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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (2): 225-244    DOI: 10.1007/s40333-022-0006-z     CSTR: 32276.14.s40333-022-0006-z
Research article     
Dieback intensity but not functional and taxonomic diversity indices predict forest productivity in different management conditions: Evidence from a semi-arid oak forest ecosystem
Mona KARAMI1, Mehdi HEYDARI2, Ali SHEYKHOLESLAMI1,*(), Majid ESHAGH NIMVARI1, Reza OMIDIPOUR3, YUAN Zuoqiang4, Bernard PREVOSTO5
1Faculty of Natural Resources, Chalus Branch, Islamic Azad University, Chalus 46615/397, Iran
2Department of Forestry, Chalous Branch, Islamic Azad University, Chalous 46615/397, Iran
3Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran
4Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
5INRAE, Aix Marseille University, UMR RECOVER, Mediterranean Ecosystems and Risks, Aix-en-Provence 13128, France
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Abstract  

The relationships between different aspects of diversity (taxonomic, structural and functional) and the aboveground biomass (AGB) as a major component of global carbon balance have been studied extensively but rarely under the simultaneous influence of forest dieback and management. In this study, we investigate the relationships between taxonomic, functional and structural diversity of woody species (trees and shrubs) and AGB along a gradient of dieback intensity (low, moderate, high and no dieback as control) under two contrasted management conditions (protection by central government vs. traditional management by natives) in a semi-arid oak (Quercus brantii Lindl.) forest ecosystem. AGB was estimated and taxonomic diversity, community weighted average (CWM) and functional divergence indices were produced. We found that the aerial biomass was significantly higher in the intensively used area (14.57 (±1.60) t/hm2) than in the protected area (8.70 (±1.05) t/hm2) due to persistence of some large trees but with decreasing values along the dieback intensity gradient in both areas. CWM of height (H), leaf nitrogen content (LNC) and leaf dry matter content (LDMC) were also higher in the traditional managed area than in the protected area. In contrast, in the protected area, the woody species diversity was higher and the inter-specific competition was more intense, explaining a reduced H, biomass and LDMC. Contrary to the results of CWM, none of the functional diversity traits (FDvar) was affected by dieback intensity and only FDvar values of LNC, leaf phosphorus content (LPC) and LDMC were influenced by management. We also found significantly positive linear relationships of AGB with CWM and FDvar indices in the protected area, and with taxonomic and structural diversity indices in the traditional managed area. These results emphasize that along a dieback intensity gradient, the leaf functional traits are efficient predictors in estimating the AGB in protected forests, while taxonomic and structural indices provide better results in forests under a high human pressure. Finally, species identity of the dominant species (i.e., Brant's oak) proves to be the main driver of AGB, supporting the selection effect hypothesis.

Key words： environmental stress      sudden oak dieback      degradation      conservation      selection effect hypothesis     
Received: 02 December 2021      Published: 28 February 2022
Corresponding Authors: *Ali SHEYKHOLESLAMI (E-mail: islamiali@iauc.ac.ir)
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Mona KARAMI
Mehdi HEYDARI
Ali SHEYKHOLESLAMI
Majid ESHAGH NIMVARI
Reza OMIDIPOUR
YUAN Zuoqiang
Bernard PREVOSTO
Cite this article:

Mona KARAMI, Mehdi HEYDARI, Ali SHEYKHOLESLAMI, Majid ESHAGH NIMVARI, Reza OMIDIPOUR, YUAN Zuoqiang, Bernard PREVOSTO. Dieback intensity but not functional and taxonomic diversity indices predict forest productivity in different management conditions: Evidence from a semi-arid oak forest ecosystem. Journal of Arid Land, 2022, 14(2): 225-244.

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http://jal.xjegi.com/10.1007/s40333-022-0006-z     OR     http://jal.xjegi.com/Y2022/V14/I2/225

Fig. 1 (a), location of the study area in the Ilam Province, Iran; (b), map of the two studied areas; views of the traditional managed area (c, d) and protected area (e, f).
Dieback intensity class
≥65% 36%-65% 10%-35% ≤10%
High intensity dieback Moderate intensity dieback Low intensity dieback No dieback or control
Signs of canopy dryness can be seen all over the tree canopy and in total about 3/4 of the tree canopy has dried; symptoms such as deep cracks, numerous holes, decay and disease can be seen all over the trunk, and sometimes half of the trunk is gone. Signs of canopy dryness can be seen in the tree canopy and in total about 1:2 of the tree canopy has dried; symptoms such as cracks, decay and disease can be seen in parts of the trunk. Signs of canopy dryness can be seen in the tree canopy and in total about 1:4 of the tree canopy has dried; there are no other symptoms such as cracks, decay and disease in the trunk. In late spring, there are no signs of canopy dryness in the tree and the crown and only few dry branches are seen.
Table 1 Dieback intensity classes
Fig. 2 Trees in the four classes of dieback intensity. (a) no dieback; (b) low dieback; (c) moderate dieback; (d) high dieback.
Fig. 3 Mean aboveground biomass values under different dieback intensity classes (control, low, moderate and high dieback) in the traditional managed (a) and protected (b) areas. Different lowercase letters indicate significant differences among different dieback intensity classes within the same area at P<0.05 level.
df CWM of LPC CWM of LNC CWM of H
F P F P F P
Management (A) 1 117.60 <0.001 148,042 <0.001 10,098 <0.001
Dieback intensity (B) 3 3.55 0.019 6.79 <0.001 6.89 <0.001
A×B 3 3.55 0.019 7.31 <0.001 7.27 <0.001
df CWM of LDMC CWM of SLA CWM of WD
F P F P F P
Management (A) 1 167,215 <0.001 446,409 0.001 4.02 0.049
Dieback intensity (B) 3 7.01 <0.001 3.72 0.015 4.77 0.004
A×B 3 7.22 <0.001 3.89 0.012 6.55 0.001
df FDvar of LPC FDvar of LNC FDvar of H
F P F P F P
Protection (A) 1 17.170 <0.001 7.370 0.009 5.930 0.190
Dieback intensity (B) 3 0.152 0.928 1.120 0.351 1.130 0.348
A×B 3 0.216 0.806 0.137 0.872 0.002 0.998
df FDvar of LDMC FDvar of SLA FDvar of WD
F F P F P
Protection (A) 1 8.32 0.238 0.628 1.170 0.282
Dieback intensity (B) 3 0.56 0.728 0.540 2.780 0.052
A×B 3 0.51 0.088 0.916 0.278 0.758
Table 2 Results of the generalized linear model with protection, dieback intensity and their interaction on CWM and FDvar functional traits
Functional diversity index Traditional managed Protected
No dieback or control Low intensity dieback Moderate intensity dieback High intensity dieback No dieback
or control		 Low intensity dieback Moderate intensity dieback High intensity dieback
CWM of LPC 0.238±0.00c 0.24±0.00c 0.239±0.00c 0.240±0.00c 1.114±0.13b 1.985±0.27a 1.834±0.28a 1.174±0.23b
CWM of LNC 2.72±0.014b 2.85±0.000a 2.76±0.020b 2.49±0.008c 0.24±0.001d 0.24±0.001d 0.24±0.001d 0.24±0.001d
CWM of H 9.02±0.150c 9.29±0.000b 9.36±0.210b 9.72±0.080a 2.77±0.006d 2.74±0.006d 2.74±0.012d 2.77±0.007d
CWM of
LDMC		 570.44±3.11d 586.66±0.00a 577.53±4.29c 580.93±1.72b 9.27±0.06e 8.96±0.13e 9.05±0.14e 9.35±0.08e
CWM of SLA 7.52±0.04c 7.74±0.00c 7.62±0.05c 7.72±0.02c 581.11±0.80a 574.93±1.90b 575.98±2.40b 581.25±1.20a
CWM of WD 0.81±0.012e 0.87±0.000a 0.83±0.016cde 0.86±0.006ab 0.84±0.004bcd 0.82±0.009de 0.82±0.01de 0.85±0.006abc
FDvar of LPC 1.00±0.00a - 1.000±0.00a 1.000±0.00a 0.341±0.29b 0.165±0.09b 0.171±0.09b 0.057±0.01b
FDvar of LNC 0.016±0.00d - 0.018±0.00d 0.015±0.00d 3.01±0.87ab 1.51±1.02c 2.61±1.10bc 4.40±1.10a
FDvar of H 0.191±0.019 - 0.222±0.030 0.195±0.001 0.327±0.029 0.415±0.051 0.356±0.042 0.327±0.050
FDvar of
LDMC		 0.016±0.00b - 0.018±0.00b 0.016±0.00b 3.203±0.71a 3.309±1.29a 3.308±1.13a 4.254±0.99a
FDvar of SLA 0.018±0.002 - 0.020±0.003 0.017±0.002 0.044±0.007 0.466±0.392 0.065±0.009 0.338±0.207
FDvar of WD 0.156±0.017 - 0.181±0.026 0.158±0.013 0.175±0.028 0.460±0.173 0.247±0.032 0.357±0.111
Table 3 Comparison of mean of CWM and FDvar of functional traits under different dieback intensities in the traditional managed and protected areas
Source of variation df Species richness Shannon index Evenness index
F P F P F P
Protection (A) 1 193.02 <0.001 32.82 <0.001 744.06 <0.001
Dieback intensity (B) 3 3.12 0.031 6.13 0.001 7.48 <0.001
A×B 3 2.75 0.049 7.49 <0.001 2.97 0.038
Source of variation df Mingling index Height differentiation Diameter differentiation
F P F P F P
Protection (A) 1 316.04 <0.001 689.90 <0.001 832.03 <0.001
Dieback intensity (B) 3 9.78 <0.001 18.12 <0.001 28.15 <0.001
A×B 3 2.02 0.118 7.32 <0.001 13.65 <0.001
Table 4 Effect of management, dieback intensity and their interaction on taxonomic (richness, evenness and Shannon-Wiener diversity) and structural diversity (mingling, height differentiation and diameter differentiation) indices
Fig. 4 Mean values of the taxonomic diversity (diversity (a, b), richness (c, d) and evenness (e, f)) and structural diversity (height and diameter differentiation indices (g-j) and mingling (k, l)) indices under different dieback intensity classes (control, low, moderate and high) in the traditional managed and protected areas. Different lowercase letters indicate significant differences among different dieback intensity classes within the same area at P<0.05 level.
Fig. 5 Relationships between aboveground biomass and functional diversity (CWM (a-e) and Fdvar (f)), taxonomic diversity (structural diversity (diameter differentiation, g), richness (h) and evenness (i)) indices in protected (green confidence interval) and traditional managed areas (red confidence interval)
Fig. 6 Relative importance of different variables in each category of plant diversity, stand structure and trait composition of woody species in the traditional managed (a) and protected (b) area; The first and second best parameters for SEM (structural equation modeling) are indicated by green and blue colors. S, richness; E, evenness; H, Shannon-Wiener diversity; MI, mingling index; HD, height differentiation; DD, diameter differentiation; LPC, leaf phosphorus content; LNC, leaf nitrogen content; H, height; LDMC, leaf dry matter content; SLA, specific leaf area; WD, wood density.
Fig. 7 The best-fit structural equation model linking dieback intensity, taxonomic diversity, stand structural indices and functional trait composition to aboveground biomass of woody species in the traditional managed (a) the protected (b) area. Solid and dashed arrows indicate significant and non-significant paths. For each path, the standardized regression coefficients are indicated by the adjacent values. Chi, chi-square test; R2, coefficient of determination; CFI, comparative fit index; RMSEA, root mean square error of approximation. *, ** and *** indicate significant differences at P<0.05, P<0.01 and P<0.001 levels, respectively.
[1]   Ali A, Yan E R. 2017. Functional identity of overstorey tree height and understorey conservative traits drive aboveground biomass in a subtropical forest. Ecological Indicators, 83:158-168.
doi: 10.1016/j.ecolind.2017.07.054
[2]   Ali A, Lin S L, He J K, et al. 2019a. Big-sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests. Global Change Biology, 25(8):2810-2824.
doi: 10.1111/gcb.2019.25.issue-8
[3]   Ali A, Chen H Y, You W H, et al. 2019b. Multiple abiotic and biotic drivers of aboveground biomass shift with forest stratum. Forest Ecology and Management, 436:1-10.
doi: 10.1016/j.foreco.2019.01.007
[4]   Anderson J M, Ingram J S I. 1993. Tropical Soil Biology and Fertility: A Handbook of Methods. Wallingford: CAB International, 221.
[5]   Artz R R, Reid E, Anderson I C, et al. 2009. Long term repeated prescribed burning increases evenness in the basidiomycetelac case gene pool in forest soils. FEMS Microbiology Ecology, 67(3):397-410.
doi: 10.1111/fem.2009.67.issue-3
[6]   Attarod P, Sadeghi S M M, Pypker T G, et al. 2017. Oak trees decline; a sign of climate variability impacts in the west of Iran. Caspian Journal of Environmental Sciences, 15(4):373-384.
[7]   Ayma-Romay A I, Bown H E. 2019. Biomass and dominance of conservative species drive above-ground biomass productivity in a mediterranean-type forest of Chile. Forest Ecosystems, 6(1):1-13.
doi: 10.1186/s40663-019-0161-7
[8]   Azad M S, Kamruzzaman M, Osawa A. 2020. Quantification and understanding of above and belowground biomass in medium saline zone of the Sundarbans, Bangladesh: the relationships with forest attributes. Journal of Sustainable Forestry, 39(4):331-345.
doi: 10.1080/10549811.2019.1664307
[9]   Balima L H, Nacoulma B M I, Bayen P, et al. 2020. Agricultural land use reduces plant biodiversity and carbon storage in tropical West African savanna ecosystems: Implications for sustainability. Global Ecology and Conservation, 21:e00875, doi: 10.1016/j.gecco.2019.e00875.
doi: 10.1016/j.gecco.2019.e00875
[10]   Barlow J, Peres C A, Lagan B O, et al. 2003. Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecology letters, 6(1):6-8.
doi: 10.1046/j.1461-0248.2003.00394.x
[11]   Barton K. 2016. MuMIn: Multi-model Inference. R package version 1.15.6. [2021-04-10]. https://CRAN.R-project.org/package=MuMIn .
[12]   Bílá K, Moretti M, de Bello F, et al. 2014. Disentangling community functional components in a litter‐macrodetritivore model system reveals the predominance of the mass ratio hypothesis. Ecology and evolution, 4(4):408-416.
doi: 10.1002/ece3.2014.4.issue-4
[13]   Blaum N, Seymour C, Rossmanith E, et al. 2009. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodiversity and Conservation, 18(5):1187-1199.
doi: 10.1007/s10531-008-9498-x
[14]   Bochet E, Molina M J, Monleón V, et al. 2021. Interactions of past human disturbance and aridity trigger abrupt shifts in the functional state of Mediterranean holm oak woodlands. CATENA, 206:105514, doi: 10.1016/j.catena.2021.105514.
doi: 10.1016/j.catena.2021.105514
[15]   Bohn F J, Huth A. 2017. The importance of forest structure to biodiversity-productivity relationships. Royal Society Open Science, 4(1):160521, doi: 10.1098/rsos.160521.
doi: 10.1098/rsos.160521
[16]   Boukili V K, Chazdon R L. 2017. Environmental filtering, local site factors and landscape context drive changes in functional trait composition during tropical forest succession. Perspectives in Plant Ecology, Evolution and Systematics, 24:37-47.
doi: 10.1016/j.ppees.2016.11.003
[17]   Bradstreet R B. 1965. The Kjeldahl Method for Organic Nitrogen. New York: Academic Press, 239.
[18]   Brancalion P H, Campoe O, Mendes J C T, et al. 2019. Intensive silviculture enhances biomass accumulation and tree diversity recovery in tropical forest restoration. Ecological Applications, 29(2):1847, doi: doi.org/10.1002/eap.1847.
doi: doi.org/10.1002/eap.1847
[19]   Brasier C M, Scott J K. 1994. European oak declines and global warming: a theoretical assessment with special reference to the activity of Phytophthora cinnamomi. EPPO Bulletin, 24(1):221-232.
doi: 10.1111/j.1365-2338.1994.tb01063.x
[20]   Chai Y, Yue M, Wang M, et al. 2016. Plant functional traits suggest a change in novel ecological strategies for dominant species in the stages of forest succession. Oecologia, 180(3):771-783.
doi: 10.1007/s00442-015-3483-3
[21]   Chaturvedi R K, Raghubanshi A S, Singh J S. 2012. Effect of grazing and harvesting on diversity, recruitment and carbon accumulation of juvenile trees in tropical dry forests. Forest Ecology and Management, 284:152-162.
doi: 10.1016/j.foreco.2012.07.053
[22]   Chaturvedi R K, Raghubanshi A S. 2015. Assessment of carbon density and accumulation in mono-and multi-specific stands in Teak and Sal forests of a tropical dry region in India. Forest Ecology and Management, 339:11-21.
doi: 10.1016/j.foreco.2014.12.002
[23]   Chaturvedi R K, Raghubanshi A S, Tomlinson K W, et al. 2017a. Impacts of human disturbance in tropical dry forests increase with soil moisture stress. Journal of Vegetation Science, 28(5):997-1007.
doi: 10.1111/jvs.12547
[24]   Chaturvedi R K, Raghubanshi A S, Singh J S. 2017b. Sapling harvest: A predominant factor affecting future composition of tropical dry forests. Forest Ecology and Management, 384:221-235.
doi: 10.1016/j.foreco.2016.10.026
[25]   Chaturvedi R K, Tripathi A, Raghubanshi , et al. 2021. Functional traits indicate a continuum of tree drought strategies across a soil water availability gradient in a tropical dry forest. Forest Ecology and Management, 482:118740, doi: 10.1016/j.foreco.2020.118740.
doi: 10.1016/j.foreco.2020.118740
[26]   Chave J, Réjou-Méchain M, Búrquez A, et al. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global change biology, 20(10):3177-3190.
doi: 10.1111/gcb.2014.20.issue-10
[27]   Chiang J M, Spasojevic M J, Muller-Landau H C, et al. 2016. Functional composition drives ecosystem function through multiple mechanisms in a broadleaved subtropical forest. Oecologia, 182(3):829-840.
doi: 10.1007/s00442-016-3717-z
[28]   Choi I Y. 2011. First report of bark dieback on blueberry caused by Botryosphaeria dothidea in Korea. Plant disease, 95(2):227-227.
doi: 10.1094/PDIS-05-10-0371 pmid: 30743439
[29]   Connell J H. 1978. Diversity in tropical rain forests and coral reefs. Science, 199(4335):1302-1310.
pmid: 17840770
[30]   Cornelissen J H C, Cerabolini B, Castro‐Díez P, et al. 2003. Functional traits of woody plants: correspondence of species rankings between field adults and laboratory‐grown seedlings?. Journal of Vegetation Science, 14(3):311-322.
doi: 10.1111/jvs.2003.14.issue-3
[31]   Dănescu A, Albrecht A T, Bauhus J. 2016. Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany. Oecologia, 182(2):319-333.
doi: 10.1007/s00442-016-3623-4 pmid: 27059713
[32]   Díaz S, Lavorel S, Chapin F S, et al. 2007. Functional diversity-at the crossroads between ecosystem functioning and environmental filters. In: Canadell J, Pataki D, Pitelka L. Terrestrial Ecosystems in a Changing World. Heidelberg: Springer, 81-91.
[33]   Erfanzadeh R, Omidipour R, Faramarzi M. 2015. Variation of plant diversity components in different scales in relation to grazing and climatic conditions. Plant Ecology and Diversity, 8(4):537-545.
doi: 10.1080/17550874.2015.1033774
[34]   Eslaminejad P, Heydari M, Kakhki F, et al. 2020. Plant species and season influence soil physicochemical properties and microbial function in a semi-arid woodland ecosystem. Plant and Soil, 456(1):43-59.
doi: 10.1007/s11104-020-04691-1
[35]   Fichtner A, Härdtle W, Li Y, et al. 2017. From competition to facilitation: how tree species respond to neighborhood diversity. Ecology Letters, 20(7):892-900.
doi: 10.1111/ele.12786 pmid: 28616871
[36]   Finegan B, Peña-Claros M, de Oliveira A, et al. 2015. Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses. Journal of Ecology, 103(1):191-201.
doi: 10.1111/1365-2745.12346
[37]   García-Girón J, Heino J, Baastrup-Spohr L, et al. 2020. Global patterns and determinants of lake macrophyte taxonomic, functional and phylogenetic beta diversity. Science of the Total Environment, 723:138021, doi: 10.1016/j.scitotenv.2020.138021.
doi: 10.1016/j.scitotenv.2020.138021
[38]   Garnier E, Cortez J, Billès G, et al. 2004. Plant functional markers capture ecosystem properties during secondary succession. Ecology, 85(9):2630-2637.
doi: 10.1890/03-0799
[39]   Goodarzi M, Pourhashemi M, Azizi Z. 2019. Investigation on Zagros forests cover changes under the recent droughts using satellite imagery. Journal of Forest Science, 65(1):9-17.
doi: 10.17221/JFS
[40]   Grime J P. 1973. Competitive exclusion in herbaceous vegetation. Nature, 242(5396):344-347.
doi: 10.1038/242344a0
[41]   Grime J P. 1998. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. Journal of Ecology, 86(6):902-910.
doi: 10.1046/j.1365-2745.1998.00306.x
[42]   Grossiord C, Granier A, Ratcliffe S, et al. 2014a. Tree diversity does not always improve resistance of forest ecosystems to drought. Proceedings of the National Academy of Sciences, 111(41):14812-14815.
[43]   Grossiord C, Granier A, Gessler A, et al. 2014b. Does drought influence the relationship between biodiversity and ecosystem functioning in boreal forests? Ecosystems, 17(3):394-404.
doi: 10.1007/s10021-013-9729-1
[44]   Henry M, Besnard A, Asante W A, et al. 2010. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. Forest Ecology and Management, 260(8):1375-1388.
doi: 10.1016/j.foreco.2010.07.040
[45]   Heydari M, Salehi, A, Mahdavi A, et al. 2012. Effects of different fire severity levels on soil chemical and physical properties in Zagros forests of western Iran. Folia Forestalia Polonica, Series A-Forestry, 54(4):241-250.
[46]   Heydari M, Poorbabaei H, Bazgir M, et al. 2014. Earthworms as indicators for different forest management types and human disturbance in Ilam oak forest, Iran. Folia Forestalia Polonica. Series A-Forestry, 56(3):121-134.
[47]   Heydari M, Omidipour R, Abedi M, et al. 2017a. Effects of fire disturbance on alpha and beta diversity and on beta diversity components of soil seed banks and aboveground vegetation. Plant Ecology and Evolution, 150(3):247-256.
doi: 10.5091/plecevo
[48]   Heydari M, Prévosto B, Abdi T, et al. 2017b. Establishment of oak seedlings in historically disturbed sites: Regeneration success as a function of stand structure and soil characteristics. Ecological Engineering, 107:172-182.
doi: 10.1016/j.ecoleng.2017.07.016
[49]   Heydari M, Moradizadeh H, Omidipour R, et al. 2020. Spatio-temporal changes in the understory heterogeneity, diversity, and composition after fires of different severities in a semiarid oak (Quercus brantii Lindl.) forest. Land Degradation and Development, 31(8):1039-1049.
doi: 10.1002/ldr.v31.8
[50]   Hogg E H, Brandt J P, Michaelian M. 2008. Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests. Canadian Journal of Forest Research, 38(6):1373-1384.
doi: 10.1139/X08-001
[51]   Hulvey K B, Hobbs R J, Standish R J, et al. 2013. Benefits of tree mixes in carbon plantings. Nature Climate Change, 3(10):869-874.
doi: 10.1038/nclimate1862
[52]   Inagaki M, Tange T. 2014. Nutrient accumulation in aboveground biomass of planted tropical trees: a meta-analysis. Soil Science and Plant Nutrition, 60(4):598-608.
doi: 10.1080/00380768.2014.929025
[53]   Jactel H, Bauhus J, Boberg J, et al. 2017. Tree diversity drives forest stand resistance to natural disturbances. Current Forestry Reports, 3(3):223-243.
doi: 10.1007/s40725-017-0064-1
[54]   Jin X M, Han , G D. 2010. Effects of grazing intensity on species diversity and structure of meadow steppe community. Pratacultural Science, 27(4):7-10. (in Chinese)
[55]   Juřička D, Novotná J, Houška J, et al. 2020. Large-scale permafrost degradation as a primary factor in Larixsibirica forest dieback in the Khentii massif, northern Mongolia. Journal of Forestry Research, 31(1):197-208.
doi: 10.1007/s11676-018-0866-4
[56]   Kamata N, Esaki K, Kato K, et al. 2002. Potential impact of global warming on deciduous oak dieback caused by ambrosia fungus Raffaelea sp. carried by ambrosia beetle Platypus quercivorus (Coleoptera: Platypodidae) in Japan. Bulletin of Entomological Research, 92:119-126.
pmid: 12020369
[57]   Kardol P, Campany C E, Souza L, et al. 2010. Climate change effects on plant biomass alter dominance patterns and community evenness in an experimental old-field ecosystem. Global Change Biology, 16(10):2676-2687.
doi: 10.1111/gcb.2010.16.issue-10
[58]   Keesing F, Holt R D, Ostfeld R S. 2006. Effects of species diversity on disease risk. Ecology letters, 9(4):485-498.
pmid: 16623733
[59]   König P, Tautenhahn S, Cornelissen J H C, et al. 2018. Advances in flowering phenology across the Northern Hemisphere are explained by functional traits. Global Ecology and Biogeography, 27(3):310-321.
doi: 10.1111/geb.2018.27.issue-3
[60]   Kraft N J, Valencia R, Ackerly D D. 2008. Functional traits and niche-based tree community assembly in an Amazonian forest. Science, 322(5901):580-582.
doi: 10.1126/science.1160662
[61]   Lasky J R, Uriarte M, Boukili V K, et al. 2014. The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession. Ecology letters, 17(9):1158-1167.
doi: 10.1111/ele.2014.17.issue-9
[62]   Levenbach S. 2009. Grazing intensity influences the strength of an associational refuge on temperate reefs. Oecologia, 159(1):181-190.
doi: 10.1007/s00442-008-1186-8 pmid: 18975012
[63]   Liu Q, Buyantuev A, Wu J, et al. 2018. Intensive land-use drives regional-scale homogenization of plant communities. Science of the Total Environment, 644:806-814.
doi: 10.1016/j.scitotenv.2018.07.019
[64]   Lloret F, Siscart D, Dalmases C. 2004. Canopy recovery after drought dieback in holm‐oak Mediterranean forests of Catalonia (NE Spain). Global Change Biology, 10(12):2092-2099.
doi: 10.1111/gcb.2004.10.issue-12
[65]   Ma X, Mahecha M D, Migliavacca M, et al. 2019. Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sensing of Environment, 233:111368, doi: 10.1016/j.rse.2019.111368.
doi: 10.1016/j.rse.2019.111368
[66]   Mason N W, Mouillot D, Lee W G, et al. 2005. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111(1):112-118.
doi: 10.1111/oik.2005.111.issue-1
[67]   Menezes R S C, Sales A T, Primo D C, et al. 2021. Soil and vegetation carbon stocks after land-use changes in a seasonally dry tropical forest. Geoderma, 390:114943, doi: 10.1016/j.geoderma.2021.114943.
doi: 10.1016/j.geoderma.2021.114943
[68]   McDowell N, Pockman W T, Allen C D, et al. 2008. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytologist, 178(4):719-739.
doi: 10.1111/j.1469-8137.2008.02436.x pmid: 18422905
[69]   Milkias A, Toru T. 2018. Assessment of land use land cover change drivers and its impacts on above ground biomass and regenerations of woody plants: A case study at Dire Dawa administration, Ethiopia. Atmospheric and Climate Sciences, 8(1):111-120.
doi: 10.4236/acs.2018.81008
[70]   Mitchell R J, Beaton J K, Bellamy P E, et al. 2014. Ash dieback in the UK: a review of the ecological and conservation implications and potential management options. Biological conservation, 175:95-109.
doi: 10.1016/j.biocon.2014.04.019
[71]   Morillas L, Gallardo A, Portillo-Estrada M, et al. 2012. Nutritional status of Quercus suber populations under contrasting tree dieback. Forestry: An International Journal of Forest Research, 85(3):369-378.
doi: 10.1093/forestry/cps035
[72]   O'Connor M I, Gonzalez A, Byrnes J E, et al. 2017. A general biodiversity-function relationship is mediated by trophic level. Oikos, 126(1):18-31.
doi: 10.1111/oik.2016.v126.i1
[73]   Ogaya R, Barbeta A, Başnou C, et al. 2015. Satellite data as indicators of tree biomass growth and forest dieback in a Mediterranean holm oak forest. Annals of Forest Science, 72(1):135-144.
doi: 10.1007/s13595-014-0408-y
[74]   Ozdemir E, Makineci E, Yilmaz E, et al. 2019. Biomass estimation of individual trees for coppice-originated oak forests. European Journal of Forest Research, 138(4):623-637.
doi: 10.1007/s10342-019-01194-2
[75]   Perez-Harguindeguy N, Diaz S, Garnier E, et al. 2013. New handbook for standardized measurement of plant functional traits worldwide. Australian Journal of Botany, 61(3):167-234.
doi: 10.1071/BT12225
[76]   Pinheiro J, Bates D, DebRoy S, et al. 2017. R development core team nlme: Linear and nonlinear mixed effects models R package version 1. 15.6. [2021-08-09]. https://CRAN.R-project.org/package=nlme .
[77]   Pommerening A. 2002. Approaches to quantifying forest structures. Journal of Forest Research, 75(3):305-324.
[78]   Rawat M, Arunachalam K, Arunachalam A, et al. 2019. Associations of plant functional diversity with carbon accumulation in a temperate forest ecosystem in the Indian Himalayas. Ecological Indicators, 98:861-868.
doi: 10.1016/j.ecolind.2018.12.005
[79]   Reich P B, Walters M B, Kloeppel B D, et al. 1995. Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species. Oecologia, 104(1):24-30.
doi: 10.1007/BF00365558 pmid: 28306909
[80]   Reich P B, Tilman D, Naeem S, et al. 2004. Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N. Proceedings of the National Academy of Sciences, 101(27):10101-10106.
[81]   Richter C, Rejmánek M, Miller J E, et al. 2019. The species diversity×fire severity relationship is hump-shaped in semiarid yellow pine and mixed conifer forests. Ecosphere, 10(10):e02882, doi: 10.1002/ecs2.2882.
doi: 10.1002/ecs2.2882
[82]   Rosseel Y. 2012. Lavaan: An R package for structural equation modeling. Version 0.5-12 (BETA). Journal of Statistical Software, 48(2):1-36.
[83]   Ruiz‐Jaen M C, Potvin C. 2011. Can we predict carbon stocks in tropical ecosystems from tree diversity? Comparing species and functional diversity in a plantation and a natural forest. New Phytologist, 189(4):978-987.
doi: 10.1111/j.1469-8137.2010.03501.x pmid: 20958305
[84]   Salehzadeh O, Eshaghi R J, Maroofi H. 2017. The effect of anthropogenic disturbance on flora and plant diversity in oak forests of west. Forest Research and Development, 2:219-240.
[85]   Sánchez-Salguero R, Camarero J J. 2020. Greater sensitivity to hotter droughts underlies juniper dieback and mortality in Mediterranean shrublands. Science of the Total Environment, 721:137599, doi: 10.1016/j.scitotenv.2020.137599.
doi: 10.1016/j.scitotenv.2020.137599
[86]   Schielzeth H. 2010. Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution, 1(2):103-113.
doi: 10.1111/j.2041-210X.2010.00012.x
[87]   Shiravand H, Hosseini S A. 2020. A new evaluation of the influence of climate change on Zagros oak forest dieback in Iran. Theoretical and Applied Climatology, 141:685-697.
doi: 10.1007/s00704-020-03226-z
[88]   Stojanović M, Sánchez-Salguero R, Levanič T, et al. 2017. Forecasting tree growth in coppiced and high forests in the Czech Republic. The legacy of management drives the coming Quercus petraea climate responses. Forest Ecology and Management, 405:56-68.
doi: 10.1016/j.foreco.2017.09.021
[89]   Tahmasebi P, Moradi M, Omidipour R, 2017. Plant functional identity as the predictor of carbon storage in semi-arid ecosystems. Plant Ecology and Diversity, 10(2-3):139-151.
doi: 10.1080/17550874.2017.1355414
[90]   Tetemke B A, Birhane E, Rannestad M M, et al. 2019. Allometric models for predicting aboveground biomass of trees in the dry afromontane forests of northern Ethiopia. Forests, 10(12):1114.
doi: 10.3390/f10121114
[91]   Thorn S, Seibold S, Leverkus A B, et al. 2020. The living dead: acknowledging life after tree death to stop forest degradation. Frontiers in Ecology and the Environment, 18(9):505-512.
doi: 10.1002/fee.v18.9
[92]   Touhami I, Chirino E, Aouinti H, et al. 2020. Decline and dieback of cork oak (Quercus suber L.) forests in the Mediterranean basin: a case study of Kroumirie, Northwest Tunisia. Journal of Forestry Research, 31(5):1461-1477.
doi: 10.1007/s11676-019-00974-1
[93]   Valipour A, Plieninger T, Shakeri Z, et al. 2014. Traditional silvopastoral management and its effects on forest stand structure in northern Zagros, Iran. Forest ecology and management, 327:221-230.
doi: 10.1016/j.foreco.2014.05.004
[94]   van Con T, Thang N T, Khiem C C, et al. 2013. Relationship between aboveground biomass and measures of structure and species diversity in tropical forests of Vietnam. Forest Ecology and management, 310:213-218.
doi: 10.1016/j.foreco.2013.08.034
[95]   van der Plas F. 2019. Biodiversity and ecosystem functioning in naturally assembled communities. Biological Reviews, 94(4):1220-1245.
[96]   Vance-Chalcraft H D, Willig M R, Cox S B, et al. 2010. Relationship between aboveground biomass and multiple measures of biodiversity in subtropical forest of Puerto Rico. Biotropica, 42(3):290-299.
doi: 10.1111/j.1744-7429.2009.00600.x
[97]   Wekesa C, Kirui B K, Maranga E K, et al. 2019. Variations in forest structure, tree species diversity and above-ground biomass in edges to interior cores of fragmented forest patches of Taita Hills, Kenya. Forest Ecology and Management, 440:48-60.
doi: 10.1016/j.foreco.2019.03.011
[98]   Zhang F, Zhan J, Zhang Q, et al. 2017. Impacts of land use/cover change on terrestrial carbon stocks in Uganda. Physics and Chemistry of the Earth, Parts A/B/C, 101:195-203.
doi: 10.1016/j.pce.2017.03.005
[99]   Zhang Y, Chen H Y, Reich P B. 2012. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. Journal of Ecology, 100(3):742-749.
doi: 10.1111/j.1365-2745.2011.01944.x
[100]   Zhang Y, Loreau M, Lü X, et al. 2016. Nitrogen enrichment weakens ecosystem stability through decreased species asynchrony and population stability in a temperate grassland. Global Change Biology, 22(4):1445-1455.
doi: 10.1111/gcb.2016.22.issue-4
[101]   Zhang Y, Chen H Y, Taylor A R. 2017. Positive species diversity and above-ground biomass relationships are ubiquitous across forest strata despite interference from overstorey trees. Functional Ecology, 31(2):419-426.
doi: 10.1111/fec.2017.31.issue-2
[102]   Zirbel C R, Grman E, Bassett T, et al. 2019. Landscape context explains ecosystem multifunctionality in restored grasslands better than plant diversity. Ecology, 100(4):e02634, doi: 10.1002/ecy.2634.
doi: 10.1002/ecy.2634
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