in southeast asia many forests have been

However, the forest area of Southeast Asia will decrease quite dramatically over the next 12 to 15 years, while that of East Asia will increase even faster. Figure 8. Forest area 2020. Some of the forest areas classified as semi-natural forests have been established by planting of native species, while others have been established by Although deforestation and forest degradation have long been considered the most significant threats to tropical biodiversity, across Southeast Asia (Northeast India, Indochina, Sundaland, Philippines) substantial areas of natural habitat have few wild animals (>1 kg), bar a few hunting-tolerant … The tropical forests of Southeast Asia are under immense pressure. This tropical forest region has lost a large proportion of its original forest cover and is now a deforestation hotspot [7]. Over Cách Vay Tiền Trên Momo. Loading metrics Open Access Peer-reviewed Research Article Takuya Furukawa , Riyou Tsujino, Shumpei Kitamura, Takakazu Yumoto Factors affecting forest area change in Southeast Asia during 1980-2010 Nobuo Imai, Takuya Furukawa, Riyou Tsujino, Shumpei Kitamura, Takakazu Yumoto x Published May 15, 2018 Figures AbstractWhile many tropical countries are experiencing rapid deforestation, some have experienced forest transition FT from net deforestation to net reforestation. Numerous studies have identified causative factors of FT, among which forest scarcity has been considered as a prerequisite for FT. In fact, in SE Asia, the Philippines, Thailand and Viet Nam, which experienced FT since 1990, exhibited a lower remaining forest area 30±8% than the other five countries 68±6%, Cambodia, Indonesia, Laos, Malaysia, and Myanmar where forest loss continues. In this study, we examined 1 the factors associated with forest scarcity, 2 the proximate and/or underlying factors that have driven forest area change, and 3 whether causative factors changed across FT phases from deforestation to net forest gain during 1980–2010 in the eight SE Asian countries. We used production of wood, food, and export-oriented food commodities as proximate causes and demographic, social, economic and environmental factors, as well as land-use efficiency, and wood and food trade as underlying causes that affect forest area change. Remaining forest area in 1990 was negatively correlated with population density and potential land area of lowland forests, while positively correlated with per capita wood production. This implies that countries rich in accessible and productive forests, and higher population pressures are the ones that have experienced forest scarcity, and eventually FT. Food production and agricultural input were negatively and positively correlated, respectively, with forest area change during 1980–2009. This indicates that more food production drives deforestation, but higher efficiency of agriculture is correlated with forest gain. We also found a U-shaped response of forest area change to social openness, suggesting that forest gain can be achieved in both open and closed countries, but deforestation might be accelerated in countries undergoing societal transition. These results indicate the importance of environmental, agricultural and social variables on forest area dynamics, and have important implications for predicting future tropical forest change. Citation Imai N, Furukawa T, Tsujino R, Kitamura S, Yumoto T 2018 Factors affecting forest area change in Southeast Asia during 1980-2010. PLoS ONE 135 e0197391. Krishna Prasad Vadrevu, University of Maryland at College Park, UNITED STATESReceived January 5, 2018; Accepted May 1, 2018; Published May 15, 2018Copyright © 2018 Imai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are Availability All relevant data are within the paper and its Supporting Information This study was financially supported by the Environment Research and Technology Development Fund S9-1 of the Ministry of the Environment, Japan, and the “International Program of Collaborative Research” funded by the Center for Southeast Asian Studies CSEAS, Kyoto University to NI, and the "Project to support activities for promoting REDD+ by private companies and nongovernmental organizations" funded by the Forestry Agency of Japan to TF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the interests The authors have declared that no competing interests exist. IntroductionAlteration of land use is one of the major causes of global environmental change which is driving species to extinction and emitting increasing amount of green-house gases. In particular, global deforestation rate is still alarmingly high[1], and the tropics are the only biome to exhibit an increasing trend of forest cover loss in the 21st-century[2]. Deforestation and forest degradation in the tropics are responsible for 7–14% of anthropogenic carbon emissions[3] and pose one of the greatest threats to global biodiversity[4]. Therefore, reducing tropical deforestation and even reversing the trend to net forest gain are top priorities of global environmental policy. While many tropical countries are experiencing ongoing deforestation, some have gone through a transition from net deforestation to net reforestation, as known as “forest transition FT”[5]. The FT hypothesis explains forest recovery as a result of abandonment of marginal agricultural land followed by forest regeneration, as well as tree plantation[6][7][8]. Economic development is almost a prerequisite of FT[9][10][11][12][13][14][15], but different pathways have been suggested on how it affects forest recovery. The wealth brought by economic development would enable tropical countries to be financially comfortable enough to invest in reforestation schemes[16] or import wood and food products from other countries while preserving its own forest[13][14][17][18] [19][20]. Economic development may also change the demographic pattern of a country decrease in rural population with the increase in urban population through the increase in off-farm employment, which leads to cropland abandonment[5]. Improvement in agricultural productivity is also suggested to encourage abandonment of marginal croplands[21]. Although it may not be a direct result of economic development, democratic societies[22][23][24] or countries with better governance[15][25][26] are suggested to show less deforestation and/or more forest recovery. Despite the diversity of socio-economic factors that have been suggested to be related to FT, most studies have employed a limited number of factors in their analysis. Additionally, various environmental conditions, such as precipitation, temperature, vegetation, and topography, are known to affect forest area change at the local to subnational scales[27][28][29], but their effects have rarely been incorporated in national-scale studies. Exhaustion of forest resources is also considered as a prerequisite of a country to experience FT. When forests become scarce, the need for forest conservation is realized with rising price of forest products, or forest protection is promoted in order to restore the deteriorated forest ecosystem services[30][31][32]. Rudel et al. 2005[31] pointed out that this “forest scarcity pathway” could be more prominent in densely populated Asian countries, compared to less populous Latin American countries. In southeast Asia, forest area stopped to decline in Thailand and increased in the Philippines and Viet Nam since 1990, but the other five SE Asian countries experienced forest loss during 1980–2010 Fig 1[1][33]. The three FT countries Philippines, Thailand and Viet Nam exhibited lower remaining forest area 30±8%, mean±SD compared to the other five SE Asian countries 68±6%, Cambodia, Indonesia, Laos, Malaysia, and Myanmar as of 1990 Fig 1. This implies that forest scarcity per se may have led to FT in the three countries. Although the pattern and processes of FT in the three countries have been well studied[6][14] [34][35][36], clarifying why the three particular countries, but not the other five countries, have already exhausted their forest resources and experienced FT would lead to a better understanding of the entering point of the forest scarcity pathway. Grainger[7] suggested that, during the FT process, mechanisms underlying the deforestation phase and the subsequent reforestation phase are not identical. However, recent studies reported that factors associated with forest area change are consistent during both deforestation and reforestation phases, while relative importance of each factor varied among phases[15][37]. This implies that there might be a common mechanism across the FT phases, in which a socio-economic factor might initially accelerate deforestation, but then encourage reforestation. Such process could be a key to not only reduce deforestation but also enhance forest recovery. SE Asia used to experience the fastest rate of deforestation among the tropics especially until the 1990s[38]. Smallholders supported by recolonization programs by the state were considered the main driver of deforestation up to the 1980s, but their role was replaced by private enterprise agriculture until the 1990s[39]. Deforestation continued during the 1990s and 2000s in the region but with a slower rate because of reversing trends in forest area in Thailand, the Philippines, and Viet Nam Fig 1[1]. Displacement of deforestation to other countries through timber imports played a big role to achieve forest recovery in Viet Nam[34][35]. Expansion of oil-palm plantation has been one of the major causes of deforestation in Indonesia and Malaysia during this period[39][40]. In Myanmar, commercial agricultural concession, timber extraction and infrastructure development, underlain by international investment, civil war and weak land tenure, were identified as the major drivers of deforestation[41]. To elucidate the general process of FT in SE Asia, we employed 33 socio-economic factors pertaining to proximate production of wood, food, wood and food aggregated, and export-oriented food commodities and underlying causes demographic, social, economic and environmental factors, as well as land-use efficiency, and wood and food trade of deforestation in eight SE Asian countries at the national scale during 1980–2010. We also examined the relationship between percentage forest area and these causative factors in 1990 to understand the conditions leading to forest scarcity. We addressed three specific questions; 1 what are the socio-economic conditions that lead a country to enter the forest scarcity pathway, 2 which proximate and/or underlying factors have the most significant impacts on forest area change, and 3 whether the relationship with the identified causative factors change across the FT phases from deforestation to net forest gain? Methods Data collection This study covered eight southeast Asian countries, namely, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Thailand and Viet Nam, encompassing 3 decades 1980–2009 divided into four periods 1980–1989, 1990–1999, 2000–2004 and 2005–2009. All countries were analyzed together to extract common mechanisms underlying the entering point of the forest scarcity pathway and the process across the FT phases. Data on remaining forest area % and the rate of change in forest area % yr-1 were obtained from the Global Forest Resources Assessment FRA data on the 1980s were from FRA 1990[33] and data on the 1990s and onward were from FRA 2010[1]. FRA’s forest area data have been criticized for being variable in quality across countries and for inconsistencies in definitions[42], but it remains the sole comprehensive source of national deforestation rates prior to 2000 see Hansen et al. 2013[2]. We used data on wood and food production as the proximate causes of forest area change. Instead of using production volume, we converted the values into per capita land area required to produce the products km2 person-1 yr-1 in order to account for the difference in land use impacts difference in land area required to produce the same volume of different products. Details of the calculation process is provided in Kastner et al. 2014[43] and the Supporting Information S1 Text. This calculation enabled us to directly compare and aggregate the production impact of different wood and food products under the same unit. Wood production covered industrial roundwood including derived products. Food production encompassed almost 450 crops and livestock products, including ten major crops and two groups of commodity crops of interest, namely, oil palm and stimulants coffee and cocoa. As for the underlying driving forces of forest area change, we considered demographic, economic, social, and environmental variables, as well as land-use efficiency, and wood and food trade. Demographic variables included population density person km-2, rural, urban and total annual population growth rates % yr-1, and percentage of urban population Panels a-d in S7 Fig. Economic variables included GDP per capita PPP adjusted, current international USD, GDP growth rate % yr-1, level of industrialization represented by the share of manufacturing industry % of GDP, headcount poverty ratio at USD per day % of population, forest rents % of GDP, total natural resources rents % of GDP, proportion of forest rents to total natural resources rents %, and the Human Development Index HDI, unitless Panels e-k in S7 Fig. Social variables included corruption and social openness Panels l and m in S2 Fig. The Corruption Perception Index CPI provided by Transparency International was used to represent corruption. Indices of polity and freedom, obtained from Polity IV regime authority characteristics and transitions datasets, INSCR and Freedom in the world, Freedom House respectively, were summarized based on principal component analysis PCA, and the score of its first axis was used to represent social openness. Land-use efficiency included the index of agricultural input unitless, cereal yield Hg ha-1, and the index of agricultural yield unitless Panels n-p in S2 Fig. Agricultural input was represented by the first axis of PCA among agricultural machines import, pesticides import and fertilizers consumption per unit agricultural area. Similarly, the yield values of major crops were summarized by PCA to represent agricultural yield. The self-sufficiency ratios SSR, unitless for wood, food, and wood and food aggregated were used as the indices of wood and food trade. The SSR was defined as The SSR was calculated based on land area required for wood and food production S3 Fig, and area associated with import/export of wood and food in the eight countries S4 and S5 Figs. Data on import/export values of food were obtained from Kastner et al. 2014[43], while those of wood were calculated based on various data sources see S1 Text. Environmental variables included remaining forest area at the beginning of each period %, median elevation m, total land area km2, and percentage land area of lowland tropical forests as potential natural vegetation %. Characteristics of climate and soil summarized based on PCA analyses S1 Text were also used in the analyses. All variables used in the analyses are listed in Table 1. The data sources and details of the calculation processes are described in S1 Text. Statistical analyses For all the 33 variables of proximate and underlying causes Table 1, we calculated the mean values in each of the four periods 1980–1989, 1990–1999, 2000–2004 and 2005–2009. We first examined the relationships between percentage forest area in 1990, when forest area in Philippines and Viet Nam began to increase Fig 1, and the remaining 32 variables in the 1980s by Pearson’s correlation analysis. We then examined the relationships between the rate of change in forest area % yr-1 and the 33 variables. As a result, 10 out of 33 variables had a significant correlation with forest area change in at least one of the four periods S7 Fig and S3 Table; see Results. To further analyze the strength of each of the 10 causative factors on the rate of change in forest area during the four periods, we examined the explanatory power of major variables based on multiple regression analyses. We considered wood and food production individually instead of their aggregated values, and excluded headcount poverty ratio since it was not available for Myanmar. We also considered squared terms for variables that changed their correlation coefficient between positive and negative over time expecting that the variables might have altered their relationship with forest area during FT. The multi-collinearity of explanatory variables was examined based on variance inflation factor VIF. Variables having VIF ≥ 10 were dropped with preferential omission of squared terms to avoid severe multi-collinearity[44], leaving a total of 8 explanatory variables of which only one was a squared term. The full model with all explanatory variables was defined as where ΔFAi is the rate of change in forest area, FPi is per capita area required for food production, WPi per capita area required for wood production, POPi is population density, URBi is proportion of urban population, SOPi is social openness, AGIi is agricultural input, and WSSRi is wood SSR. β1~9 represent model coefficients intercept and slopes, εi is the error term, and i depicts data from each country and time period. Model selection was based on Akaike information criterion for small sample sizes AICc[45]. For each candidate model, we calculated AICc weight which value adds to 1 representing the normalized likelihood of a model in the set of candidate models[46]. The relative importance of variables IOV; values ranging from 0 to 1 was calculated by adding the AICc weights of the models in which a variable was selected [46]. All statistical analyses were performed using R[47]. 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Mangrove ecosystem services, such as erosion prevention, shoreline protection and mitigation of climate change through carbon sequestration, depend on the size and arrangement of forest patches, but we know little about broad-scale patterns of mangrove forest fragmentation. Here we conduct a multi-scale analysis using global estimates of mangrove density and regional drivers of mangrove deforestation to map relationships between habitat loss and fragmentation. Mangrove fragmentation was ubiquitous; however, there are geographic disparities between mangrove loss and fragmentation; some regions, like Cambodia and the southern Caribbean, had relatively little loss, but their forests have been extensively fragmented. In Southeast Asia, a global hotspot of mangrove loss, the conversion of forests to aquaculture and rice plantations were the biggest drivers of loss >50% and fragmentation. Surprisingly, conversion of forests to oil palm plantations, responsible for >15% of all deforestation in Southeast Asia, was only weakly correlated with mangrove fragmentation. Thus, the management of different deforestation drivers may increase or decrease fragmentation. Our findings suggest that large scale monitoring of mangrove forests should also consider fragmentation. This work highlights that regional priorities for conservation based on forest loss rates can overlook fragmentation and associated loss of ecosystem functionality. IntroductionMangroves are intertidal wetlands found along coastlines in much of the tropical, subtropical and warm-temperate world. These forests provide valuable ecosystem services including preventing erosion1, providing habitat for fisheries species2, protecting coastal communities from extreme weather events3,4 and storing large reserves of blue carbon, thus mitigating global climate change5. The services provided by mangroves are threatened by anthropogenic processes including deforestation6 and sea-level rise7,8. Historically, mangroves were subject to high rates of deforestation of up to per annum9. However, since the turn of the millennium global mangrove deforestation rates have slowed, with annual loss rates of Lower rates of loss are due to near total historical loss of forest patches in some regions, but also improved conservation practices11 and improvements in large scale monitoring techniques that provide more accurate estimates of cover and loss than were available historically10,12. The majority of contemporary mangrove loss occurs in Southeast Asia, where ~50% of the remaining global mangrove forest area is located, with nations such as Indonesia, Malaysia and Myanmar continuing to show losses of and per year, researchers have highlighted that simply reporting mangrove total loss rates is insufficient for prioritising conservation actions11, if there is insufficient knowledge of the quality and spatial arrangement of habitat that remains. It is important to consider the proportional loss of mangroves, as areas with small amounts of mangrove forest will be particularly negatively affected by deforestation and resulting fragmentation, even though such small patches can still provide a disproportionate amount of ecosystem services for local populations13. Similarly, in addition to simply conserving mangrove forests, there is now also a focus on quantifying mangrove connectivity14,15,16. Although measurement of total areal loss is an important step towards informing conservation priorities, other metrics of environmental change, such as fragmentation, are also important indicators of habitat health17,18,19,20, ecological function and resilience of fragmented mangrove forests may be compromised in multiple ways, making fragmentation an important change to monitor22. For example, fragmented forests are likely to have a reduced capacity to ameliorate waves23,24 and so will have higher through-flow of tidal waters leading to greater erosion of sediment substrate25. Increased sediment erosion may affect the capacity of mangroves to accrete and keep pace with sea level rise7,8, so by increasing erosion fragmentation may reduce the ability of mangroves to adapt to sea level rise. In addition, increased mangrove fragmentation may mean forests are more accessible to humans, potentially leading to increased deforestation of mangroves and exploitation of species that use mangroves as habitat26. Finally, the biological integrity of fragmented mangroves is compromised by lower species diversity of both birds27 and estuarine fish28. Thus, the capability for mangroves to provide critical habitat for many fished species may be jeopardised by fragmentation. The biophysical impacts of fragmentation in mangroves are likely to influence the ability of forests to capture and store carbon6,29. Given the number of important ecological changes associated with the fragmentation of mangrove forests, we suggest that fragmentation should be explored as a way to monitor the deterioration of mangrove ecosystems at large compared rates of mangrove fragmentation and deforestation from a high spatial resolution dataset from 2000 to 2012 at a global scale, with ~30 m resolution at the equator10. We used four metrics of fragmentation that represent different aspects of the quality of mangrove forests globally clumpiness, perimeter-area fractal dimension PAFRAC, mean patch area and the mean distance to a patch’s nearest neighbour Supplementary Methods S1. The clumpiness index and PAFRAC assess how patches are dispersed across the landscape, and patch shape, respectively30. These metrics are independent of the areal extent of forests31, making them ideal for assessing shifts in mangrove forest arrangement. The metrics mean patch size and mean distance to nearest patch have the advantage of being immediately comprehensible and describing ecologically relevant shifts in forest arrangement28,32. However, these two metrics can be highly correlated with the extent of forests in the patterns of mangrove fragmentation are related to, but distinct from, patterns in mangrove loss at the global scale. Six of the ten nations with the highest rates of mangrove loss were also in at least one of the lists for the top ten nations for fragmentation rates Indonesia, Malaysia, Myanmar, Thailand, United States, and the Philippines Table 1. We also identified hotspots for loss that had lower rates of fragmentation, including Brazil, northern Myanmar, Mexico and Cuba Figs. 1, 2 and Supplementary Fig. S1. Although fragmentation is often linked to loss, there is a ubiquitous trend toward fragmentation globally, even in areas with low rates of loss Fig. 2, Supplementary Table S1. Landscapes in regions with both high rates of loss and fragmentation, such as Myanmar, Indonesia and Malaysia, displayed high values for all measures of fragmentation Fig. 3. Hotspots of fragmentation within the top ten for at least two of four fragmentation metrics include Cambodia, Cameroon, Guatemala, Honduras, Indonesia, Malaysia, New Guinea and the southern Caribbean Aruba, Grenada, and Trinidad and Tobago. Some of these areas are associated with high deforestation rates; however, areas such as Cambodia, Cameroon, New Guinea and nations with little mangrove area in the southern Caribbean Aruba, Grenada, and Trinidad and Tobago have comparatively low loss 1 The top ten nations ranked by total areal loss and rates of fragmentation for each of the four main metrics. Nation and value are size tableFigure 1A description of similarities and disparities between fragmentation and areal loss of mangroves, with example size imageFigure 2Global distribution of total mangrove loss panel A, proportional mangrove loss panel B and fragmentation, measured as 1 changes in distance to nearest patch Panel C and, 2 shifts in mean size of mangrove patches panel D.Full size imageFigure 3Maps of four landscapes, each demonstrating a notable shift in one of the four metrics of fragmentation employed in this size imageThe spatial distribution of mangrove fragmentation is variable and depends on which metric of fragmentation is considered Table 1, Fig. 2. Generally, there is a fragmentation hotspot centred in Southeast Asia, concomitant with known areas of mangrove loss10. There are other hotspots of fragmentation albeit less severe than in Southeast Asia in the Caribbean, northern South America and the eastern Pacific. These hotspots ranked highly for fragmentation in the metrics of mean distance to nearest neighbour and patch area see Fig. 2, metrics which have high ecological relevance. Western Africa also ranked highly on the sensitive metrics of PAFRAC and clumpiness see Supplementary Fig. S1.Land-use changesFragmentation and loss were highly correlated in Southeast Asia, and this relationship was mediated by the specific land-use transition. Rank correlations indicate a strong relationship between the extent of loss and all fragmentation metrics correlation coefficients ranged from to all correlations had p 0 to 0. Rasters were spatially transformed to the local UTM and exported as GeoTIFF files, resulting in 8,985 landscapes with mangrove presence in 2000. All spatial processing was conducted using R version and the packages raster48, rgeos49, rgdal50 and sp51. Fragstats52 was used to process the landscapes. Fragmentation statistics calculated included CLUMPY, PAFRAC, ENN_MN and AREA_MN. Total mangrove cover in the landscape was calculated using the raw cover values in the cropped raster were assigned to a nation and a biogeographical ecoregion53. The GADM version and ecoregional layers53 were cropped to each landscape, and the nation and ecoregion that was most dominant in the landscape were assumed to be the nation/ecoregion containing the mangroves within the landscape. The majority of landscapes were assigned only one nation Plotting was conducted using the R packages sf54 and of land-use transitionsFor Southeast Asia, dominant land-use transitions were extracted from a previous analysis using remote sensing of Landsat imagery45. In the previous study, all areas of mangrove deforested in Southeast Asia between 2000 and 2012 and larger than hectares in size were classified to identify their land cover in 2012 using a machine learning model45. Data on the prevalence of six types of land-use transition were extracted from this dataset urban developments, rice paddy, oil palm plantations, aquaculture, mangrove regrowth including mangrove forestry, rehabilitation or natural regeneration and other including recent deforestation with no identifiable form of land-use, deforestation caused by erosion, and conversion to non-oil palm terrestrial landscapes. Each landscape was queried for the number of mangrove patches and the total area of mangrove undergoing different land-use transitions. Many landscapes had multiple land-use transitions within their boundaries. Accordingly, the dominant land-use transition for each landscape was assigned. The land-use classification which had both; 1 the highest total area within the landscape, and 2 was present in the most or equal to the most mangrove patches within the landscape was considered dominant. Spearman rank correlations were conducted to identify the relationship between mangrove deforestation loss in hectares and absolute shifts in metrics describing habitat arrangement. The Spearman rank correlation was used because initial analyses with linear regression indicated the residuals did not conform to a normal distribution. We then modelled the correlation coefficient as a function of fragmentation metric and land-use transition using a linear model. The linear model tested the hypothesis that the extent of deforestation and fragmentation would be more strongly linked for some land-use transitions than others. All processing was conducted in R version Data availabilityThe datasets generated during and analysed during the current study are available in the dryad repository, WEBLINK. To be made public upon publication.ReferencesKoch, E. W. et al. Non-linearity in ecosystem services temporal and spatial variability in coastal protection. Front. Ecol. 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Springer-Verlag New York, 2016.Download was supported by a Discovery Early Career Researcher Award DE160101207 from the Australian Research Council, and and by The Global Wetlands Project. FA was supported by an Advance Queensland Fellowship from the Queensland Government, Australia. was supported by an Australian Government Research Training Program RTP informationAuthors and AffiliationsAustralian Rivers Institute – Coast and Estuaries, School of Environment and Science, Griffith University, Gold Coast, QLD, 4222, AustraliaDale N. Bryan-Brown & Rod M. ConnollyETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore, SingaporeDaniel R. RichardsAustralian Rivers Institute, Griffith University, Nathan, QLD, 4111, AustraliaFernanda AdameDepartment of Geography, National University of Singapore, 1 Arts Link, 117570, Singapore, SingaporeDaniel A. FriessAustralian Rivers Institute – Coast and Estuaries, School of Environment and Science, Griffith University, Nathan, QLD, 4111, AustraliaChristopher J. BrownAuthorsDale N. Bryan-BrownYou can also search for this author in PubMed Google ScholarRod M. ConnollyYou can also search for this author in PubMed Google ScholarDaniel R. RichardsYou can also search for this author in PubMed Google ScholarFernanda AdameYou can also search for this author in PubMed Google ScholarDaniel A. FriessYou can also search for this author in PubMed Google ScholarChristopher J. BrownYou can also search for this author in PubMed Google and conceived the project. conducted the data management and analysis. suggested project direction and provided support in planning stages. and provided data for land-use changes in Southeast Asia. and interpreted results. drafted the manuscript. All authors contributed to editing the manuscript. All authors consented to the manuscript being submitted in its final authorCorrespondence to Christopher J. declarations Competing interests The authors declare no competing interests. Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional informationRights and permissions Open Access This article is licensed under a Creative Commons Attribution International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit Reprints and PermissionsAbout this articleCite this articleBryan-Brown, Connolly, Richards, et al. Global trends in mangrove forest fragmentation. Sci Rep 10, 7117 2020. citationReceived 18 June 2019Accepted 06 April 2020Published 28 April 2020DOI This article is cited by New contributions to mangrove rehabilitation/restoration protocols and practices Alexander Cesar FerreiraLuiz Drude de LacerdaLuis Ernesto Arruda Bezerra Wetlands Ecology and Management 2023 Natural Protected Areas effect on the cover change rate of mangrove forests in the Yucatan Peninsula, Mexico Laura Osorio-OlveraRodolfo Rioja-NietoFrancisco Guerra-Martínez Wetlands 2023 Genomic population structure of Parkia platycephala Benth. Leguminosae from Northeastern Brazil João Gabriel Silva MoraisMarcones Ferreira CostaAngela Celis de Almeida Lopes Genetic Resources and Crop Evolution 2023 Effect of Mangrove Complexity and Environmental Variables on Fish Assemblages Across a Tropical Estuarine Channel of the Mexican Pacific Salvador Santamaría-DamiánCristian Tovilla-HernándezAlejandro Ortega-Argueta Wetlands 2023 Mangrove diversity is more than fringe deep Steven W. J. CantyJohn Paul KennedyJennifer K. Rowntree Scientific Reports 2022 CommentsBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. As firefighters rushed to protect communities under threat, more than 160 forest fires continued to burn in Quebec, the vast majority of them out of control, coating much of the province in thick smoke and haze. The fires prompted air quality warnings across Quebec on Monday morning, including in Montreal, where Environment Canada urged residents to take precautions against smog. In Sept-Îles, the North Shore town whose outskirts are threatened by fire, Environment Canada issued a severe special air quality statement, urging residents to wear respirators if they had to venture outside and to use air filters to recirculate and clean indoor air. The special air quality statement extended over much of the province, stretching from the north shore to James Bay and including part of the Outaouais. Monday late afternoon, the Atikamekw community of Opitciwan, 350 kilometres north of Montreal, announced it would be taking people with respiratory and mobility issues to Roberval or Lac Saint-Jean in the Saguenay region due to the deterioration of air quality. Steeve Beaupré, the mayor of Sept-Îles, said residents there woke up to a thick cloud of smoke on Monday morning. He urged people to keep their doors and windows closed and to avoid physical activity outdoors. Beaupré accompanied firefighters on a flyover to observe the fires near the town on Sunday. He said he witnessed the proximity of the flames to residential areas and how unpredictable they were. One of the major fires near Sept-Îles had grown since then but was being pushed northward, away from the town, by wind. Fortunately, he said, the weather forecast seemed to offer some relief. "The weather seems to be turning in our favour with the promise of significant precipitation this week," he said. Premier François Legault said at an afternoon news conference that approximately 10,000 people have had to leave their homes across the province because of the fires, which are burning over 2,000 square does all this wildfire smoke in southern Quebec mean for your health?Yan Boulanger, a research scientist with Natural Resources Canada said that the ground covered by the fires in Quebec's commercial forests in the past four days is massive and estimates that it's equal to what was covered in the past 10 years combined."It's a really exceptional situation," he said. "To have that number of fires means we had very dry, very warm conditions in the last few days and we also had a thunderstorm on Thursday that ignited those fires."The number of fires has overwhelmed the capacity of the province's forest fire fighting agency, SOPFEU. WATCH Forest fires are worse than usual this year. Here's why What's behind Quebec's 'unprecedented' forest fire season?CBC's Steve Rukavina explains why so many fires are burning, many out of control, in the province this agency is prioritizing fighting fires that pose an urgent risk to human life or critical infrastructure. Firefighters are battling 35 fires, but Mélanie Morin, a spokesperson for SOPFEU, said reinforcements from the Canadian military were beginning to lend a hand. Forest fires force thousands out of their homes in Quebec's North ShoreClova, Que., the small town Premier Legault said was burning to the ground, still standing — for now"More manpower means more fires that we can intervene on," she said. "Daily, our priorities change depending on wind shifts. One day a fire could be going away from a community whereas the next day a wind shift could change that."Morin hoped the rain and colder weather forecast for later this week would help firefighters gain the upper hand on some blazes. "Cooler temperatures, lighter winds are always a very welcome addition to the mix," she said. Smog filled the air in the Montreal area on Monday afternoon. Environment Canada warned asthmatic children and people with respiratory ailments or heart disease to avoid intense physical activity Boyer, the mayor of Laval, Que., said on Twitter public transit prices there would be lowered to $1 per bus ride on Tuesday in an effort to reduce the number of cars on the road and keep smog to a minimum.

in southeast asia many forests have been