PeerJ:Forestryhttps://peerj.com/articles/index.atom?journal=peerj&subject=1439Forestry articles published in PeerJThe influence of stand composition and season on canopy structure and understory light environment in different subtropical montane Pinus massoniana forestshttps://peerj.com/articles/170672024-03-152024-03-15Peng JinMing XuQiupu YangJian Zhang
Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana—Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.
Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana—Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.Wood identification based on macroscopic images using deep and transfer learning approacheshttps://peerj.com/articles/170212024-02-282024-02-28Halime Ergun
Identifying forest types is vital for evaluating the ecological, economic, and social benefits provided by forests, and for protecting, managing, and sustaining them. Although traditionally based on expert observation, recent developments have increased the use of technologies such as artificial intelligence (AI). The use of advanced methods such as deep learning will make forest species recognition faster and easier. In this study, the deep network models RestNet18, GoogLeNet, VGG19, Inceptionv3, MobileNetv2, DenseNet201, InceptionResNetv2, EfficientNet and ShuffleNet, which were pre-trained with ImageNet dataset, were adapted to a new dataset. In this adaptation, transfer learning method is used. These models have different architectures that allow a wide range of performance evaluation. The performance of the model was evaluated by accuracy, recall, precision, F1-score, specificity and Matthews correlation coefficient. ShuffleNet was proposed as a lightweight network model that achieves high performance with low computational power and resource requirements. This model was an efficient model with an accuracy close to other models with customisation. This study reveals that deep network models are an effective tool in the field of forest species recognition. This study makes an important contribution to the conservation and management of forests.
Identifying forest types is vital for evaluating the ecological, economic, and social benefits provided by forests, and for protecting, managing, and sustaining them. Although traditionally based on expert observation, recent developments have increased the use of technologies such as artificial intelligence (AI). The use of advanced methods such as deep learning will make forest species recognition faster and easier. In this study, the deep network models RestNet18, GoogLeNet, VGG19, Inceptionv3, MobileNetv2, DenseNet201, InceptionResNetv2, EfficientNet and ShuffleNet, which were pre-trained with ImageNet dataset, were adapted to a new dataset. In this adaptation, transfer learning method is used. These models have different architectures that allow a wide range of performance evaluation. The performance of the model was evaluated by accuracy, recall, precision, F1-score, specificity and Matthews correlation coefficient. ShuffleNet was proposed as a lightweight network model that achieves high performance with low computational power and resource requirements. This model was an efficient model with an accuracy close to other models with customisation. This study reveals that deep network models are an effective tool in the field of forest species recognition. This study makes an important contribution to the conservation and management of forests.Navigating the complexities of the forest land sharing vs sparing logging dilemma: analytical insights through the governance theory of social-ecological systems dynamicshttps://peerj.com/articles/168092024-01-292024-01-29Jean-Baptiste Pichancourt
This study addresses the ongoing debate on forest land-sparing vs land-sharing, aiming to identify effective strategies for both species conservation and timber exploitation. Previous studies, guided by control theory, compared sharing and sparing by optimizing logging intensity along a presumed trade-off between timber yield and ecological outcomes. However, the realism of this trade-off assumption is questioned by ecological and governance theories. This article introduces a mathematical model of Social-Ecological System (SES) dynamics, distinguishing selective logging intensification between sharing and sparing, with associated governance requirements. The model assumes consistent rules for logging, replanting, conservation support, access regulation, socio-economic, soil and climate conditions. Actors, each specialized in sustainable logging and replanting of a single species, coexist with various tree species in the same space for land sharing, contrasting with separate actions on monospecific stands for sparing. In sharing scenarios, a gradient of intensification is created from 256 combinations of selective logging for a forest with eight coexisting tree species. This is compared with eight scenarios of monospecific stands adjacent to a spared eight-species forest area safeguarded from logging. Numerical projections over 100 years rank sparing and sharing options based on forest-level tree biodiversity, carbon storage, and timber yield. The findings underscore the context-specific nature of the problem but identify simple heuristics to optimize both sparing and sharing practices. Prioritizing the most productive tree species is effective when selecting sparing, especially when timber yield and biodiversity are benchmarks. Conversely, sharing consistently outperforms sparing when carbon storage and biodiversity are main criteria. Sharing excels across scenarios considering all three criteria, provided a greater diversity of actors access and coexist in the shared space under collective rules ensuring independence and sustainable logging and replanting. The present model addresses some limitations in existing sparing-sharing theory by aligning with established ecological theories exploring the intricate relationship between disturbance practices, productivity and ecological outcomes. The findings also support a governance hypothesis from the 2009 Nobel Prize in Economics (E. Ostrom) regarding the positive impact on biodiversity and productivity of increasing polycentricity, i.e., expanding the number of independent species controllers’ channels (loggers/replanters/supporters/regulators). This hypothesis, rooted in Ashby’s law of requisite variety from control theory, suggests that resolving the sharing/sparing dilemma may depend on our ability to predict the yield-ecology performances of sparing (in heterogeneous landscapes) vs of sharing (in the same space) from their respective levels of “polycentric requisite variety”.
This study addresses the ongoing debate on forest land-sparing vs land-sharing, aiming to identify effective strategies for both species conservation and timber exploitation. Previous studies, guided by control theory, compared sharing and sparing by optimizing logging intensity along a presumed trade-off between timber yield and ecological outcomes. However, the realism of this trade-off assumption is questioned by ecological and governance theories. This article introduces a mathematical model of Social-Ecological System (SES) dynamics, distinguishing selective logging intensification between sharing and sparing, with associated governance requirements. The model assumes consistent rules for logging, replanting, conservation support, access regulation, socio-economic, soil and climate conditions. Actors, each specialized in sustainable logging and replanting of a single species, coexist with various tree species in the same space for land sharing, contrasting with separate actions on monospecific stands for sparing. In sharing scenarios, a gradient of intensification is created from 256 combinations of selective logging for a forest with eight coexisting tree species. This is compared with eight scenarios of monospecific stands adjacent to a spared eight-species forest area safeguarded from logging. Numerical projections over 100 years rank sparing and sharing options based on forest-level tree biodiversity, carbon storage, and timber yield. The findings underscore the context-specific nature of the problem but identify simple heuristics to optimize both sparing and sharing practices. Prioritizing the most productive tree species is effective when selecting sparing, especially when timber yield and biodiversity are benchmarks. Conversely, sharing consistently outperforms sparing when carbon storage and biodiversity are main criteria. Sharing excels across scenarios considering all three criteria, provided a greater diversity of actors access and coexist in the shared space under collective rules ensuring independence and sustainable logging and replanting. The present model addresses some limitations in existing sparing-sharing theory by aligning with established ecological theories exploring the intricate relationship between disturbance practices, productivity and ecological outcomes. The findings also support a governance hypothesis from the 2009 Nobel Prize in Economics (E. Ostrom) regarding the positive impact on biodiversity and productivity of increasing polycentricity, i.e., expanding the number of independent species controllers’ channels (loggers/replanters/supporters/regulators). This hypothesis, rooted in Ashby’s law of requisite variety from control theory, suggests that resolving the sharing/sparing dilemma may depend on our ability to predict the yield-ecology performances of sparing (in heterogeneous landscapes) vs of sharing (in the same space) from their respective levels of “polycentric requisite variety”.Vegetation restoration improved aggregation stability and aggregated-associated carbon preservation in the karst areas of Guizhou Province, southwest Chinahttps://peerj.com/articles/166992024-01-222024-01-22Hui YangHui LongXuemei LiXiulong LuoYuanhang LiaoChangmin WangHua CaiYingge Shu
Background
The change in the soil carbon bank is closely related to the carbon dioxide in the atmosphere, and the vegetation litter input can change the soil organic carbon content. However, due to various factors, such as soil type, climate, and plant species, the effects of vegetation restoration on the soil vary. Currently, research on aggregate-associated carbon has focused on single vegetation and soil surface layers, and the changes in soil aggregate stability and carbon sequestration under different vegetation restoration modes and in deeper soil layers remain unclear. Therefore, this study aimed to explore the differences and relationships between stability and the carbon preservation capacity (CPC) under different vegetation restoration modes and to clarify the main influencing factors of aggregate carbon preservation.
Methods
Grassland (GL), shrubland (SL), woodland (WL), and garden plots (GP) were sampled, and they were compared with farmland (FL) as the control. Soil samples of 0–40 cm were collected. The soil aggregate distribution, aggregate-associated organic carbon concentration, CPC, and stability indicators, including the mean weight diameter (MWD), fractal dimension (D), soil erodibility (K), and geometric mean diameter (GMD), were measured.
Results
The results showed that at 0–40 cm, vegetation restoration significantly increased the >2 mm aggregate proportions, aggregate stability, soil organic carbon (SOC) content, CPC, and soil erosion resistance. The >2 mm fractions of the GL and SL were at a significantly greater proportion at 0–40 cm than that of the other vegetation types but the CPC was only significantly different between 0 and 10 cm when compared with the other vegetation types (P < 0.05). The >2 mm aggregates showed a significant positive correlation with the CPC, MWD, and GMD (P < 0.01), and there was a significant negative correlation with the D and K (P < 0.05). The SOC and CPC of all the vegetation types were mainly distributed in the 0.25–2 mm and <0.25 mm aggregate fractions. The MWD, GMD, SOC, and CPC all gradually decreased with increasing soil depth. Overall, the effects of vegetation recovery on soil carbon sequestration and soil stability were related to vegetation type, aggregate particle size, and soil depth, and the GL and SL restoration patterns may be more suitable in this study area. Therefore, to improve the soil quality and the sequestration of organic carbon and reduce soil erosion, the protection of vegetation should be strengthened and the policy of returning farmland to forest should be prioritized.
Background
The change in the soil carbon bank is closely related to the carbon dioxide in the atmosphere, and the vegetation litter input can change the soil organic carbon content. However, due to various factors, such as soil type, climate, and plant species, the effects of vegetation restoration on the soil vary. Currently, research on aggregate-associated carbon has focused on single vegetation and soil surface layers, and the changes in soil aggregate stability and carbon sequestration under different vegetation restoration modes and in deeper soil layers remain unclear. Therefore, this study aimed to explore the differences and relationships between stability and the carbon preservation capacity (CPC) under different vegetation restoration modes and to clarify the main influencing factors of aggregate carbon preservation.
Methods
Grassland (GL), shrubland (SL), woodland (WL), and garden plots (GP) were sampled, and they were compared with farmland (FL) as the control. Soil samples of 0–40 cm were collected. The soil aggregate distribution, aggregate-associated organic carbon concentration, CPC, and stability indicators, including the mean weight diameter (MWD), fractal dimension (D), soil erodibility (K), and geometric mean diameter (GMD), were measured.
Results
The results showed that at 0–40 cm, vegetation restoration significantly increased the >2 mm aggregate proportions, aggregate stability, soil organic carbon (SOC) content, CPC, and soil erosion resistance. The >2 mm fractions of the GL and SL were at a significantly greater proportion at 0–40 cm than that of the other vegetation types but the CPC was only significantly different between 0 and 10 cm when compared with the other vegetation types (P < 0.05). The >2 mm aggregates showed a significant positive correlation with the CPC, MWD, and GMD (P < 0.01), and there was a significant negative correlation with the D and K (P < 0.05). The SOC and CPC of all the vegetation types were mainly distributed in the 0.25–2 mm and <0.25 mm aggregate fractions. The MWD, GMD, SOC, and CPC all gradually decreased with increasing soil depth. Overall, the effects of vegetation recovery on soil carbon sequestration and soil stability were related to vegetation type, aggregate particle size, and soil depth, and the GL and SL restoration patterns may be more suitable in this study area. Therefore, to improve the soil quality and the sequestration of organic carbon and reduce soil erosion, the protection of vegetation should be strengthened and the policy of returning farmland to forest should be prioritized.Data science competition for cross-site individual tree species identification from airborne remote sensing datahttps://peerj.com/articles/165782023-12-212023-12-21Sarah J. GravesSergio MarconiDylan StewartIra HarmonBen WeinsteinYuzi KanazawaVictoria M. SchollMaxwell B. JosephJoseph McGlinchyLuke BrowneMegan K. SullivanSergio Estrada-VillegasDaisy Zhe WangAditya SinghStephanie BohlmanAlina ZareEthan P. White
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.
Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods’ ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46–0.55, macro F1 = 0.09–0.32, cross entropy loss = 2.4–9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07–0.32, macro F1 = 0.02–0.18, cross entropy loss = 2.8–16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.A taxonomic revision of Garcinia section Xanthochymus (Clusiaceae) in Thailandhttps://peerj.com/articles/165722023-12-202023-12-20Chatchai NgernsaengsaruayPichet ChantonMinta ChaiprasongsukNisa Leksungnoen
Garcinia section Xanthochymus (Clusiaceae) is revised for Thailand with four native species, i.e., G. dulcis, G. nervosa, G. prainiana, and G. xanthochymus. All species are described with updated morphological descriptions, illustrations, and an identification key, together with notes on distributions, distribution maps, habitats and ecology, phenology, conservation assessments, etymology, vernacular names, uses, and specimens examined. Four taxa, G. andamanica, G. andamanica var. pubescens, G. cambodgiensis and G. vilersiana, are synonymized under G. dulcis, and two taxa, G. nervosa var. pubescens and G. spectabilis, are newly synonymized under G. nervosa. Nine names are lectotypified: G. dulcis and its associated synonyms (G. cambodgiensis and G. vilersiana), G. nervosa and its associated synonyms (G. andersonii, G. nervosa var. pubescens, and G. spectabilis), G. prainiana, and G. xanthochymus. All species have a conservation assessment of Least Concern (LC). The fruits of all species are edible and have a sour or sweet-sour taste.
Garcinia section Xanthochymus (Clusiaceae) is revised for Thailand with four native species, i.e., G. dulcis, G. nervosa, G. prainiana, and G. xanthochymus. All species are described with updated morphological descriptions, illustrations, and an identification key, together with notes on distributions, distribution maps, habitats and ecology, phenology, conservation assessments, etymology, vernacular names, uses, and specimens examined. Four taxa, G. andamanica, G. andamanica var. pubescens, G. cambodgiensis and G. vilersiana, are synonymized under G. dulcis, and two taxa, G. nervosa var. pubescens and G. spectabilis, are newly synonymized under G. nervosa. Nine names are lectotypified: G. dulcis and its associated synonyms (G. cambodgiensis and G. vilersiana), G. nervosa and its associated synonyms (G. andersonii, G. nervosa var. pubescens, and G. spectabilis), G. prainiana, and G. xanthochymus. All species have a conservation assessment of Least Concern (LC). The fruits of all species are edible and have a sour or sweet-sour taste.Carbon mapping in pine-oak stands under timber management in southern Mexicohttps://peerj.com/articles/164312023-12-152023-12-15Ashmir Ambrosio-LazoGerardo Rodríguez-OrtizJoaquín Alberto Rincón-RamírezVicente Arturo Velasco-VelascoJosé Raymundo Enríquez-del ValleJudith Ruiz-Luna
The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.
The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.Impact of livestock grazing management on carbon stocks: a case study in sparse elm woodlands of semi-arid landshttps://peerj.com/articles/166292023-12-122023-12-12Yi Tang
Livestock grazing is a widespread practice in human activities worldwide. However, the effects of livestock grazing management on vegetation carbon storage have not been thoroughly evaluated. In this study, we used the system dynamic approach to simulate the effects of different livestock grazing management strategies on carbon stock in sparse elm woodlands. The livestock grazing management strategies included rotational grazing every 5 years (RG5), prohibited grazing (PG), seasonal prohibited grazing (SPG), and continuous grazing (CG). We evaluated the carbon sequestration rate in vegetation using logistical models. The results showed that the carbon stock of elm trees in sparse woodlands was 5–15 M g ha−1. The values of the carbon sequestration rate were 0.15, 0.13, 0.13, and 0.09 Mg C ha−1 year−1 in RG5, PG, CG, and SPG management, respectively. This indicates that rotational grazing management might be the optimal choice for improving vegetation carbon accumulation in sparse woodlands. This study contributes to decision-making on how to choose livestock grazing management to maintain higher carbon storage.
Livestock grazing is a widespread practice in human activities worldwide. However, the effects of livestock grazing management on vegetation carbon storage have not been thoroughly evaluated. In this study, we used the system dynamic approach to simulate the effects of different livestock grazing management strategies on carbon stock in sparse elm woodlands. The livestock grazing management strategies included rotational grazing every 5 years (RG5), prohibited grazing (PG), seasonal prohibited grazing (SPG), and continuous grazing (CG). We evaluated the carbon sequestration rate in vegetation using logistical models. The results showed that the carbon stock of elm trees in sparse woodlands was 5–15 M g ha−1. The values of the carbon sequestration rate were 0.15, 0.13, 0.13, and 0.09 Mg C ha−1 year−1 in RG5, PG, CG, and SPG management, respectively. This indicates that rotational grazing management might be the optimal choice for improving vegetation carbon accumulation in sparse woodlands. This study contributes to decision-making on how to choose livestock grazing management to maintain higher carbon storage.Ground beetle assemblages inhabiting various age classes of Norway spruce stands in north-eastern Polandhttps://peerj.com/articles/165022023-12-012023-12-01Mariusz NietupskiAgnieszka KosewskaEmilia Ludwiczak
Assemblages of epigeic ground beetles living in Norway spruce forests in north-eastern Poland in three age ranges: young: 20–30 years (A); middle-aged: 40–50 years (B); old: 70–80 years (C) were investigated. In each age category, 4 plots with 5 Barber traps were set up. Ground beetle assemblages were compared in terms of their abundance, species richness, and the Shannon H’ index value. Quantitative ecological description of the carabids captured in the analysed age-classes of Norway spruce forests was performed, and the values of the mean individual biomass (MIB) were calculated. To determine the correlation between mean individual biomass and abundance of various ecological groups of carabid beetles, the Spearman rank correlation coefficient was calculated. The assemblages of ground beetles living in the Norway spruce forests in north-eastern Poland were characterised by quite large species richness (44 species in total). There were significant differences in species richness among the different ages of Norway spruce forests. The oldest Norway spruce stands (70–80 years old) had a smaller number of species and specimens of ground beetles as well as the highest MIB values in comparison with the younger spruce forests A and B. The results revealed that high MIB values were positively correlated with the presence of large ground beetle species with higher moisture requirements. Lower values of the MIB index were due to the presence of smaller open habitat macropterous species, with the spring type of breeding and associated with open areas.
Assemblages of epigeic ground beetles living in Norway spruce forests in north-eastern Poland in three age ranges: young: 20–30 years (A); middle-aged: 40–50 years (B); old: 70–80 years (C) were investigated. In each age category, 4 plots with 5 Barber traps were set up. Ground beetle assemblages were compared in terms of their abundance, species richness, and the Shannon H’ index value. Quantitative ecological description of the carabids captured in the analysed age-classes of Norway spruce forests was performed, and the values of the mean individual biomass (MIB) were calculated. To determine the correlation between mean individual biomass and abundance of various ecological groups of carabid beetles, the Spearman rank correlation coefficient was calculated. The assemblages of ground beetles living in the Norway spruce forests in north-eastern Poland were characterised by quite large species richness (44 species in total). There were significant differences in species richness among the different ages of Norway spruce forests. The oldest Norway spruce stands (70–80 years old) had a smaller number of species and specimens of ground beetles as well as the highest MIB values in comparison with the younger spruce forests A and B. The results revealed that high MIB values were positively correlated with the presence of large ground beetle species with higher moisture requirements. Lower values of the MIB index were due to the presence of smaller open habitat macropterous species, with the spring type of breeding and associated with open areas.Water-use characteristics of Syzygium antisepticum and Adinandra integerrima in a secondary forest of Khao Yai National Park in Thailand with implications for environmental managementhttps://peerj.com/articles/165252023-11-302023-11-30Ratchanon AmpornpitakAnuttara NathalangPantana Tor-ngern
Background
Southeast Asia has experienced widespread deforestation and change in land use. Consequently, many reforestation projects have been initiated in this region. However, it is imperative to carefully choose the tree species for planting, especially in light of the increasing climate variability and the potential alteration of plantation on the watershed water balance. Thus, the information regarding water-use characteristics of various tree species and sizes is critical in the tree species selection for reforestation.
Methods
We estimated tree water use (T) of dominant species including Syzygium antisepticum and Adinandra integerrima, hereafter Sa and Ai, respectively, in a secondary tropical forest in Khao Yai National Park, Thailand, using sap flow data, and compared T between species and size classes. Additionally, we evaluated the responses of T of both species in each size class to environmental factors including soil moisture and vapor pressure deficit (VPD).
Results
Results showed consistently higher T in Sa compared to Ai across ranges of VPD and soil moisture. Under low soil moisture, T of Sa responded to VPD, following a saturating exponential pattern while Ai maintained T across different VPD levels, irrespective of tree size. No responses of T to VPD were observed in either species when soil water was moderate. When soil moisture was high, T of both species significantly increased and saturated at high VPD, albeit the responses were less sensitive in large trees. Our results imply that Ai may be suitable for reforestation in water-limited areas where droughts frequently occur to minimize reforestation impact on water availability to downstream ecosystems. In contrast, Sa should be planted in regions with abundant and reliable water resources. However, a mixed species plantation should be generally considered to increase forest resilience to increasing climate variation.
Background
Southeast Asia has experienced widespread deforestation and change in land use. Consequently, many reforestation projects have been initiated in this region. However, it is imperative to carefully choose the tree species for planting, especially in light of the increasing climate variability and the potential alteration of plantation on the watershed water balance. Thus, the information regarding water-use characteristics of various tree species and sizes is critical in the tree species selection for reforestation.
Methods
We estimated tree water use (T) of dominant species including Syzygium antisepticum and Adinandra integerrima, hereafter Sa and Ai, respectively, in a secondary tropical forest in Khao Yai National Park, Thailand, using sap flow data, and compared T between species and size classes. Additionally, we evaluated the responses of T of both species in each size class to environmental factors including soil moisture and vapor pressure deficit (VPD).
Results
Results showed consistently higher T in Sa compared to Ai across ranges of VPD and soil moisture. Under low soil moisture, T of Sa responded to VPD, following a saturating exponential pattern while Ai maintained T across different VPD levels, irrespective of tree size. No responses of T to VPD were observed in either species when soil water was moderate. When soil moisture was high, T of both species significantly increased and saturated at high VPD, albeit the responses were less sensitive in large trees. Our results imply that Ai may be suitable for reforestation in water-limited areas where droughts frequently occur to minimize reforestation impact on water availability to downstream ecosystems. In contrast, Sa should be planted in regions with abundant and reliable water resources. However, a mixed species plantation should be generally considered to increase forest resilience to increasing climate variation.