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Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances...
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The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays...
Citations
... The situation is even worse in South America, which recorded the second largest rate of net forest loss during the last decade (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020), losing annually an area about the size of Ruanda (i.e., 2.6 million ha per year) (FAO 2020). Within this region, the expansion of agriculture and the intensification of livestock farming at the expense of less intensive land uses (i.e., compatible with forest cover), converted many tropical and subtropical forests into deforestation hotspots (Hansen et al. 2013;De Marzo et al. 2022). This land use change caused the displacement of low-intensity activities, such as livestock raising and artisanal wood harvesting, to areas with lower productivity occupied by subtropical dry forests, leading to their degradation by overgrazing and firewood extraction (Hasnat and Hossain 2020;De Marzo et al. 2022). ...
... Within this region, the expansion of agriculture and the intensification of livestock farming at the expense of less intensive land uses (i.e., compatible with forest cover), converted many tropical and subtropical forests into deforestation hotspots (Hansen et al. 2013;De Marzo et al. 2022). This land use change caused the displacement of low-intensity activities, such as livestock raising and artisanal wood harvesting, to areas with lower productivity occupied by subtropical dry forests, leading to their degradation by overgrazing and firewood extraction (Hasnat and Hossain 2020;De Marzo et al. 2022). In this context, it is essential to assess restoration strategies of subtropical dry forests degraded by livestock-forestry overuse aimed at recovering their ability to provide environmental goods and services. ...
Livestock raising provides a livelihood for millions of people who inhabit forests worldwide. However, browsing and trampling can disrupt the regeneration of tree species, compromising the persistence of native forests in the long term. Therefore, in this study, we assess low‐cost restoration practices compatible with livestock production. Specifically, we tested the effect of thorny branch protection on the survival and growth of natural regeneration and nursery‐grown saplings of Lithraea molleoides . Additionally, we evaluated the effect of hydrogel application on protected and unprotected L. molleoides nursery‐grown saplings. Finally, we compared the survival and growth of protected and unprotected natural regeneration versus nursery‐grown saplings. In three fields under different grazing pressures, we marked 105 L. molleoides seedlings < 40 cm height (we protected 69, whereas 36 remained unprotected). Also, we transplanted 120 saplings > 70 cm height that were randomly assigned to four treatments (protected‐with‐ and without‐hydrogel; unprotected‐with‐ and without‐hydrogel). The protection with thorny branches facilitated the growth of natural regeneration and nursery‐grown saplings. However, the efficiency of this practice depended on the grazing pressure, being more effective in the field with lower grazing pressure. Hydrogel addition did not affect nursery‐grown saplings survival or growth, suggesting that in our study system the main filter to L. molleoides regeneration is cattle browsing and trampling. Finally, protecting naturally recruited individuals was more effective than protecting nursery‐grown saplings. The practice assessed in this study allows for combining restoring and producing activities rather than separating them, thereby adapting to the management objectives of land owners and incorporating human livelihood needs in restoration plans.
... Our analyses revealed that both agricultural intensification and wildlife conservation affected the relative contribution of disturbance agents to woody plant damage (Figure 1b-d). This finding underscores that land use changes strongly alter the disturbance regime acting on woody vegetation, even in inherently disturbance-prone ecosystems (De Marzo et al. 2022;Mograbi et al. 2017;Ouédraogo et al. 2015). ...
Uncertainties in carbon storage estimates for disturbance-prone dryland ecosystems hinder accurate assessments of their contribution to the global carbon budget. This study examines the effects of land-use change on carbon storage in an African savanna landscape, focusing on two major land-use change pathways: agricultural intensification and wildlife conservation, both of which alter disturbance regimes. By adapting tree inventory and soil sampling methods for dryland conditions, we quantified aboveground and belowground carbon in woody vegetation (AGC and BGC) and soil organic carbon (SOC) across these pathways in two vegetation types (scrub savanna and woodland savanna). We used Generalized Additive Mixed Models to assess the effects of multiple environmental drivers on AGC and whole-ecosystem carbon storage (Ctotal). Our findings revealed a pronounced variation in the vulnerability of carbon reservoirs to disturbance, depending on land-use change pathway and vegetation type. In scrub savanna vegetation, shrub AGC emerged as the most vulnerable carbon reservoir, declining on average by 56% along the conservation pathway and 90% along the intensification pathway compared to low-disturbance sites. In woodland savanna, tree AGC was most affected, decreasing on average by 95% along the intensification pathway. Unexpectedly, SOC stocks were often higher at greater disturbance levels, particularly under agricultural intensification, likely due to the preferential conversion of naturally carbon-richer soils for agriculture and the redistribution of AGC to SOC through megaherbivore browsing. Strong unimodal relationships between disturbance agents, such as megaherbivore browsing and woodcutting, and both AGC and Ctotal suggest that intermediate disturbance levels can enhance ecosystem-level carbon storage in disturbance-prone dryland ecosystems. These findings underline the importance of locally tailored management strategies–such as in carbon certification schemes–that reconcile disturbance regimes in drylands with carbon sequestration goals. Moreover, potential trade-offs between land-use objectives and carbon storage goals must be considered.
... The extent of anthropogenic alteration and degradation of natural forests in many tropical and subtropical ecosystems is one of the main causes of biodiversity widely recognized for its ability to assess the fraction of photosynthetically active radiation absorbed by vegetation [42][43][44]. Recently, however, advancements in satellite data and processing power have enabled the exploration of additional spectral indices with enhanced spatial and temporal resolution and faster processing speeds [18,45]. These new tools provide opportunities for detailed phenological analyses and the development of novel indicators for sustainable management specifically tailored to the Chaco region. ...
Anthropogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN’s 15th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancaní Provincial Reserve and surrounding areas of the West Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWIGao (Normalized Difference Water Index), and MCARISent (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.
... The focus has also been on specific drivers such as fire [80], shifting cultivation [11], cocoa [38], mining [44], soybeans [71], logging [34], and rubber [82]. Other studies have focused on the sub-national or country level [62,67,14,36,53,32,21,51,47]. Some of the most novel advancements have been in near real time classification of drivers to enable rapid interventions by governments and forest defenders [70]. ...
... Some of the most novel advancements have been in near real time classification of drivers to enable rapid interventions by governments and forest defenders [70]. Many of these studies have used machine learning models, most commonly random forest models [32,11,82,71,67,14,36,53,86,51,47]; however, deep learning approaches are increasingly being leveraged [59,62,49,38,70]. ...
Forests are in decline worldwide due to human activities such as agricultural expansion, urbanization, and mineral extraction. Forest loss due to generally temporary causes, such as wildfire and forest management, is important to distinguish from permanent land use conversion due to the differing ecological and climate impacts of these disturbances and for the purposes of developing effective policies and management strategies. Existing global maps of the drivers of forest loss that are widely used are not spatially or thematically detailed enough for decision makers at local-to-regional scales, such as governments, land managers, or companies. Using publicly available satellite observations (Landsat, Sentinel) and ancillary biophysical and population data, we developed a 1 km resolution, global map of the dominant drivers of forest loss from 2001 to 2022 with seven classes: permanent agriculture (e.g., commodity crops or pasture), hard commodities (e.g., mining), shifting cultivation, forest management (e.g., logging or wood fiber plantations), settlements and infrastructure, wildfire, and other natural disturbances. We interpreted nearly 7,000 reference samples to train a global neural network model that classifies the driver of forest loss with an overall accuracy of 90.5%. Our results show that permanent agriculture was the leading driver of forest loss globally, representing 35% of loss from 2001 to 2022. The drivers of forest loss vary by region, with the leading driver identified as forest management in Europe, permanent agriculture across the tropics, and wildfire in Russia, the Asian mainland, North America, and Oceania. Our results enable assessment of forest disturbance dynamics from local to global scales and can support tracking progress towards corporate and governmental zero-deforestation commitments, monitoring deforestation risks within jurisdictions and supply chains, and assessment of global biodiversity targets.
... (Lavilla et al., 2004) and P. cuqui (IUCN SSC Amphibian Specialist Group, 2022). Their environmental niches overlap, mainly, with the Chaco region, exposed not only to anthropic environmental simplification but also to the cultural degradation of their traditional communities (De Marzo et al., 2022). For both species, we corroborated the existence of synonymy and possible taxonomic identification errors. ...
... The predictions obtained for the current scenario allow us to identify environmental niche regions not only for these species but also for other species of the family (Medina et al., 2020;Soberon, 2007). These results facilitate the objective planning of future samplings and allow for defining management and mitigation actions in regions exposed to intensified anthropic actions in agricultural deforestation and mining exploitation (De Marzo et al., 2022). Consistent with our predictions, the family Leptodactylidae has been reported from the southern tip of Texas (United States), Southern Sonora (Mexico), and the northern Antilles to southern regions of Brazil, Argentina, and Chile (Frost, 2022;Medina et al., 2020). ...
... Global warming and variations in rainfall regimes and hydrological networks limit the reproductive behaviors and oviposition of different species of anurans (Blaustein et al., 2010;Bruzzi-Lion et al., 2019;Clavel et al., 2011;Martins, 1988;Pounds et al., 2006). This added to the regionally intensified land use, could modify the spatial and demographic patterns of currently stable populations (De Marzo et al., 2022). The reproductive modes of anurans could emerge as determinants of their reproductive success; for most species, reproduction occurs during the warm season, coinciding with the rainy season (Exbrayat, 2018). ...
Introduction:
Leptodactylus latinasus and Physalaemus cuqui are sympatric anuran species with similar environmental requirements and contrasting reproductive modes. Climatic configuration determines distribution patterns and promotes sympatry of environmental niches, but specificity/selectivity determines the success of reproductive modes. Species distribution models (SDM) are a valuable tool to predict spatio-temporal distributions based on the extrapolation of environmental predictors.
Objectives:
To determine the spatio-temporal distribution of environmental niches and assess whether the protected areas of the World Database of Protected Areas (WDPA) allow the conservation of these species in the current scenario and future.
Methods:
We applied different algorithms to predict the distribution and spatio-temporal overlap of environmental niches of L. latinasus and P. cuqui within South America in the last glacial maximum (LGM), middle-Holocene, current and future scenarios. We assess the conservation status of both species with the WDPA conservation units.
Results:
All applied algorithms showed high performance for both species (TSS = 0.87, AUC = 0.95). The L. latinasus predictions showed wide environmental niches from LGM to the current scenario (49 % stable niches, 37 % gained niches, and 13 % lost niches), suggesting historical fidelity to stable climatic-environmental regions. In the current-future transition, L. latinasus would increase the number of stable (70 %) and lost (20 %) niches, suggesting fidelity to lowland regions and a possible trend toward microendemism. P. cuqui loses environmental niches from the LGM to the current scenario (25 %) and in the current-future transition (63 %), increasing the environmental sympathy between both species; 31 % spatial overlap in the current scenario and 70 % in the future.
Conclusion:
Extreme drought events and rainfall variations, derived from climate change, suggest the loss of environmental niches for these species that are not currently threatened but are not adequately protected by conservation units. The loss of environmental niches increases spatial sympatry which represents a new challenge for anurans and the conservation of their populations.
... For example, carbon in aboveground vegetation in Dry Chaco woodlands ranged from 1 to >100 tC/ha (Pötzschner et al., 2022), or the soil organic carbon ranged from 39 to 125 tC/ha in the Cerrado woodlands and savannas (Dionizio et al., 2020). Likewise, there is a large heterogeneity of traditional land-use practices taking place in dry woodlands, from slash-and-burn and subsistence agriculture, to livestock rearing, to charcoal and fuelwood production, which modify natural vegetation to varying extents (De Marzo et al., 2022;Ryan et al., 2016). Recently, intensified commodity agriculture, including cropping (e.g., soybean and maize), tree crops (e.g., rubber, coffee) and intensified cattle ranching have all been expanding into dry woodlands, often leading to the loss of large shares of natural vegetation and thus larger amounts of GHG emissions (Pendrill et al., 2019). ...
... Despite the vast area occupied and its great ecological importance, very little is known about fire dynamics within this biome, specifically in countries such as Paraguay, Bolivia and Brazil (Baumann et al., 2016). Worryingly, land use changes are increasingly promoted by increased agricultural and livestock production and the associated high deforestation rates at regional level (Hansen et al., 2013;De Marzo et al., 2022), threatening to fragment the largest block of tropical dry forest in South America. ...
... The countries across which the Gran Chaco extends are considered developing countries and, not surprisingly, the regions most affected by changes produced in the fire regimes are those with high levels of poverty, rurality and poor governance of natural resource management (Pivello et al., 2021). Fire is a frequent disturbance across the Gran Chaco, affecting different ecosystems including grasslands, savannas and forests (Bucher 1982;Bravo et al., 2001Bravo et al., , 2010Landi et al., 2021;De Marzo et al;. However, considering the complex interactions with environmental factors, human land transformations and dominant vegetation, fire may have variable effects within the same ecosystem (Nogueira et al., 2017;Giorgis et al., 2021). ...
Fire is a natural element of some tropical dry ecosystems. However, during the last decades, fire occurrence has become more frequent and intense due, in part, to climate change and land use transformation. This is the case in the Gran Chaco Americano, one of the largest dry forests all over the world that extends across Argentina, Paraguay, Bolivia and Brazil. Fire has shaped the Gran Chaco landscape since ancient times, but today, as in many other regions, the pattern, frequency, severity and intensity are being dramatically altered. Based on information collected mainly over the last two decades, this paper presents a detailed review of the available literature on the fire regime across the Gran Chaco region. Here, we present a multi- disciplinary understanding considering fire behavior and dynamics in the study ecosystem within a very specific ecological, administrative and historical framework.
A noteworthy aspect of this review indicates the clear imbalance between regions in terms of available literature; while information about the Argentine Chaco is abundant, the literature for the Paraguayan or Bolivian Chaco is practically non-existent. The rainfall gradient and drought periods are key climatic drivers of fire ignitions while cattle ranching is the main socioeconomic activity of this region and key precursor of forest fires. In general, a substantial part of the available information focused on ecological aspects of the fire regime as the effect of fires on plant functional traits such as bark thickness, resprouting ability and flammability patterns. Other post fire effects on soil, invasive species, herbivory and soil seed banks have been also explored in detail to understand ecosystem recovery and research needs. We finally highlight current necessities and future prospects, mainly related to soil burn severity (SBS), invasive species and wildlife impact. Although our study specifically focused on changes in the fire regime of the Gran Chaco, some generalities were further discussed about fire regimes that could be relevant for diverse fire-sensitive ecosystems in the tropics
... The Chaco is a fire-exposed region with a long history of periodic fires (Kunst and Bravo 2003;Argañaraz et al. 2015a, b;Jaureguiberry et al. 2020;Bravo et al. 2021). Commonly, the fires start in managed or unmanaged grasslands, savannas, or croplands, eventually spreading into neighbor forests and shrublands with significant effects on biodiversity and ecosystem services (Bravo et al. 2010;Tálamo et al. 2013;Loto and Bravo 2020;Giorgis et al. 2021;De Marzo et al. 2022). Like other regions, some of these fires are natural or accidental, but most are anthropic and intentional, used for deforestation through slash-and-burn practices or as an agricultural management tool to clear vegetation or to promote crops and pastures (Gürtler 2009;Gasparri and Baldi 2013;Baumann et al. 2018;De Marzo et al. 2023). ...
... Focusing on the Argentine Dry Chaco, De Marzo et al. (2021Marzo et al. ( , 2022Marzo et al. ( , 2023 used Landsat imagery to study the causes and consequences of forest disturbances between 1990 and 2017. They found that post-disturbance trajectories depend on the disturbance agent, and anthropic fires stand out as a major one with strong negative impacts on woody vegetation and its recovery. ...
... Compared to tropical rainforests, tropical and subtropical dry forests as the Chaco have been much less studied, but they are recently receiving more attention as important reservoirs of native forests and carbon stocks De Marzo et al. 2022). The severe fires that affected the Chaco in 2020 represented a wake-up call for the scientific community to encourage and develop research on the environmental situation of this important region (Bonfanti and Sánchez 2021;Naval Fernández et al. 2023). ...
Background
Wildfires represent an important element in the bio-geophysical cycles of various ecosystems across the globe and are particularly related to land transformation in tropical and subtropical regions. In this study, we analyzed the links between fires, land use (LU), and meteorological variables in the South American Chaco (1.1 million km ² ), a global deforestation hotspot and fire-exposed region that has recently attracted greater attention as the largest and one of the last tropical dry forests in the world.
Results
We found that the Dry Chaco (73% of the total area of Chaco) exhibits a unimodal fire seasonality (winter-spring), and the Wet Chaco (the remaining 23%) displays a bimodal seasonality (summer-autumn and winter-spring). While most of the burnt area (BA) was found in the Wet Chaco (113,859 km ² ; 55% of the entire BA), the Dry Chaco showed the largest fraction of forest loss (93,261 km ² ; 88% of the entire forest loss). Between 2001 and 2019, 26% of the entire Chaco’s forest loss occurred in areas with BA detections, and this percentage varies regionally and across countries, revealing potential connections to LU and policy. Argentina lost 51,409 km ² of its Chaco tree cover, surpassing the forest losses of Paraguay and Bolivia, and 40% of this loss was related to fire detections. The effect of meteorological fluctuations on fuel production and flammability varies with land cover (LC), which emerged as the principal factor behind BA. While wet areas covered with herbaceous vegetation showed negative correlations between BA and precipitation, some dry regions below 800 mm/year, and mostly covered by shrublands, showed positive correlations. These results reveal the two different roles of precipitation in (a) moisture content and flammability and (b) production of biomass fuel.
Conclusions
As fires and deforestation keep expanding in the South American Chaco, our study represents a step forward to understanding their drivers and effects. BA is dependent on LC types, which explains the discrepancies in fire frequency and seasonality between the Wet and Dry Chaco subregions. The links between fires and deforestation also vary between regions and between countries, exposing the role of anthropic forcing, land management, and policy. To better understand the interactions between these drivers, further studies at regional scale combining environmental sciences with social sciences are needed. Such research should help policy makers take action to preserve and protect the remaining forests and wetlands of the Chaco.
... Largescale retrospective insights into drivers have been gained in samplebased studies (De Sy et al., 2019;Laso Bayas et al., 2022;Tyukavina et al., 2018) or classifications on a coarse grid cell level (Curtis et al., 2018). More spatially explicit classifications methods have also been studied, with annual driver classifications at the disturbance pixel-or patch-level, in temperate and boreal forests (Hermosilla et al., 2015;Huo et al., 2019;Nguyen et al., 2018;Oeser et al., 2017;Schroeder et al., 2011Schroeder et al., , 2017Sebald et al., 2021;Senf and Seidl, 2021;Stewart et al., 2009;Vogeler et al., 2020;Zhang et al., 2022) and in tropical (dry) forests (De Marzo et al., 2022;Shimizu et al., 2019). In order to obtain spatially explicit outputs, these studies relied predominantly on medium spatial resolution (30 m) Landsat data and used a variety of rule-based and machine learning methods. ...
... Our findings confirm the strong capacities of applying a CNN to high spatiotemporal resolution satellite data for rapid classification of newly detected disturbance patches as smallholder agriculture, road development, selective logging, mining or other, with a Macro-F1 score of 0.861 and an OA of 0.897. Previous studies have applied various approaches for spatially explicit forest disturbance driver classifications, of which some also focused on tropical (dry) forests (De Marzo et al., 2022;Irvin et al., 2020;Masolele et al., 2021Masolele et al., , 2022Shimizu et al., 2019). However, our study is the first to demonstrate near real-time monitoring methods specifically for small-scale disturbance drivers in the tropics. ...
... (UA = User's Accuracy, PA = Producer's Accuracy, OA = Overall Accuracy). two classes into one overarching logging class, which was also done in other driver classification studies in the tropics (De Marzo et al., 2022;Shimizu et al., 2019). In these studies, based on Landsat data, logging still proved to be a problematic class to distinguish. ...
Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests. The produced forest disturbance driver maps can be accessed via: https://bartslagter94.users.earthengine.app/view/forest-disturbance-drivers.
... Forest disturbance detection itself is now fairly operational, thanks to open access to high-resolution satellite image archives extending back to the 1980 s, new algorithms, and ever-increasing cloud-processing capabilities (Banskota et al., 2014;Frazier et al., 2014;Pasquarella et al., 2022;Wulder et al., 2012;Zhu, 2017). Disturbances and disturbance agents can be robustly characterized thanks to temporal segmentation algorithms and machine-learning methods (De Marzo et al., 2022;Kennedy et al., 2015;Nguyen et al., 2018;Shimizu et al., 2017;Zhang et al., 2022). In addition, multi-sensor approaches allow to reliably characterize forest structural composition, including biomass or fractional woody cover (Baumann et al., 2018;Bourgoin et al., 2018;Pötzschner et al., 2022;Shao and Zhang, 2016). ...
... This diversity makes the Argentine Dry Chaco a very interesting case to assess forest disturbances and post-disturbance recovery. In previous work, we mapped forest disturbance extent, timing and agents for the time period 1990 to 2017 (De Marzo et al., 2022, fractional tree and shrub cover for the year 2015 (Baumann et al., 2018) and aboveground biomass for the year 2019 (Pötzschner et al., 2022) at high spatial and temporal resolution. Building on these datasets, we here use a Bayesian multilevel framework to understand how different disturbance types and histories relate to current forest structure. ...
... We used a comprehensive forest disturbance dataset, including the timing and agents of disturbance, for the Argentine Dry Chaco from our own previous work (De Marzo et al., 2022. Our maps, produced using Landsat TM/ETM + OLI time series, covered the period 1990 to 2017 at 30-m spatial resolution, providing detailed information about forest disturbances at annual temporal resolution. ...
Tropical dry forests are widespread, harbour vast amounts of carbon and unique biodiversity, and underpin the livelihoods of millions. A variety of natural and anthropogenic disturbances affect tropical dry forest canopy, yet our understanding of how these disturbances impact on forest structure and ecosystem functioning, and how forests develop after different disturbances, is partial. This translates into knowledge gaps regarding long-term
outcomes of disturbances on forest structure as well as which of these outcomes signify recovery vs forest degradation. Here, we use a rich dataset of remotely-sensed, high-resolution forest indicators in a multilevel Bayesian regression framework to understand the effect of different disturbance agents (partial clearing, fire, logging, drought and riparian changes) on aboveground biomass, and woody cover in the Argentine Dry Chaco. Our models show that post-disturbance trajectories of forest structural indicators differ markedly among different
disturbance agents. For example, riparian changes affected biomass most strongly but had the fastest recovery, whereas logging had a generally lower impact and mostly affected tree cover, but recovery was slow or never occurred. Importantly, even three decades after the disturbance event, woody cover and biomass exhibited higher values for natural disturbances compared to anthropogenic disturbances. Furthermore, anthropogenic disturbances had slower recovery rates than natural disturbances. Overall, our approach shows the potential of
remote-sensing indicators and space-for-time substitution to unravel the diverse vegetation response of different disturbance agents. Given the high and rising human pressure on dry forests in the Chaco and globally, our findings also show the long-lasting effects that anthropogenic disturbances have on these valuable forests.
