The Effects of Climate Change and Anthropogenic Activities on Patterns, Structures and Functions of Terrestrial Ecosystems

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Cuicui Jiao
Sichuan University of Science and Engineering
Wenping Yuan
Sun Yat-sen University
Wei Zhao
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS)
Guoqi Wen
Laval University
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ved values of T_mean (A), T_max (B), and T_min (C). The solid line is 1 :1 line."},{"height":1372,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g003.jpg","width":1902,"caption":"Distribution of DISO for T_mean,T_max and extreme temperature indices of 17 meteorological stations in the QLM."},{"height":1135,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g004.jpg","width":1902,"caption":"Trends in daily temperature and DTR in observation and ERA5-Land datasets from 1979 to 2017 in the QLM."},{"height":1122,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g005.jpg","width":1902,"caption":"Trends in absolute indices of extreme temperature in observation and ERA5-Land datasets from 1979 to 2017 in the QLM."},{"height":1125,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g006.jpg","width":1902,"caption":"Trends in percentile-based indices of extreme temperature in observation and ERA5-Land datasets from 1979 to 2017 in the QLM."},{"height":574,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g007.jpg","width":1902,"caption":"Trends in threshold indices of extreme temperature in observation and ERA5-Land datasets from 1979 to 2017 in the QLM."},{"height":1149,"url":"https://www.frontiersin.org/files/Articles/1135895/fevo-11-1135895-HTML/image_m/fevo-11-1135895-g008.jpg","width":1902,"caption":"Relationship of bias and elevation differences between ERA5-Land daily temperature and daily observations in the QLM during the period of 1979–2017."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1135895","pubDate":"2023-02-24","score":28.00823388399618,"title":"Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China","topics":["Climate Change","reanalysis","Qilian Mountains","Extreme temperature","ERA5-land"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1135895/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f35","abstract":"Atmospheric nitrogen (N) deposition is among the main manifestations of global change and has profoundly affected forest biogeochemical cycles. However, the threshold of N deposition to soil nutrient contents and enzyme activities have rarely been studied in a forest. We here explored the effects of N deposition on soil nutrients and enzyme activities in the Larix principis-rupprechtii plantation on the northern Yanshan Mountain through multigradient N addition experiments (0, 5, 10, 20, 40, 80, and 160 kg N ha−1 yr−1) after fertilization for 2 years. Compared with the controls, N addition first led to a decrease in soil NH4+-N and NO3−-N, which then increased significantly. N addition had no significant effects on other soil nutrients. N addition overall elevated soil β-glucosidase activity. N application of \u003e40 kg N ha−1 yr−1 significantly reduced soil leucine aminopeptidase activity, but had no significant effects on soil acid phosphatase, N-acetyl-β-D-glucosidase, and urease activities. N addition increased the overall stoichiometry ratio of EEA C:N and EEA C:P, but EEA N:P started decreasing after N application of 40 kg N ha−1 yr−1. The ratios of C, N, and P acquisition activities changed from 1:1.2:1 under the control conditions to 1:1.1:1 under N application of 160 kg N ha−1 yr−1. N addition increased the overall vector length and had no significant effects on the vector angle. Correlation and redundancy analyses revealed that N addition-induced change in available soil N was the main factor affecting soil enzyme activity and stoichiometry. In general, different enzyme activities had different sensitivities to N addition. Moderate N addition or atmospheric N deposition (e.g., \u003c40 kg N ha−1 yr−1) had beneficial effects on soil nutrient cycling and microorganisms in Larix principis-rupprechtii plantation.","htmlAbstract":"\u003cp\u003eAtmospheric nitrogen (N) deposition is among the main manifestations of global change and has profoundly affected forest biogeochemical cycles. However, the threshold of N deposition to soil nutrient contents and enzyme activities has rarely been studied in a forest. In this study, we explored the effects of N deposition on soil nutrients and enzyme activities in a \u003ci\u003eLarix principis-rupprechtii\u003c/i\u003e plantation on the northern Yanshan Mountain through multigradient N addition experiments (0, 5, 10, 20, 40, 80, and 160 kg N ha\u003csup\u003e−1\u003c/sup\u003e year\u003csup\u003e−1\u003c/sup\u003e) after fertilization for 2 years. Compared with the controls, N addition first led to a decrease in soil NH\u003cmath\u003e\u003cmsubsup\u003e\u003cmrow\u003e\u003c/mrow\u003e\u003cmrow\u003e\u003cmn\u003e4\u003c/mn\u003e\u003c/mrow\u003e\u003cmrow\u003e\u003cmo\u003e+\u003c/mo\u003e\u003c/mrow\u003e\u003c/msubsup\u003e\u003c/math\u003e-N and NO\u003cmath\u003e\u003cmsubsup\u003e\u003cmrow\u003e\u003c/mrow\u003e\u003cmrow\u003e\u003cmn\u003e3\u003c/mn\u003e\u003c/mrow\u003e\u003cmrow\u003e\u003cmo\u003e-\u003c/mo\u003e\u003c/mrow\u003e\u003c/msubsup\u003e\u003c/math\u003e-N, which then increased significantly. N addition had no significant effects on other soil nutrients. N addition overall elevated soil β-glucosidase activity. N application of \u0026gt;40 kg N ha\u003csup\u003e−1\u003c/sup\u003e year\u003csup\u003e−1\u003c/sup\u003e significantly reduced soil leucine aminopeptidase activity but had no significant effects on soil acid phosphatase, N-acetyl-β-D-glucosidase, and urease activities. N addition increased the overall stoichiometry ratio of EEA C:N and EEA C:P, but EEA N:P started decreasing after N application of 40 kg N ha\u003csup\u003e−1\u003c/sup\u003e year\u003csup\u003e−1\u003c/sup\u003e. The ratios of C, N, and P acquisition activities changed from 1:1.2:1 under the control conditions to 1:1.1:1 under the N application of 160 kg N ha\u003csup\u003e−1\u003c/sup\u003e year\u003csup\u003e−1\u003c/sup\u003e. N addition increased the overall vector length and had no significant effects on the vector angle. Correlation and redundancy analyses revealed that N addition-induced change in available soil N was the main factor affecting soil enzyme activity and stoichiometry. In general, different enzyme activities had different sensitivities to N addition. Moderate N addition or atmospheric N deposition (e.g., \u0026lt;40 kg N ha\u003csup\u003e−1\u003c/sup\u003e year\u003csup\u003e−1\u003c/sup\u003e) had beneficial effects on soil nutrient cycling and microorganisms in a \u003ci\u003eLarix principis-rupprechtii\u003c/i\u003e plantation.\u003c/p\u003e","authors":[{"fullName":"Xiaocong Yang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Forestry, Hebei Agricultural University","address":null},"affiliations":[{"name":"College of Forestry, Hebei Agricultural University","address":null}],"nessieId":null},{"fullName":"Liu Yang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Forestry, Hebei Agricultural University","address":null},"affiliations":[{"name":"College of Forestry, Hebei Agricultural University","address":null}],"nessieId":null},{"fullName":"Qianru Li","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Economics and Management, Hebei Agricultural University","address":null},"affiliations":[{"name":"College of Economics and Management, Hebei Agricultural University","address":null}],"nessieId":null},{"fullName":"Xiao Li","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Mulanweichang National Forestry Administration of Hebei Province","address":null},"affiliations":[{"name":"Mulanweichang National Forestry Administration of Hebei Province","address":null}],"nessieId":null},{"fullName":"Guoqiao Xu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Mulanweichang National Forestry Administration of Hebei Province","address":null},"affiliations":[{"name":"Mulanweichang National Forestry Administration of Hebei Province","address":null}],"nessieId":null},{"fullName":"Zhongqi Xu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Forestry, Hebei Agricultural University","address":null},"affiliations":[{"name":"College of Forestry, Hebei Agricultural University","address":null}],"nessieId":null},{"fullName":"Yanlong Jia","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2090002/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2090002/overview","affiliation":{"name":"College of Forestry, Hebei Agricultural University","address":null},"affiliations":[{"name":"College of Forestry, Hebei Agricultural University","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2023-01-25","recentDate":"2023-02-20"},"doi":"10.3389/fevo.2023.1105150","frontiersExtra":{"articleType":"Original Research","impact":{"citations":5,"crossrefCitations":0,"downloads":575,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":1910},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1105150,"images":[{"height":138,"url":"https://www.frontiersin.org/files/myhome article library/1105150/1105150_Thumb_400.jpg","width":400,"caption":null},{"height":457,"url":"https://www.frontiersin.org/files/Articles/1105150/fevo-11-1105150-HTML/image_m/fevo-11-1105150-g001.jpg","width":1325,"caption":"Effects of nitrogen addition on soil ammonium nitrogen (A) and nitrate nitrogen (B) in a Larix principis-rupprechtii plantation in North China."},{"height":1449,"url":"https://www.frontiersin.org/files/Articles/1105150/fevo-11-1105150-HTML/image_m/fevo-11-1105150-g002.jpg","width":1325,"caption":"Effects of nitrogen addition on soil enzyme activity of a Larix principis-rupprechtii plantation in North China. (A–E) BG, NAG, LAP, ACP, and UE, respectively."},{"height":959,"url":"https://www.frontiersin.org/files/Articles/1105150/fevo-11-1105150-HTML/image_m/fevo-11-1105150-g003.jpg","width":1325,"caption":"Effects of nitrogen addition on soil enzyme stoichiometry in a Larix principis-rupprechtii plantation in North China. (A–C) BG: (LAP+NAG), BG:ACP, and (LAP+NAG):ACP, respectively."},{"height":451,"url":"https://www.frontiersin.org/files/Articles/1105150/fevo-11-1105150-HTML/image_m/fevo-11-1105150-g004.jpg","width":1325,"caption":"Effect of nitrogen addition on the length (A) and angle (B) of enzyme activity vector."},{"height":612,"url":"https://www.frontiersin.org/files/Articles/1105150/fevo-11-1105150-HTML/image_m/fevo-11-1105150-g005.jpg","width":624,"caption":"Redundant analysis diagram of the influence of soil nutrients on soil enzyme activity and stoichiometric ratios. Soil enzyme activity and the ecological enzyme stoichiometric ratio were used as response variables, and soil environmental factors were used as explanatory variables for redundancy analysis (RDA). The arrows connecting soil microbial enzyme activities and enzyme stoichiometric ratios are in blue, and the arrows connecting the soil environmental factors are in red."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1105150","pubDate":"2023-02-20","score":9.996873208609157,"title":"Short-term responses of soil nutrients and enzyme activities to nitrogen addition in a Larix principis-rupprechtii plantation in North China","topics":["Nitrogen addition","Soil nutrients","North China","Larix principis-rupprechtii","Soil enzyme activity"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1105150/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f42","abstract":"Abstract China's forests have sequestrated a significant amount of carbon over the past two decades. However, it is not clear whether China's forests will be able to continue to have as much carbon sequestration potential capacity in the future. In order to research China’s forest carbon storage and carbon sequestration potential capacities at spatial and temporal scales, we built a digital forest model for each province of China using the data from The China Forest Resources Report (2014-2018) and calculated the carbon storage capacity and sequestration potential capacity of each province with the current management practices without considering natural successions. The results showed that the current forest carbon storage is 10.0 Pg C, and the carbon sequestration potential in the next 40 years (from year 2019 to 2058) will be 5.04 Pg C. Since immature forests account for the majority of current forests, the carbon sequestration capacity of the forest was also high (0.202Pg C yr-1). However, the forest carbon storage reached the maximum with the increase of stand maturity, and, at this time, the forest carbon sequestration capacity leans towards zero. After 90 years, all stands developed into mature and over-mature forests, and the forest carbon sequestration capacity was 0.008 Pg yr-1; and the carbon sequestration rate was approximately 4% of what it is nowadays. The change trend of forest carbon in each province was consistent with that of the country. In addition, considering the large forest coverage area in China, the differences in tree species and growing conditions, the forest carbon storage and carbon sequestration capacities among provinces were different. The growth rate of carbon density in high-latitude provinces (such as Heilongjiang, Jilin, and Inner Mongolia) was lower than that in the south (Guangdong, Guangxi, or Hunan), but the forest carbon potential was higher. Planning and implementing targeted forest management strategies is the key to increasing forest carbon storage and extending the service time of forest carbon sinks in provinces. In order to reach the national carbon neutrality goals, we recommend that each province have an informative strategic forest management plan.","htmlAbstract":"\u003cp\u003e\u003cb\u003eIntroduction:\u003c/b\u003e China’s forests have sequestrated a significant amount of carbon over the past two decades. However, it is not clear whether China’s forests will be able to continue to have as much carbon sequestration potential capacity in the future.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e In order to research China’s forest carbon storage and carbon sequestration potential capacities at spatial and temporal scales, we built a digital forest model for each province of China using the data from The China Forest Resources Report (2014– 2018) and calculated the carbon storage capacity and sequestration potential capacity of each province with the current management practices without considering natural successions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e The results showed that the current forest carbon storage is 10.0 Pg C, and the carbon sequestration potential in the next 40 years (from year 2019 to 2058) will be 5.04 Pg C. Since immature forests account for the majority of current forests, the carbon sequestration capacity of the forest was also high (0.202 Pg C year−1). However, the forest carbon storage reached the maximum with the increase of stand maturity. At this time, if scenarios such as afforestation and reforestation, human and natural disturbances, and natural succession are not considered, the carbon sequestration capacity of forests will continue to decrease. After 90 years, all stands will develop into mature and over-mature forests, and the forest carbon sequestration capacity is 0.008 Pg year−1; and the carbon sequestration rate is ~4% of what it is nowadays. The change trend of forest carbon in each province is consistent with that of the country. In addition, considering the large forest coverage area in China, the differences in tree species and growing conditions, the forest carbon storage and carbon sequestration capacities among provinces were different. The growth rate of carbon density in high-latitude provinces (such as Heilongjiang, Jilin, and Inner Mongolia) was lower than that in the south (Guangdong, Guangxi, or Hunan), but the forest carbon potential was higher.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscussion:\u003c/b\u003e Planning and implementing targeted forest management strategies is the key to increasing forest carbon storage and extending the service time of forest carbon sinks in provinces. In order to reach the national carbon neutrality goals, we recommend that each province have an informative strategic forest management plan.\u003c/p\u003e","authors":[{"fullName":"Fushan Cheng","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2117567/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2117567/overview","affiliation":{"name":"College of Forestry, Beijing Forestry University","address":null},"affiliations":[{"name":"College of Forestry, Beijing Forestry University","address":null}],"nessieId":null},{"fullName":"Jiaxin Tian","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2200008/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2200008/overview","affiliation":{"name":"College of Forestry, Northeast Forestry University","address":null},"affiliations":[{"name":"College of Forestry, Northeast Forestry University","address":null}],"nessieId":null},{"fullName":"Jingyuan He","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2120413/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2120413/overview","affiliation":{"name":"College of Forestry, Beijing Forestry University","address":null},"affiliations":[{"name":"College of Forestry, Beijing Forestry University","address":null}],"nessieId":null},{"fullName":"Huaijiang He","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Jilin Provincial Academy of Forestry Sciences","address":null},"affiliations":[{"name":"Jilin Provincial Academy of Forestry Sciences","address":null},{"name":"Jilin Province Degraded Forest Ecosystem Restoration and Reconstruction Interregional Cooperation Science and Technology Innovation Center","address":null}],"nessieId":null},{"fullName":"Guoliang Liu","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2116314/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2116314/overview","affiliation":{"name":"College of Forestry, Beijing Forestry University","address":null},"affiliations":[{"name":"College of Forestry, Beijing Forestry University","address":null},{"name":"Nanjing Jialin System Engineering Ltd","address":null}],"nessieId":null},{"fullName":"Zhonghui Zhang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Jilin Provincial Academy of Forestry Sciences","address":null},"affiliations":[{"name":"Jilin Provincial Academy of Forestry Sciences","address":null},{"name":"Jilin Province Degraded Forest Ecosystem Restoration and Reconstruction Interregional Cooperation Science and Technology Innovation Center","address":null}],"nessieId":null},{"fullName":"Liping Zhou","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Shenyang Forestry Management Institute of China Railway Shenyang Bureau Group Co. Ltd","address":null},"affiliations":[{"name":"Shenyang Forestry Management Institute of China Railway Shenyang Bureau Group Co. Ltd","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2023-01-31","recentDate":"2023-02-20"},"doi":"10.3389/fevo.2023.1110594","frontiersExtra":{"articleType":"Original Research","impact":{"citations":14,"crossrefCitations":0,"downloads":1027,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":4500},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1110594,"images":[{"height":282,"url":"https://www.frontiersin.org/files/myhome article library/1110594/1110594_Thumb_400.jpg","width":400,"caption":null},{"height":598,"url":"https://www.frontiersin.org/files/Articles/1110594/fevo-11-1110594-HTML/image_m/fevo-11-1110594-g001.jpg","width":851,"caption":"The carbon storage and carbon sequestration capacity of China’s forests in contemporary times and in the future."},{"height":579,"url":"https://www.frontiersin.org/files/Articles/1110594/fevo-11-1110594-HTML/image_m/fevo-11-1110594-g002.jpg","width":851,"caption":"The age structure of China’s forests in currently and in the future."},{"height":1555,"url":"https://www.frontiersin.org/files/Articles/1110594/fevo-11-1110594-HTML/image_m/fevo-11-1110594-g003.jpg","width":1654,"caption":"Carbon storage of China’s forests by the provinces."},{"height":1915,"url":"https://www.frontiersin.org/files/Articles/1110594/fevo-11-1110594-HTML/image_m/fevo-11-1110594-g004.jpg","width":1902,"caption":"Carbon sequestration capacities (Tg C year−1) of 31 provinces over the next 200 years. Forests in China are divided into six regions: Northwestern region (Shaanxi, Gansu, Xinjiang, Qinghai, and Ningxia), Northern region (Inner Mongolia, Hebei, Inner Mongol, Shanxi, Beijing, and Tianjin), Northeastern region (Heilongjiang, Jilin, and Liaoning), Southwestern region (Sichuan, Yunnan, Tibet, Guizhou, and Chongqing), Central southern region (Guangxi, Guangdong, Hubei, Hunan, Henan, and Hainan), and Eastern region (Fujian, Jiangxi, Zhejiang, Shanghai, Anhui, Shandong, and Jiangsu)."},{"height":1903,"url":"https://www.frontiersin.org/files/Articles/1110594/fevo-11-1110594-HTML/image_m/fevo-11-1110594-g005.jpg","width":1902,"caption":"Carbon density (Mg C ha−1) of 31 provincial regions."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1110594","pubDate":"2023-02-20","score":25.766915072871747,"title":"The spatial and temporal distribution of China’s forest carbon","topics":["Carbon Sequestration","forest carbon storage","Spatial–temporal distribution","forest carbon modeling","national and provincial scale"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1110594/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f45","abstract":"Land use and land cover (LULC) change is a pattern of alteration of the Earth's land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances economic development and ecological protection. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.","htmlAbstract":"\u003cp\u003eLand use and land cover (LULC) change is a pattern of alteration of the Earth\u0026#x2019;s land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances ED and EP. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.\u003c/p\u003e","authors":[{"fullName":"Yu Tao","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2104501/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2104501/overview","affiliation":{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null},"affiliations":[{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null},{"name":"Anhui Province Key Laboratory of Physical Geographical Environment","address":null}],"nessieId":null},{"fullName":"Lei Tian","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2107486/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2107486/overview","affiliation":{"name":"College of Forestry, Nanjing Forestry University","address":null},"affiliations":[{"name":"College of Forestry, Nanjing Forestry University","address":null}],"nessieId":null},{"fullName":"Chun Wang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null},"affiliations":[{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null},{"name":"Anhui Province Key Laboratory of Physical Geographical Environment","address":null}],"nessieId":null},{"fullName":"Wen Dai","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null},"affiliations":[{"name":"School of Geographic Information and Tourism, Chuzhou University","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2023-01-16","recentDate":"2023-02-14"},"doi":"10.3389/fevo.2023.1102015","frontiersExtra":{"articleType":"Original Research","impact":{"citations":20,"crossrefCitations":0,"downloads":1233,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":4882},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1102015,"images":[{"height":353,"url":"https://www.frontiersin.org/files/myhome article library/1102015/1102015_Thumb_400.jpg","width":400,"caption":null},{"height":1459,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g001.jpg","width":1654,"caption":"Location of Nanjing metropolitan circle with a digital elevation model (DEM)."},{"height":1083,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g002.jpg","width":1775,"caption":"Historic LULC data in (A) 2000, (B) 2010, and (C) 2020 for the Nanjing metropolitan circle."},{"height":1854,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g003.jpg","width":1902,"caption":"The 18 driving factors affecting LULC. Note: The legend of soil type represents, 1: yellow-brown soils; 2: yellow-cinnamon soils; 3: limestone soils; 4: chisley soils; 5: fluvo-aquic soils; 6: paddy soils; 7: red earths; 8: yellow earths; 9: water; and 10: others."},{"height":1918,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g004.jpg","width":1654,"caption":"Research framework."},{"height":1402,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g005.jpg","width":1772,"caption":"Contributions of driving factors."},{"height":1900,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g006.jpg","width":1562,"caption":"Simulation results of LULC in 2030 under four scenarios: (A) ND; (B) ED; (C) EP; and (D) CD."},{"height":1517,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g007.jpg","width":1654,"caption":"Land use type conversions from 2020–2030 under four scenarios: (A) ND; (B) ED; (C) EP; and (D) CD."},{"height":1496,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g008.jpg","width":854,"caption":"Carbon storage distribution in 2030 under four scenarios: (A) ND; (B) ED; (C) EP; and (D) CD."},{"height":732,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g009.jpg","width":851,"caption":"Sequestered carbon content of different land uses over 2020–2030 under four scenarios."},{"height":1492,"url":"https://www.frontiersin.org/files/Articles/1102015/fevo-11-1102015-HTML/image_m/fevo-11-1102015-g010.jpg","width":854,"caption":"Net present value (unit RMB) of Nanjing metropolitan circle in 2020–2030 under four scenarios: (A) ND; (B) ED; (C) EP; and (D) CD."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1102015","pubDate":"2023-02-14","score":32.75317456181508,"title":"Dynamic simulation of land use and land cover and its effect on carbon storage in the Nanjing metropolitan circle under different development scenarios","topics":["Carbon Storage","land use and land cover","Scenario simulation","InVEST model","Plus model","Nanjing metropolitan circle"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1102015/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f44","abstract":"Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses' spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.","htmlAbstract":"\u003cp\u003eSeagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines \u003ci\u003ein situ\u003c/i\u003e sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.\u003c/p\u003e","authors":[{"fullName":"Bohao He","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/1973962/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/1973962/overview","affiliation":{"name":"School of Ecology and Environment, Hainan University","address":null},"affiliations":[{"name":"School of Ecology and Environment, Hainan University","address":null},{"name":"Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University","address":null}],"nessieId":"429497323705"},{"fullName":"Yanghe Zhao","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2150831/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2150831/overview","affiliation":{"name":"School of Ecology and Environment, Hainan University","address":null},"affiliations":[{"name":"School of Ecology and Environment, Hainan University","address":null},{"name":"Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University","address":null}],"nessieId":null},{"fullName":"Siyu Liu","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2035131/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2035131/overview","affiliation":{"name":"School of Ecology and Environment, Hainan University","address":null},"affiliations":[{"name":"School of Ecology and Environment, Hainan University","address":null},{"name":"Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University","address":null}],"nessieId":null},{"fullName":"Shahid Ahmad","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/935463/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/935463/overview","affiliation":{"name":"School of Ecology and Environment, Hainan University","address":null},"affiliations":[{"name":"School of Ecology and Environment, Hainan University","address":null},{"name":"Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University","address":null}],"nessieId":"309238298226"},{"fullName":"Wei Mao","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/405091/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/405091/overview","affiliation":{"name":"School of Ecology and Environment, Hainan University","address":null},"affiliations":[{"name":"School of Ecology and Environment, Hainan University","address":null},{"name":"Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University","address":null}],"nessieId":"94489828373"}],"dates":{"acceptedDate":"2023-01-16","recentDate":"2023-02-02"},"doi":"10.3389/fevo.2023.1116083","frontiersExtra":{"articleType":"Original Research","impact":{"citations":5,"crossrefCitations":0,"downloads":1023,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":4438},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1116083,"images":[{"height":400,"url":"https://www.frontiersin.org/files/myhome article library/1116083/1116083_Thumb_400.jpg","width":360,"caption":null},{"height":946,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g001.jpg","width":851,"caption":"Study area and seagrass field distribution location sites."},{"height":1268,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g002.jpg","width":1559,"caption":"Random forest model structure."},{"height":1777,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g003.jpg","width":1654,"caption":"Correlation analysis matrix for different environmental variables."},{"height":1086,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g004.jpg","width":1654,"caption":"Importance analysis of 11 environmental features."},{"height":775,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g005.jpg","width":1775,"caption":"Potential habitat areas (Predicted by (A) RF model and (B) RF-SWOA model)."},{"height":1636,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g006.jpg","width":1654,"caption":"RF and RF-SWOA model performance evaluation."},{"height":2016,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g007.jpg","width":1654,"caption":"Sensitivity and specificity tests of RF and RF-SWOA models. The upper part of the panel shows the statistical test results of frequentist analysis, and the lower part of the panel shows the statistical test results of Bayesian analysis. The results follow the gold standard of statistical reporting (Patil, 2021)."},{"height":1117,"url":"https://www.frontiersin.org/files/Articles/1116083/fevo-11-1116083-HTML/image_m/fevo-11-1116083-g008.jpg","width":1904,"caption":"RF and RF-SWOA model performance evaluation. (A) Griewank simulation function; (B) Schwefel 2.20 simulation function; (C) Ackley simulation function; (D) Rastrigin simulation function."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083","pubDate":"2023-02-02","score":16.62789445341963,"title":"Mapping seagrass habitats of potential suitability using a hybrid machine learning model","topics":["machine learning","Hybrid model","niches","seagrass","habitat suitability","Species distribution model"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f43","abstract":"Accurate assessment of the net ecosystem productivity (NEP) is very important for understanding the global carbon balance. However, it remains unknown whether climate change (CC) promoted or weakened the impact of human activities (HA) on the NEP from 1983 to 2018. Here, we quantified the contribution of CC and HA to the global NEP based on a boosted regression tree model and sensitivity analysis. The results show that (1) a total of 69% of the areas showed an upward trend in the NEP, with HA and CC controlled 36.33% and 32.79% of the NEP growth, respectively. While, the contribution of HA (HA_con) far exceeded that of CC by 6.4 times. (2) The CO2 concentration had the largest positive contribution (37%) to NEP and the largest influence area (32.5%). It made the most significant contribution to the NEP trend in the range of 435–440 ppm. In more than 50% of the areas, the main loss factor was solar radiation (SR) in any control area of the climate factors. (3) Interestingly, CC enhanced the positive HA_con to the NEP in 44% of the world, and in 25% of the area, the effect was greater than 50%. Our results shed light on the optimal range of each climatic factor for enhancing the NEP and emphasize the important role of CC in enhancing the positive HA_con to the NEP than found in previous studies.","htmlAbstract":"\u003cp\u003e\u003cb\u003eIntroduction:\u003c/b\u003e Accurate assessment of the net ecosystem productivity (NEP) is very important for understanding the global carbon balance. However, it remains unknown whether climate change (CC) promoted or weakened the impact of human activities (HA) on the NEP from 1983 to 2018.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e Here, we quantified the contribution of CC and HA to the global NEP under six different scenarios based on a boosted regression tree model and sensitivity analysis over the last 40 years.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults and discussion:\u003c/b\u003e The results show that (1) a total of 69% of the areas showed an upward trend in the NEP, with HA and CC controlled 36.33 and 32.79% of the NEP growth, respectively. The contribution of HA (HA_con) far exceeded that of CC by 6.4 times. (2) The CO2 concentration had the largest positive contribution (37%) to NEP and the largest influence area (32.5%). It made the most significant contribution to the NEP trend in the range of 435–440 ppm. In more than 50% of the areas, the main loss factor was solar radiation (SR) in any control area of the climate factors. (3) Interestingly, CC enhanced the positive HA_con to the NEP in 44% of the world, and in 25% of the area, the effect was greater than 50%. Our results shed light on the optimal range of each climatic factor for enhancing the NEP and emphasize the important role of CC in enhancing the positive HA_con to the NEP found in previous studies.\u003c/p\u003e","authors":[{"fullName":"Min Liu","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2100862/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2100862/overview","affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null},{"fullName":"Xiaoyong Bai","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"CAS Center for Excellence in Quaternary Science and Global Change, Xi’an","address":null},{"name":"Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences","address":null}],"nessieId":null},{"fullName":"Qiu Tan","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null},"affiliations":[{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null},{"fullName":"Guangjie Luo","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University","address":null},"affiliations":[{"name":"Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University","address":null}],"nessieId":null},{"fullName":"Cuiwei Zhao","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null},"affiliations":[{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null},{"fullName":"Luhua Wu","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"CAS Center for Excellence in Quaternary Science and Global Change, Xi’an","address":null},{"name":"Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences","address":null}],"nessieId":null},{"fullName":"Fei Chen","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"CAS Center for Excellence in Quaternary Science and Global Change, Xi’an","address":null},{"name":"Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences","address":null}],"nessieId":null},{"fullName":"Chaojun Li","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"CAS Center for Excellence in Quaternary Science and Global Change, Xi’an","address":null},{"name":"Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences","address":null}],"nessieId":null},{"fullName":"Yujie Yang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Geography and Environmental Science, Lanzhou University","address":null},"affiliations":[{"name":"College of Geography and Environmental Science, Lanzhou University","address":null}],"nessieId":null},{"fullName":"Chen Ran","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null},{"fullName":"Xuling Luo","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null},{"fullName":"Sirui Zhang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},"affiliations":[{"name":"State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","address":null},{"name":"School of Geography and Environmental Sciences, Guizhou Normal University","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2022-12-28","recentDate":"2023-01-19"},"doi":"10.3389/fevo.2022.1101135","frontiersExtra":{"articleType":"Original Research","impact":{"citations":11,"crossrefCitations":0,"downloads":691,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":2892},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1101135,"images":[{"height":2302,"url":"https://www.frontiersin.org/files/myhome article library/1101135/1101135_Thumb_400.jpg","width":4317,"caption":null},{"height":2302,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g001.jpg","width":4317,"caption":"The trend of NEP. (A) Spatial pattern of the Theil–Sen trend of NEP between 1983 and 2018; (B) latitude map of NEP trend in different classification, strong decrease is STD, slight decrease is SLD, no change is NC, strong increase is STI, and slight increase is SLI; (C) the ΔP (the carbon sink areas difference between the current year and the previous year) and ΔN (the carbon source areas difference between the current year and the previous year) and (D) the mean value of NEP; (E) the Mann–Kendall mutation test of NEP."},{"height":3098,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g002.jpg","width":4328,"caption":"Sensitivity of NEP to climate factor. Sensitivity of NEP to changes in CO2 concentration (A), P (B), SM (C), SR (D), and T (E); correlation analysis between NEP and climate factors (F)."},{"height":2143,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g003.jpg","width":4328,"caption":"(A) The main controlling factors and (B) the main load controlling factor influencing the NEP. The five driving factors include CO2 concentration, T, P, SR, and SM. (C,D) The contributions of the five driving factors at latitudes of 55°S–80°N."},{"height":3483,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g004.jpg","width":4328,"caption":"The best contribution interval of each climatic factor to the NEP. Annual NEP in the climate domains based on the (A) T and P; (B) T and SR; (C) T and SM; (D) T and CO2; (E) P and CO2; (F) P and SR; (G) P and SM; (H) CO2 and SM; (I) CO2 and SR; (J) SM and SR; (K) NEP test data; and (L) NEP training data."},{"height":5413,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g005.jpg","width":4328,"caption":"The HA_con (A) and the CC_con (B) to NEP, the CC_con and HA_con to NEP increasing (C,E) and decreasing (D,F); spatial pattern of the CC and HA controlled the increasing (G) and decreasing (H) of the NEP; spatial diagram of the positive of CC_con to HA_con (I); the proportions that CC enhances the HA_con (J); the trend of NEP in the top 50 countries of global (K)."},{"height":2115,"url":"https://www.frontiersin.org/files/Articles/1101135/fevo-10-1101135-HTML/image_m/fevo-10-1101135-g006.jpg","width":4328,"caption":"The HA_con and CC_con to NEP in the world and six continents."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2022.1101135","pubDate":"2023-01-19","score":18.556951030972694,"title":"Climate change enhanced the positive contribution of human activities to net ecosystem productivity from 1983 to 2018","topics":["Climate Change","NEP","CO2 concentration","Human activity","Boosted regression tree"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2022.1101135/pdf"},{"__typename":"Feed_Article","_id":"6859731b6a241a0467167f3c","abstract":"The National Key Ecological Functional Areas (NKEFAs) are a location-oriented ecological engineering of China, which rely on the main functional area planning. This paper is designed to assess the co-benefits of ecological product supply and ecological environment improvement in NKEFAs. On this basis, the establishment of NKEFAs is considered a quasi-natural experiment, and the impact of NKEFAs on carbon sequestration (CS) and environmental quality (EQ) is assessed using a time-varying difference-in-differences (DID) model based on the panel data of prefecture-level cities in China from 2001 to 2019. Additionally, we explore whether the co-benefits of ecological product supply and eco-environment protection can be achieved. The results indicate that NKEFAs enhance CS and EQ and thus achieve co-benefits for both. NKEFAs can influence the co-benefits through territory spatial allocation and labor force aggregation, but industrial structure upgrading only positively mediates the impact of NKEFAs on CS. The co-benefits of NKEFAs are heterogeneous on CS and EQ across functional area types, geospatial locations, and quantiles, while only CS at windbreak-sand fixation area, northwestern region, and low quantile regions is enhanced. This study makes a theoretical and methodological contribution to the existing literature on the policy effect assessment of ecological engineering. It also provides a comprehensive framework for evaluating the effects of relevant policies in other countries by integrating the co-benefits of ecological products and eco-environment, analyzing regional heterogeneity, and exploring the underlying mechanisms.","htmlAbstract":"\u003cp\u003e\u003cb\u003eIntroduction:\u003c/b\u003e The National Key Ecological Functional Areas (NKEFAs) are location-oriented ecological engineering of China, which rely on the main functional area planning. The co-benefits of ecological product supply and ecological environment improvement of NKEFAs has not been fully assessed in the literature.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e NKEFAs is considered a quasi-natural experiment, and the time-varying difference-in-differences (DID) model is used to assess the impact of NKEFAs on carbon sequestration (CS) and environmental quality (EQ) based on the panel data of 330 cities in China from 2001 to 2019. Then, we explore whether the co-benefits of ecological product supply and eco-environment protection can be achieved.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults and discussion:\u003c/b\u003e NKEFAs can enhance CS and EQ and thus achieve co-benefits for both. NKEFAs can achieve the co-benefits of CS and EQ through territory spatial allocation and labor force aggregation, but industrial structure upgrading only positively mediates the impact of NKEFAs on CS. The co-benefits of CS and EQ are heterogeneous across functional area types, geospatial locations, and quantiles, while only CS at windbreak-sand fixation area, northwestern region, and low quantile regions is enhanced. This study makes a theoretical and methodological contribution to the existing literature on the policy effect assessment of ecological engineering. It also provides a comprehensive framework for evaluating the ecological effects of relevant policies in other countries by integrating the co-benefits of ecological products and eco-environment, analyzing regional heterogeneity, and exploring the underlying mechanisms.\u003c/p\u003e","authors":[{"fullName":"Hanyu Chen","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Economics, Hebei University","address":null},"affiliations":[{"name":"School of Economics, Hebei University","address":null},{"name":"Research Center of Resources Utilization and Environmental Conservation, Hebei University","address":null}],"nessieId":null},{"fullName":"Mengyang Hou","firstName":null,"middleName":null,"lastName":null,"image":{"height":null,"url":"https://loop.frontiersin.org/images/profile/2085956/70","width":null,"caption":null},"loopProfileUrl":"https://loop.frontiersin.org/people/2085956/overview","affiliation":{"name":"School of Economics, Hebei University","address":null},"affiliations":[{"name":"School of Economics, Hebei University","address":null},{"name":"Research Center of Resources Utilization and Environmental Conservation, Hebei University","address":null}],"nessieId":"730144982846"},{"fullName":"Zenglei Xi","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"School of Economics, Hebei University","address":null},"affiliations":[{"name":"School of Economics, Hebei University","address":null},{"name":"Research Center of Resources Utilization and Environmental Conservation, Hebei University","address":null}],"nessieId":null},{"fullName":"Xiao Zhang","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Economics and Management, Northwest A\u0026F University","address":null},"affiliations":[{"name":"College of Economics and Management, Northwest A\u0026F University","address":null},{"name":"Center for Resource Economics and Environment Management, Northwest A\u0026F University","address":null}],"nessieId":null},{"fullName":"Shunbo Yao","firstName":null,"middleName":null,"lastName":null,"image":null,"loopProfileUrl":null,"affiliation":{"name":"College of Economics and Management, Northwest A\u0026F University","address":null},"affiliations":[{"name":"College of Economics and Management, Northwest A\u0026F University","address":null},{"name":"Center for Resource Economics and Environment Management, Northwest A\u0026F University","address":null}],"nessieId":null}],"dates":{"acceptedDate":"2023-01-02","recentDate":"2023-01-17"},"doi":"10.3389/fevo.2023.1093135","frontiersExtra":{"articleType":"Original Research","impact":{"citations":11,"crossrefCitations":0,"downloads":784,"frontiersViews":0,"pmcDownloads":0,"pmcViews":0,"scopusCitations":0,"views":3331},"isPartOfResearchTopic":true,"isPublished":true,"section":"Population, Community, and Ecosystem Dynamics"},"guid":1093135,"images":[{"height":311,"url":"https://www.frontiersin.org/files/myhome article library/1093135/1093135_Thumb_400.jpg","width":400,"caption":null},{"height":1208,"url":"https://www.frontiersin.org/files/Articles/1093135/fevo-11-1093135-HTML/image_m/fevo-11-1093135-g001.jpg","width":1558,"caption":"The distribution map of NKEFAs."},{"height":746,"url":"https://www.frontiersin.org/files/Articles/1093135/fevo-11-1093135-HTML/image_m/fevo-11-1093135-g002.jpg","width":851,"caption":"The framework of NKEFAs affecting CS and EQ."},{"height":1296,"url":"https://www.frontiersin.org/files/Articles/1093135/fevo-11-1093135-HTML/image_m/fevo-11-1093135-g003.jpg","width":854,"caption":"Parallel trend hypothesis and dynamic effects of NKEFAs on CS (A) and EQ (B)."},{"height":1292,"url":"https://www.frontiersin.org/files/Articles/1093135/fevo-11-1093135-HTML/image_m/fevo-11-1093135-g004.jpg","width":854,"caption":"The placebo test of CS (A) and EQ (B). The X-axis is the coefficients of DID for the 500 random processes. The vertical lines on the right side are the baseline coefficients of DID, all significantly located in the low-tailed of the kernel density distribution."}],"journal":{"guid":471,"name":"Frontiers in Ecology and Evolution","link":null,"nessieId":null,"palette":null,"publisher":"Frontiers Media","images":null,"isOnline":null,"isDeleted":null,"isDisabled":null,"issn":null},"link":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1093135","pubDate":"2023-01-17","score":19.70846144492515,"title":"Co-benefits of the National Key Ecological Function Areas in China for carbon sequestration and environmental quality","topics":["Carbon Sequestration","China","environmental quality","Co-benefits","National key ecological function areas","Time-varying Difference-in-Differences"],"pdfUrl":"https://www.frontiersin.org/articles/10.3389/fevo.2023.1093135/pdf"}]],"pageParams":[null]},"dataUpdateCount":1,"dataUpdatedAt":1757171128181,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":["research-topic-articles",48998,1],"queryHash":"[\"research-topic-articles\",48998,1]"},{"state":{"data":{"researchTopicId":48998,"articleViews":43911,"articleDownloads":13244,"topicViews":1849,"summary":59004},"dataUpdateCount":1,"dataUpdatedAt":1757171128074,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":["research-topic-impact",48998],"queryHash":"[\"research-topic-impact\",48998]"}]}},"__N_SSG":true},"page":"/research-topics/[id]/[slug]/mag","query":{"id":"48998","slug":"the-effects-of-climate-change-and-anthropogenic-activities-on-patterns-structures-and-functions-of-terrestrial-ecosystems"},"buildId":"ZvyQJ1c6REyZAR__c3437","assetPrefix":"/_rtmag","isFallback":false,"gsp":true,"scriptLoader":[{"id":"google-analytics","strategy":"afterInteractive","children":"(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':\n new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],\n j=d.createElement(s),dl=l!='dataLayer'?'\u0026l='+l:'';j.async=true;j.src=\n 'https://tag-manager.frontiersin.org/gtm.js?id='+i+dl+ '\u0026gtm_auth=PYjuAXuPWCihEq8Nf7ErrA\u0026gtm_preview=env-1\u0026gtm_cookies_win=x';f.parentNode.insertBefore(j,f);\n })(window,document,'script','dataLayer','GTM-PT9D93K');"}]}