of plant area

• The authors observed strong edge effects on phenology.
• These higher temperatures may lead to an increase in vapour pressure deficit (VPD), inducing stomatal closure and leaf loss 23, 69, 78, 79 , as shedding leaves may help to avoid the desiccating effects of water and heat stress 80 .
• The aseasonality of plant area in the understory of edges indicates that leaf production rates were similar to leaf loss rates during wet and dry seasons.
• The dry season losses in upper canopy leaf area-where large trees dominate-that the authors observed in fragment edges may supress total CO2 uptake, with possible negative consequences for tree growth, and lower investment in tissue maintenance and defence 82 .

METHODS

• The study was conducted in Central Amazonian forests (2°20 30 ′S, 60° 05 37 W) within the Biological Dynamics of Forest Fragments Project , the world's longest-running experimental study of habitat fragmentation 44 .
• The region has seen notable carbon and biodiversity losses due to forest fragmentation effects 25, 45 and is predicted to be markedly impacted by climatic changes 46 .
• The pioneering BDFFP project sites are composed of forest fragments originally isolated in 1980 by converting mature forest into cattle pastures.
• Currently, the matrix is dominated by secondary growth forests, but a 100 m strip surrounding the forest fragments is regularly cleaned by cutting vegetation regrowth to keep the forest fragments isolated .
• The authors selected a 100-ha forest fragment to investigate phenological responses with varying distances from the fragment edges (0 -500 m).

Terrestrial Laser Scanning: data acquisition, registration and Plant Area Index estimation

• The TLS data were acquired using a RIEGL VZ-400i system between April and October 2019 every Given that the RIEGL VZ-400i has a zenith angle range of 30-130°, an additional scan was acquired at each sampling location with the scanner tilted at 90° from the vertical position.
• Using an inverse distance weighting algorithm in the function grid_terrain in lidR in the software R, a common DTM was constructed from LiDAR ground returns.
• The volume occupied by vegetation within each transect was divided into 1 m 3 voxels, and the PAD calculated for each of these voxels .
• The effective sampling area of each laser pulse (or fraction of pulse in case of multiple hits) is computed from the theoretical beam section (a function of distance to laser and divergence of laser beam) and the remaining beam fraction entering a voxel.

Determining edge effects and number of forest strata

• To test the hypotheses that first fragmentation has significant effects on the structure of the vegetation in the BDFFP experimentfollowing Almeida and colleagues 53 and second that edge effects also impact phenology, the authors related Plant Area Index (PAI) with edge distance in a nonlinear mixed model.
• A hockey-stick model consisting of two linear segments was also implemented with the R package hockeystick.
• This model identified a "distance from edge" threshold, dividing voxels into edge and interior groups (Supplementary Methods 2).
• Species, functional and phylogenetic composition of the understory are distinct from the upper canopy in Central Amazonian forests 32, 37 .
• The authors then calculated the changes in PAD during the dry season to investigate shifts in the vertical profile of vegetation to elucidate the seasonal responses of specific strata (Supplementary Fig. 3a, 3b ).

Climatic variables to elucidate the timing in PAI seasonal changes

• PAI changes may be controlled by micro and macroclimatic conditions and changes 19, 38, 39 .
• The authors demonstrate below how they estimated solar radiation and accumulated rainfall at the landscape level, and continuously measured air temperature and soil moisture in the understory of forest edges and interior of forest fragments to examine the synchrony between these factors and the PAI time-series in the understory and canopy.

Solar radiation and accumulated rainfall

• PAR varies significantly within forest canopies and changes over time due to variations in the incident solar flux density and solar direction 56 .
• Incident solar PAR contains two components: direct PAR and diffuse PARand the latter is mostly controlled by scattering of particles and cloud cover in the atmosphere 57 .

Microclimate variables

• Soil moisture and maximum temperatures are key drivers of species' distributions and affect how species respond to climatic variations 62, 63 .
• The authors measured air temperature (°C) and electrical conductivity of soil moisture (time-domain transmission; TDT) across a network of 22 data loggers varying in distance from the forest fragment margins (0 and 520 m).
• Data loggers were shielded from direct solar radiation and recorded data every 15 minutes.
• Microclimate data were recorded between 27 th April 2019 and 16 th October 2019, resulting in a total of 435798 coupled temperature and volumetric soil moisture readings.
• TMS device measures microclimate variables affecting many ecological processes, including those related to water and energy balance.

Phenology modelling for interior and edge forests

• The authors used a linear mixed-effects (LME) model of understory PAI, upper canopy PAI and a combination of both strata (total PAI) measured from TLS in each transect as a function of time of measurement (time).
• The time × edge effects interaction represents how edge effects caused by forest fragmentation influence the seasonal variation in PAI.
• Χ²-tests and P values were performed by comparing to random-intercept models of the form PAI~ 1 + (1| Transect) and model explanatory power was assessed in terms of AIC.
• The LME model was fitted using the lme function in the nlme R package.
• Variations in transect area and monitoring period can influence PAI trends, and the authors used varIdent weights function to account for the noise attributed to sampling effort 66 .

Full text

Fragmentation disrupts the seasonality of
Amazonian evergreen forests
Matheus Nunes (  matheus.nunes@helsinki. )
University of Helsinki
José Luís Camargo
Biological Dynamics of Forest Fragment Project (INPA & STRI) https://orcid.org/0000-0003-0370-9878
Grégoire Vincent
CIRAD
Kim Calders
Ghent University https://orcid.org/0000-0002-4562-2538
Rafael Oliveira
University of Campinas
Alfredo Huete
School of Life Sciences, University of Technology Sydney, NSW 2007
Yhasmin Moura
Karlsruhe Institute of Technology
Bruce Nelson
Brazil's National Institute for Amazon Research (INPA)
Marielle Smith
Michigan State University
Scott Stark
Michigan State University
Eduardo Maeda
University of Helsinki https://orcid.org/0000-0001-7932-1824
Biological Sciences - Article
Keywords: leaf phenology, forests, Amazonian forests, forest fragmentation and loss
Posted Date: July 29th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-722038/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
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Version of Record: A version of this preprint was published at Nature Communications on February 17th,
2022. See the published version at
https://doi.org/10.1038/s41467-022-28490-7.

Fragmentation disrupts the seasonality of Amazonian evergreen forests 1
2
Authors 3
Matheus H. Nunes
1, 11
, José Luis C. Camargo
2
, Grégoire Vincent
3
, Kim Calders
4
, Rafael S. Oliveira 4
5
, Alfredo Huete
6
, Yhasmin Mendes de Moura
7, 8
, Bruce Nelson
9
, Marielle N. Smith
10
, Scott C. 5
Stark
10
, Eduardo E. Maeda
1
6
7
1
Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland 8
2
Biological Dynamics of Forest Fragment Project, National Institute for Amazonian Research, 9
Manaus, AM, 69067-375 Brazil 10
3
AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France 11
4
CAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Faculty 12
of Bioscience Engineering, Ghent University, Ghent, Belgium 13
5
Department of Plant Biology, Institute of Biology, University of Campinas, Campinas, Brazil 14
6
School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 15
2007, Australia 16
7
Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 17
76131, Karlsruhe, Germany 18
8
Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, 19
University of Leicester, Leicester, LE17RH, United Kingdom 20
9
National Institute of Amazonian Research, Manaus, Brazil 21
10
Department of Forestry, Michigan State University, East Lansing, MI, USA 22
11
Corresponding author (matheus.nunes@helsinki.fi) 23
24

ABSTRACT 25
Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly 26
controversial, which limits our understanding of future ecosystem function with a changing 27
environment. Here, we use biweekly terrestrial LiDAR surveys spanning wet and dry seasons in 28
Central Amazonia to show that plant phenology of old-growth forests varies strongly across strata 29
but that this seasonality is sensitive to disturbances arising from forest fragmentation. In combination 30
with continuous microclimate measurements, we found that when maximum daily temperatures 31
reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests 32
shed their leaves and branches. By contrast, the understory greens-up with increased light availability 33
driven by the upper canopy loss alongside more sunlight radiation, even during periods of drier soil 34
and atmospheric conditions. However, persistently high temperatures on forest edges exacerbated the 35
upper canopy losses of large trees throughout the dry season, and the understory seasonality in these 36
light-rich environments was disrupted as a result of the altered canopy structure. These findings 37
demonstrate the plant-climate interactions controlling the seasonality of wet Amazonian forests and 38
show that forest fragmentation will aggravate forest loss under a hotter and drier future scenario. 39
40
INTRODUCTION 41
Leaf phenology of Amazonian forests is a key component controlling the exchange of energy and 42
trace gases – water vapour, carbon dioxide and volatile organic compounds - with influences on 43
vegetation feedbacks on the regional and global climates
1–5
. In the past decade, several studies have
44
demonstrated from field data and remote sensing products that a majority of Amazonian forests 45
respond to climatic variations
2,6
. There is also mounting evidence that evergreen canopies have a
46
seasonal variation
7–11
with changes in leaf demography and canopy structure
12
. Long-term studies
47
have shown that 60 - 70% of species of humid Amazonian forests flush new leaves in the dry months 48
12,13
linked to higher solar radiation
4,14
, which leads to increases in gross primary productivity as a
49
result of new young leaves with higher photosynthetic capacity and water-use efficiency
4,15,16
.
50
However, when some Amazonian forests are impacted by water stress, leaf development is reduced 51
17
, and trees shed their leaves, increasing litterfall
10,18
, which interact to alter leaf area dynamics
19
.
52
To complicate matters further, leaf phenology also responds to different gene expressions that have 53
evolved to maximize photosynthetic and water use efficiency during the dry season, reduce plant 54
competition for light and water, and minimise herbivore pressure
7,16,20–22
.
55

The effects of climatic variations on leaf phenology can also be amplified by forest fragmentation
23
. 56
Forest edges contain a large abundance of early successional species with rapid acquisition of 57
resources that maximise new leaf production and growth
24,25
, but may be more vulnerable to droughts
58
26
. Forest fragmentation can increase the evaporative demand due to higher temperatures and wind
59
exposure, and soil moisture can be lower at fragment edges
27
, which may cause leaves to drop and
60
lead to higher branch turnover
12,23
. However, ground observations of litterfall in Amazonian forests
61
have shown only a mild seasonality near edges
28
. Indeed, large uncertainty remains regarding the 62
responses of fragmented forests to climatic seasonality, particularly because some species can benefit 63
from higher solar radiation
29,30
, drought resistance varies among species
31–33
and surviving trees may 64
acclimate or be adapted to the drier, hotter conditions near edges
34
. As the number of contiguously
65
forested areas are significantly decreasing in the Amazon
35
, understanding the effects of forest 66
fragmentation on phenology is vital for predicting the benefits of protecting non-fragmented 67
Amazonian forest landscapes. 68
Seasonal variations in leaf quantity and leaf area across evergreen Amazonian forests have frequently 69
been considered negligible or small
4,12,21,36
. However, spaceborne remote sensing approaches tend to 70
detect only trees that dominate the upper canopy, thereby obtaining more information from those 71
species that are adapted to more stressful conditions such as high solar radiation and temperatures 72
and low air humidity
37
. LiDAR-based observations may provide fresh insights into the interacting 73
factors controlling vegetation dynamics and have more recently shown that leaf phenology in 74
Amazonian forests is stratified over canopy positions and conditions
19,38
. Here, we investigate the
75
phenology of forests in Central Amazonia with terrestrial laser scanning (TLS, also terrestrial 76
LiDAR) surveys collected every 15 days spanning the wet and dry seasons. We use TLS 77
measurements to investigate how forest fragmentation and microclimatic seasonality interact to affect 78
plant area of the understory and the upper canopy. Repeated TLS measurements can monitor subtle 79
changes in forest structure in specific horizontal layers
42
(Figure 1). Furthermore, the detailed and
80
precise structural measurements offered by this system can help answer fundamental questions about 81
the three-dimensional (3D) ecology of trees
43
without suffering from potentially confounding 82
artefacts present in passive optical satellite images
11,36
. Using a combination of 11 repeat TLS
83
surveys, as well as continuous air temperature and soil moisture measurements in old-growth, 84
undisturbed forests and fragmented forests under edge effects, we test: (1) whether vertically stratified 85
plant phenology in undisturbed forests varies with microclimatic conditions, and (2) whether plant 86
phenology is sensitive to disturbances arising from forest fragmentation. We predict that the hotter 87
and drier conditions of edges exacerbate leaf loss during the dry season. To our knowledge, the work 88