Conservation value and ecosystem service provision of Nothofagus antarctica forests based on phenocluster categories

Conservation value and ecosystem service provision of Nothofagus antarctica forests based on phenocluster categories | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Conservation value and ecosystem service provision of Nothofagus antarctica forests based on phenocluster categories Guillermo José Martínez Pastur, Julián Rodríguez-Souilla, Yamina Micaela Rosas, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4958863/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Remote sensing and landscape modelling improve forest classification. One approach combines variables based on forest phenology and climate to characterisefunctional rather than structural or compositional characteristics of ecosystems (phenoclusters). However, there are few studies about the correlation between these new modelling approaches and forest classifications based on ground surveys. Our objective was to define the conservation value of different functional forests, based on phenocluster categories, for Nothofagus antarctica forests in Tierra del Fuego. We used different available features model outputs standardised and homogenised at 90-m spatial resolution (phenoclusters, ecosystem services, potential biodiversity), and ground truthdata from 145 stands (soil characteristics, forest structure, animal stocking rate, understory biodiversity). The phenocluster categories were compared using uni- and multivariate analyses. The use of phenocluster categories allowed sorting of the N. antarctica forest type into contrasting subtypes with different characteristics, including (i) cultural, regulating, and provisioning ecosystem services and potential biodiversity at landscape level (F = 1.8-87.6), (ii) soil organic carbon, nitrogen, and phosphorous properties (F = 4.2-5.2), (iii) tree dominant height, overstory crown cover, basal area, and bark volume forest structure (F = 0.1-6.3), animal stock (F = 1.0-1.9), and (iv) understory plant richness (F = 1.0-9.4) at stand level. Significant differences were detected in the multivariate analyses (classifications and ordinations) supporting the split of this forest type into four functional forest subtypes: (i) coastal forests near the Atlantic Ocean, (ii) highland forests close to the steppe, (iii) ecotone areas associated with N. pumilio forests, and (iv) degraded and secondary forests. The cyclic and seasonal greenness information provided by the phenoclusters were directly related to plant understory diversity, where functional rather than structural or compositional characteristics of forest ecosystems were the main explanatory variable. Our findings can support better management and conservation proposals, e.g. different management strategies for each phenocluster category, or selection of representative forests into a reserve network design based on phenoclusters rather than forest types defined by tree canopy-cover composition. functional forests potential biodiversity forest structure soil characteristics forest management and conservation planning Patagonia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Natural forests host most of the Earth's terrestrial biodiversity (Gardner et al. 2009 ; Scheffers et al. 2012 ), where intensified anthropogenic activities induce a drastic biodiversity loss with major ecological consequences (Poulsen et al. 2013 ; Lhoest et al. 2020 ). Most conservation strategies are focused on preserving intact forests with unique characteristics inside National Parks or Reserves (set aside strategy or land-sparing), or protecting High Conservation Value Forests (HCVF) in managed forests landscapes, e.g. following The Forest Stewardship Council (FSC) or others proposals, like land-sharing strategy (Benitez et al. 2019 ; Lencinas et al. 2019 ; Areendran et al. 2020 ; Grönlund et al. 2020 ; Rosas et al. 2023 ). The conservation values of these protected forests were mostly defined according to species uniqueness, ecosystem types, environmental novelties, ecosystem services (ES) or specific needs of local communities (Halmy and Salem 2015 ; Pour et al. 2023 ). However, in regions with limited ground-based field data, conservation strategies are mainly designed according to forest intactness (e.g. null or low human foot-print), or forest types (FT) defined on the basis of the most dominant tree species (Carrasco et al. 2021 ; Rosas et al. 2022 ; Martinuzzi et al. 2023 ). The concept of ES (cultural, regulating, provisioning) refers to the goods and benefits that society obtains from natural ecosystems (Braat and de Groot 2012 ), both monetary and non-monetary (Chivulescu et al. 2024 ), which can be linked to specific FT, e.g. timber values (Peri et al. 2017 ). However, the provision of these ES differ across the landscape for each FT, e.g. timber values or cultural heritage change due to intrinsic characteristics of the forests (timber tree species, heritage, landscape beauty, accessibility) (Martínez Pastur et al. 2017 ; Peri et al. 2024 ). Similarly, biodiversity also changes in conjunction with forest type characteristics (e.g., Martínez Pastur et al. 2016 ; Lencinas et al. 2024 ), and according species groups (e.g. in Patagonia, birds are more abundant in forests while plants are more frequent in humid open-lands) (Rosas et al. 2022 ). In this context, protected areas defined according to FT based on dominant tree species may not capture all the biodiversity variability across the landscape, requiring other approaches to achieve protection of HCVF. Because different forests have different threats and conservation opportunities (Lhoest et al. 2020 ), the understanding of these differences could allow to improve the design of more effective conservation strategies across the landscape (Poulsen et al. 2011 ; Panlasigui et al. 2018 ), and along different landscapes for widely distributed forests. One alternative to basing conservation strategies on forest type or forest intactness is to incorporate variables based on alternative proxies linked to conservation values. In Argentina, natural ecosystem maps (e.g. forest and non-forest ecosystems) were mainly developed based on their floristic and physiographic characteristics (Cabrera 1971 ; Paruelo et al. 1991 ). Remote sensing and landscape modelling improve these first forest classifications (Martínez Pastur et al. 2024 ), e.g. by including social and biophysical perspectives, or by integrating climate and soil characteristics (Morello et al. 2012 ; Oyarzabal et al. 2018 ; Derguy et al. 2021 ). A new approach based on phenoclusters, which combines variables related to forest phenology (e.g. vegetation event timing and greenness) and climate has further improved forest classifications in Argentina (Siveira et al. 2022) as well as in Wisconsin, United States (Silveira et al. 2024 ). Phenoclusters can be conceptualised as a vegetation assemblage, or the structure and composition of one area relative to a continuum of vegetation conditions (Hoagland et al. 2018 ). The cyclic and seasonal greenness information provided by the phenoclusters is useful for forest planning and management, and biodiversity conservation, particularly in regions where forest ecological information is limited, such as in developing countries (Silveira et al. 2022 , 2024 ). Another advantage of using phenoclusters to characterise native forests is the capacity to capture phenology and climate gradients within a single FT allowing the sub-classification of forest types at the landscape level (Silveira et al. 2022 ; Martínez Pastur et al. 2024 ). The main challenge to map FT is finding correspondence between remote-sensing data and specific forest communities that sometimes differ one from each other by a few species, that are difficult to capture by remote sensors (Roelofsen et al. 2014 ; Bajocco et al. 2019 ; Pesaresi et al. 2020 ). Greenness dynamics based on vegetation indexes assist in detecting small-scale forest communities for forest management and conservation planning (D’Odorico et al. 2015 ; Revermann et al. 2016 ; Grabska et al. 2019 ; Adams et al. 2020 ). Furthermore, monitoring vegetation phenology helps to detect changes in ecosystem functions, providing solid baseline data to evaluate vegetation dynamics, e.g. droughts, fires or climate oscillations (Bascietto et al. 2018 ; Workie and Debella 2018 ). In this context, remote sensing provides a useful tool to define regional vegetation phenology classifications as it measures vegetation processes and functions in time and space (Bajocco et al. 2019 ; Silveira et al. 2024 ). Zoning is one of the planning tools used by the Argentinian government to regulate human activities in native forests, as is stated in the National Law 26,331/07, and the provinces are obligated to define land use zones every five years (Martinuzzi et al. 2023 ; Martínez Pastur et al. 2024 ). The most traditional approach is to classify the forest ecosystems into different zones by FT according to the present situation and needs of forest planning (Prodan et al. 1997 ), e.g. taxonomy, assemblage of species, phenology, growth and development phases, soil, or topography (Cajander 1949 ; Blasi et al. 2000 ; Larsen and Nielsen 2006 ; Barbati et al. 2007 ). An alternative approach suggests classifying forests based on phenoclusters and the basal area (BA) contribution of each species in the stand (Huertas Herrera et al. 2023 , Martínez Pastur el al. 2024). This alternative approach generates a unified methodological proposal to define and characterise different native forest types at different scales in Argentina based on metrics that are easily measured during forest inventories (Martínez Pastur et al. 2024 ). Nothofagus antarctica (common name ñire) forests, one of the main forest types in Patagonia, occur variously from mixed stands with Araucaria araucana (36°30’ SL) near the tree-line to monospecific stands in Cape Horn near the marine shoreline (56°00’ SL) (Steinke et al. 2008 ; Morello et al. 2012 ; Oyarzabal et al. 2018 ). It is one of the most plastic species that can adapt to a great variety of environmental conditions (Ramírez et al. 1985 ; Soliani et al. 2021 ). In this context, this species cannot be managed or conserved as a unique FT (Peri et al. 2017 ; Martínez Pastur et al. 2020a , 2021 ; Soler et al. 2022 ). Thus, N. antarctica is a suitable species to analyse the new proposed classification alternative (phenoclusters) (Huertas Herrera et al. 2023 ; Martínez Pastur et al. 2024 ). Our objective was to define the conservation value of one forest type ( N. antarctica forests) in Tierra del Fuego, Argentina, classified according to different functional forest (phenocluster) categories at landscape level. Specifically, we analysed: (i) the provision of ES and potential biodiversity of the different forest phenocluster categories, based on available data at the landscape level; (ii) stand and forest structure characteristics of different forest phenocluster categories based on field surveys at stand level; (iii) animal stocking rate in the different forest phenocluster categories based on field surveys at stand level; and (iv) plant understory characteristics of the different forest phenocluster categories, these last three based on field surveys at stand level. We hypothesised that: (i) one forest type can present different functional characteristics based on land surface phenology and climate variables (Silveira et al. 2022 ), which can directly affect forest structure and ES provision; (ii) the cyclic and seasonal greenness information provided by the phenoclusters are directly related to biodiversity, particularly to plant understory species, influencing other components (e.g. animal grazing); and (iii) the differences in functional rather than structural or compositional characteristics of ecosystems directly influences the potential biodiversity and conservation values of these forests. We expect that these findings allow us to define better strategies for management and conservation planning, e.g. differential management proposals or better forest representation in reserve networks based on phenoclusters rather than forest types based on canopy-cover composition. 2. Materials and methods 2.1. Study area The study area covers the natural distribution of N. antarctica forests in the Argentinean portion of Tierra del Fuego (53°38’ to 54°37’ SL, 66°28’ to 68°36’ WL), including 181.5 thousand ha of pure forests (Carrasco et al. 2021 ; Silveira et al. 2022 ). These deciduous native forests are mainly used for livestock and firewood extraction, and occasionally were managed under silvopastoral systems (Peri et al. 2016a , 2017 ). In the study area, the Andes Mountains run from west to east, and define the relief and climate of the region under the influence of Antarctica and both Pacific and Atlantic oceans (Carrasco et al. 2021 ). The climate is cold oceanic with strong winds, mean annual temperature of 5.5°C, and rainfall evenly spread over the year, with an annual average of 300 to 500 mm yr − 1 (Allué et al. 2010 ; Martínez Pastur et al. 2021 ). The soils are from glacial origin, where acid brown soils are the most common (Gea Izquierdo et al. 2004 ). 2.2. Modelling outputs used at landscape level Our analyses were conducted using a database of unmanaged and managed N. antarctica forests in Tierra del Fuego (Argentina) belonging to the PEBANPA network (Peri et al. 2016b ), as well as methodologies and modelling previously developed for these forests. These models include GRIDs that were standardised and homogenised at 90-m spatial resolution in ArcMap 10.0 software (ESRI 2011 ): (i) Cover of the different forest types (FTC), including pure N. antarctica forests (NA), pure N. pumilio forests (NP), and mixed evergreen forests (MIX) (Carrasco et al. 2021 ; Silveira et al. 2022 ). (ii) Phenoclusters classes (PC) specifically defined for Tierra del Fuego (extracted from Silveira et al. 2022 ) with 27 different categories based on land surface phenology, climate patterns, normalised difference vegetation index (NDVI), and other related indices. (iii) Provision of ecosystem services (ES) at the landscape level, including cultural (ES-C), regulation (ES-R), provisioning (EC-P), and the combination of all three ES types into total ES (ES-T) (Carrasco et al. 2021 ). ES-C includes aesthetic, existence, local identity, and recreational values; ES-R was defined through the net primary productivity of natural ecosystems and habitat quality; ES-P was characterised through timber (e.g. wood for sawmills) and silvopastoral values (e.g. livestock capacity and wood for different uses as firewood) (Martínez Pastur et al. 2017 ; Peri et al. 2016a , 2017 ); and ES-T was defined as the average of the three ES types after each GRID was rescaled from 0 to 100 using a linear scale by a function tool into ArcMap 10.0 software (ESRI 2011 ). (iv) Potential biodiversity (BIODIV) at the landscape level (Martínez Pastur et al. 2016 ), was calculated using Environmental Niche Factor Analysis (Hirzel et al. 2002 ) to model habitat suitability of the most common understory plant species in each forest type based on 41 explanatory variables. 2.3. Field sampling and data analyses We obtained stand-level field data from 145 plots (even- and uneven-aged stands with > 2 ha each) across the landscape, including: (v) Soil properties, using soil organic carbon (%, SOC), soil nitrogen (%, SN), and soil phosphorus (ppm, SP; Martínez Pastur et al. 2021 , 2022a , 2023 ). (vi) Forest structure, characterised by tree dominant height (m, DH), overstory crown cover (%, CC), diameter at breast height (cm, DBH), basal area (m² ha − 1 , BA), and total over bark volume (m³ ha − 1 , TOBV) (Martínez Pastur et al. 2021 ; Aravena Acuña et al. 2023 ). (vii) Animal stocking rate expressed in sheep equivalents (SE) per hectare to make possible further comparisons, including native wild ( Lama guanicoe ) (SE ha − 1 , AS-LG) and domestic mammals (cattle, sheep, horses) (SE ha − 1 , AS-CAT) (Martínez Pastur et al. 2022b ). Finally, we used previously collected plant understory data (Martínez Pastur et al. 2020a , 2021 , b ), to calculate total richness (species per plot, RCH-T), richness of native species (species per plot, RCH-N), richness of exotic species (species per plot, RCH-E), plant understory total biomass (kg ha − 1 , UB), total understory cover (%, UC-T), cover of native species (%, UC-N), cover of exotic species (%, UC-E), cover of dicots (%, UC-D), cover of monocots (%, UC-M), cover of ferns (%, UC-F), and cover of inferior plants (mosses and liverworts) (%, UC-I). We identified plant species according to Correa (1969–1998) and Moore ( 1983 ), and classified them by their family, taxonomic groups, origin (N = native, E = exotic), and lifeform (T = tree, S = shrub, DS = dwarf-shrub, C = cushion, PH = prostrate herb, EH = erect herb, CG = caespitose grass, RG = rhizomatous grass). For each plant species, we calculated the occurrence (%, OCC) and mean cover at each sampling plot (%, COV). We determined the importance value of each species based on the average values of OCC and COV (IV-OC) after rescaling the values between 0 and 1. 2.4. Statistical analyses We conducted a cluster analysis with complete linkage and Euclidean distances among the different phenocluster classes (PC) using the variables employed in the original modelling of Silveira et al. ( 2022 ) (Appendix 1). We prioritised the phenocluster classes dominated by N. antarctica trees and cluster groups obtained in the multivariate analysis, and defined four new phenocluster groups (PG, A to D) for further analyses. We used one-way analysis of variance (ANOVA) to test for differences among phenocluster groups, using Fisher and Tukey tests at p < 0.05. We analysed: (i) ecosystem services provision (ES-C, ES-R, EC-P, ES-T) and potential biodiversity (BIODIV); (ii) soil properties and forest structure variables (SOC, SN, SP, DH, CC, DBH, BA, TOBV); and (iii) plant understory and animal stocking rate (RCH-T, RCH-N, RCH-E, UB, AS-LG, AS-CAT, UC-T, UC-N, UC-E, UC-D, UC-M, UC-F, UC-I). We also analysed the plant understory assemblage among different PG categories through: (i) a cluster analysis with complete linkage and Euclidean distances using mean species cover by sampling plot; and (ii) an overlapping analysis represented with Venn diagrams comparing the three most important groups according the cluster analysis (PG-A, PG-B, PG-C + PG-D) analysing the species occurrence and origin (native or exotic). Finally, we conducted multivariate analyses to compare the defined phenocluster groups. We compared understory vascular plant assemblages of phenocluster groups using Multi-Response Permutation Procedures (MRPP) with Bray-Curtis distance (McCune et al. 2002 ), including pairwise grouping tests to determine significance of differences (Zimmerman et al. 1985 ). We also conducted a Canonical Correspondence Analysis (CCA) (Ter Braak 1986 ) to evaluate the effect of our potential explanatory variables at the stand level. We excluded dominant height and diameter at breast height from this analysis because some plots had no trees. Due to the low importance of the species with low cover in our matrix of cover of all vascular plant species (156 species x 148 samples; see Appendix 2), we removed any species with < 5% cover in our dataset, resulting in a final matrix of 89 species x 148 samples. We tested the significance of our analysis with a Monte-Carlo test with 499 permutations. We used PC-ORD (McCune and Mefford 1999 ) for our MRPPs, and CANOCO 5.4 © Biometrics 1997–2014 (Ter Braak and Šmilauer 2002 ) for our CCA. 3. Results 3.1. Changes in phenocluster values across the landscape The 27 phenocluster classes (PC) of Tierra del Fuego included different proportions of N. antarctica forests (0–96.5%), which represented different functional values due to the regional climate across the landscape (Appendix 1). The cluster analyses based on these differential characteristics allowed us to define different forests phenocluster groups (PG) (Fig. 1 ), which included different tree species compositions for the main forest types (Table 1 ). The functionality of phenocluster groups is not totally related to the tree canopy composition, but follows a clear landscape pattern (Fig. 2 ). We focused on N. antarctica forests to define four different PG: (i) The first group (PG-A) consisted of two phenoclusters dominated in basal area by pure N. antarctica (90%) forests intermixed with N. pumilio patches (10%), and occurred in lowlands under the Atlantic Ocean influence. (ii) The second group (PG-B), consisted of three phenoclusters, dominated by pure N. antarctica (88%) forests intermixed with N. pumilio patches (12%) and a few mixed evergreen forests, and occupied inland hilly areas. These two phenocluster groups were located in the ecotone area between the Andes Mountains and open-lands (dry grasslands and shrublands). (iii) The third group (PG-C) consisted of one phenocluster characterised by heterogeneous forested landscape that was heavily degraded (e.g. clear-cuts, fires, pastures) interspersed with closed secondary N. antarctica forests. (iv)The last group (PG-D) consisted of 21 phenoclusters, and was characterised by N. antarctica forests with marginal occurrence, in a landscape dominated by N. pumilio and mixed evergreen forests (71.6%) that occurred near and across the Andes Mountains in the southern areas of Tierra del Fuego. FIGURE 1 and 2 , and Table 1 Table 1 Proportional tree canopy composition (NA = Nothofagus antarctica , NP = N. pumilio , MIX = mixed deciduous-evergreen forests) of phenocluster groups (PG) and classes (PC) in Tierra del Fuego (Argentina) defined for our study. The area of each PC is shown in thousand ha − 1 . Grey-shaded cells indicate the most important forest type at each PC. PG PC Area (thousand ha − 1 ) NA NP MIX A 2 18.52 96.5% 3.5% 0.0% 7 45.50 87.6% 12.4% 0.0% Total 64.02 90.2% 9.8% 0.0% B 9 45.54 91.9% 8.1% 0.0% 16 51.37 85.4% 14.6% 0.0% 26 4.68 84.3% 15.4% 0.3% Total 101.59 88.3% 11.7% 0.0% C 6 14.02 56.4% 42.5% 1.1% Total 14.02 56.4% 42.5% 1.1% D 1 22.94 1.4% 45.2% 53.5% 3 52.54 0.9% 42.8% 56.3% 4 0.93 37.9% 61.0% 1.2% 5 5.55 22.2% 69.0% 8.8% 8 61.78 21.4% 58.1% 20.4% 10 27.92 0.1% 7.3% 92.6% 11 32.78 0.4% 62.4% 37.2% 12 29.19 0.1% 66.2% 33.7% 13 23.60 0.2% 36.7% 63.1% 14 31.10 31.8% 58.4% 9.8% 15 13.38 0.0% 29.7% 70.3% 17 28.55 0.2% 8.0% 91.8% 18 12.63 2.3% 45.5% 52.2% 19 47.08 18.8% 74.4% 6.7% 20 25.96 33.4% 53.9% 12.7% 21 0.06 0.0% 70.3% 29.7% 22 33.28 4.1% 80.6% 15.3% 23 3.01 0.0% 55.2% 44.8% 24 72.26 9.9% 87.6% 2.5% 25 4.33 0.1% 60.2% 39.7% 27 22.21 0.2% 29.7% 70.1% Total 551.08 9.9% 57.5% 36.9% TOTAL 730.72 28.4% 44.9% 26.7% 3.2. Provision of ecosystem services and potential biodiversity of the phenocluster groups We found significant differences among the provision of ecosystem services (cultural, regulation, provision) between our phenocluster groups, but not for total ecosystem services (Table 2 ). This indicates that different ecosystem services varied across the landscape and phenocluster groups. Cultural ecosystem services were higher in PG-A where ranching and accessibility (e.g. roads) were higher, and lower in PG-B and PG-C where distances to urban areas were greater. Regulation ecosystem services were higher in PG-B and lower in PG-A, and were influenced by topography and ecosystem integrity (e.g. lower human uses). Provisioning ecosystem services were higher in PG-A and PG-D due to ranching and timber uses, respectively. Potential biodiversity at the landscape level differed among phenocluster groups (Table 2 ), following a rainfall gradient, being lower in dry open-lands and higher in mountainous landscapes. Table 2 Analyses of variance of ecosystem services provision (ES-C = cultural, ES-R = regulating, EC-P = provisioning, ES-T = total) and potential biodiversity (BIODIV) in each phenocluster group (PG) defined for Nothofagus antarctica forests in Tierra del Fuego (Argentina). PG ES-C ES-R ES-P ES-T BIODIV A 48.5 c 60.7 a 52.6 a 69.9 53.1 a B 35.1 a 71.9 c 52.1 b 71.3 63.4 b C 30.7 a 62.8 ab 57.9 c 67.1 80.0 c D 41.7 b 65.6 b 49.9 a 68.8 71.2 c F (p) 32.46 (< 0.001) 18.24 (< 0.001) 22.19 (< 0.001) 1.85 (0.137) 87.55 (< 0.001) F = Fisher test, p = probability. Different letters show differences by Tukey test (p < 0.01). 3.3. Stand characteristics associated to the phenocluster groups Soil properties varied among the phenocluster groups (Table 3 ). Soil organic carbon, nitrogen and phosphorus were highest in PG-D in N. antarctica stands associated with other forest types ( N. pumilio and mixed evergreen). These soil characteristics influenced other forest structure variables (DH, CC, BA, TOBV) that showed the lowest values in PG-A (e.g. open-forests with lower height and forest volume). Table 3 Analysis of variance of soil properties and forest structure variables in each phenocluster group (PG) defined for Nothofagus antarctica forests in Tierra del Fuego (Argentina). SOC = soil organic carbon (%), SN = soil nitrogen (%), SP = soil phosphorus (ppm), DH = tree dominant height (m), CC = overstory crown cover (%), DBH = diameter at breast height (cm), BA = basal area (m² ha − 1 ), TOBV = total over bark volume (m³ ha − 1 ). PG SOC SN SP A 9.1 a 0.6 a 19.3 a B 9.2 a 0.5 a 18.7 a C 8.4 a 0.6 ab 26.1 ab D 14.0 b 0.8 b 31.6 b F (p) 5.11 (0.002) 4.26 (0.006) 5.24 (0.001) PG DH CC DBH BA TOBV A 8.2 a 41.5 a 29.7 17.1 a 81.5 a B 9.1 ab 57.1 b 30.1 24.5 ab 122.8 ab C 10.9 b 76.3 b 33.6 35.6 b 207.5 b D 10.2 b 57.8 b 30.5 24.4 ab 132.1 b F (p) 6.27 (< 0.001) 5.54 (0.001) 0.17 (0.913) 3.50 (0.017) 5.31 (0.002) F = Fisher test, p = probability. Different letters show differences by Tukey test (p < 0.01). We found 156 understory vascular plant species in our sampling plots (full list of plant species, occurrence, cover, and importance values can be found in Appendix 2). The five most common species in N. antarctica forests were two exotics ( Poa pratensis with IV-OC = 0.942 and Taraxacum officinale with IV-OC = 0.785), one native dicot ( Osmorhiza depauperata with IV-OC = 0.296), and two native monocots ( Festuca magellanica with IV-OC = 0.409 and Trisetum spicatum with IV-OC = 0.192). Total understory vascular plant richness per plot differed among phenocluster groups, being greater in PG-B and decreasing in the other phenocluster groups (coastal and mountainous areas). This trend was concordant with exotic species response per plot due to native richness per plot remaining stable across the landscape (Table 4 ). The understory covers also varied among the phenocluster groups, where exotic and monocot species were higher in drier areas with more farming activities (PG-A and PG-B) than in the other PGs. Dicot cover increased with rainfall (from PG-A to PG-D), but decreased in degraded areas (PG-C) (Table 4 ). Finally, we found no significant differences in understory biomass and animal stocking rate (wild or domestic) among phenocluster groups. Table 4 Analyses of variance of plant understory and animal stock variables in the phenocluster groups (PG) defined for Nothofagus antarctica forests in Tierra del Fuego (Argentina). RCH-T = total richness of the understory (species per plot), RCH-N = richness of native species in the understory (species per plot), RCH-E = richness of exotic species in the understory (species per plot), UB = understory total biomass (kg ha − 1 ), AS-LG = animal stock considering native mammals ( Lama guanicoe ) (sheep equivalent, SE ha − 1 ), AS-CAT = animal stock considering domestic mammals (cattle, sheep and horses) (SE ha − 1 ), UC-T = total understory cover (%), UC-N = understory cover of native species (%), UC-E = understory cover of exotic species (%), UC-D = understory cover of dicots (%), UC-M = understory cover of monocots (%), UC-F = understory cover of ferns (%), UC-I = understory cover of inferior plants (mosses and liverworts) (%). PG RCH-T RCH-N RCH-E UB AS-LG AS-CAT A 25.0 ab 19.9 5.1 b 1533.5 0.17 2.26 B 27.1 b 22.1 5.0 b 1494.7 0.07 1.05 C 25.3 ab 20.1 5.2 b 674.5 0.05 1.98 D 22.2 a 18.2 4.0 a 1117.9 0.02 1.71 F (p) 3.03 (0.031) 2.27 (0.083) 2.82 (0.041) 1.65 (0.179) 0.97 (0.407) 1.94 (0.125) PG UC-T UC-N UC-E UC-D UC-M UC-F UC-I A 162.3 98.2 64.1 b 63.9 a 83.7 b 3.0 11.7 B 186.3 119.7 66.6 b 83.3 b 83.2 b 7.8 11.9 C 141.4 101.3 40.1 a 60.1 a 59.8 ab 6.9 14.6 D 164.7 124.4 40.3 a 84.8 b 52.9 a 5.2 21.8 F(p) 2.25 (0.085) 2.39 (0.070) 4.20 (0.007) 3.15 (0.027) 9.40 (< 0.001) 0.99 (0.401) 2.49 (0.062) F = Fisher test, p = probability. Different letters show differences by Tukey test (p < 0.01). 3.4. Understory species assemblage associated to the phenocluster groups Our cluster analysis based on species cover (Appendix 2) showed that coastal forests (PG-A) were different from hilly forests (PG-B), which joined with a greater linkage distance to southern N. antarctica forests (PG-C and PG-D) (Fig. 3 ). Total richness changed according to different phenocluster groups, being higher in northern forests (PG-A = 120 species, PG-B = 125 species) than in southern forests (PG-D = 111 species), and decreasing in degraded forests (PG-C = 53 species) (Fig. 4 ). The different phenocluster groups shared 53.2% of the total richness (72 native and 11 exotic species) 23.7% of species were shared between two phenocluster groups (12.2% between PG-A and PG-B, 6.4% between PG-A and PG-C + D, 5.1% between PG-B and PG-C + D), and 23.1% were found in only one group (5.1% for PG-A, 9.6% for PG-B, 8.3% for PG-C + D). The exclusive species in PG-A included 4 native monocot species, and 3 native and one exotic dicot species, while in PG-B exclusive species included 3 native and 2 exotic monocots, and 9 native and one exotic dicot species. Moreover, the exclusive species in PG-D included 4 native monocot species, and 6 native and 3 exotic dicot species. PG-C did not harbour any species exclusively. Bi-plots of Canonical Correspondence Analysis showed a great overlap in species assemblages among plots of the phenocluster groups (Fig. 5 ). However, the overlap followed the gradient in the landscape pattern observed in Fig. 2 . The significance of all canonical axes was F = 5.744 (p = 0.002), and the cumulative percentage variance of species cover and explanatory variables was 52.1% (axes 1 and 2). The Multi-Response Permutation Procedure (MRPP) detected significant differences among pairwise comparisons, except for PG-C vs. PG-B, and PG-C vs. PG-D (Table 5 ). These differences showed that the three main groups were different, while degraded forests (PG-C) presented intermediate characteristics between PG-B and PG-D. The most important explanatory variables were understory cover of plant groups, overstory crown cover, soil organic carbon, and richness of exotic understory species (Fig. 5 and Appendix 3). Cover of exotic species (UC-E and RCH-E) and monocots were associated with northern N. antarctica forests (PG-A and PG-B), while inferior plant cover (mosses and liverworts), native species cover (UC-I and UC-N), and soil organic carbon were associated with southern forests close to mountains. The cover of vascular understory species was also associated with these explanatory variables (Fig. 5 ), where most of the species occurred at the centre of the bi-plot graph. However, some species were particularly associated with some phenocluster group categories. For example, Stellaria media , Dactylis glomerata , Trifolium repens and Agrostis capillaris were associated with open forests with intensive farming uses, while Marsipospermum grandiflorum , Carex magellanica , Empetrum rubrum and Gunnera magellanica were associated with humid forests near peatlands. Table 5 Comparison of vascular plant vegetation assemblages among phenocluster groups (PG-A, PG-B, PG-C, PG-D) by Multi-Response Permutation Procedure (MRPP) in Nothofagus antarctica forests in Tierra del Fuego (Argentina). Group comparison MRPP statistics T A (p) Overall -8.508 0.022 < 0.001 PG-A vs. PG-B -4.496 0.010 0.001 PG-A vs. PG-C -1.985 0.008 0.045 PG-A vs. PG-D -10.784 0.025 < 0.001 PG-B vs. PG-C -1.066 0.005 0.139 PG-B vs. PG-D -5.327 0.014 < 0.001 PG-C vs. PG-D -0.137 0.001 0.354 T = statistic of MRPP, A = chance-corrected within-group agreement, (p) = probability associated with T. 4. Discussion 4.1. Provision of ecosystem services and potential biodiversity for different phenocluster categories Phenocluster classes are a useful tool to separate fine categories within forest types, and particularly to increase the number of categories in each forest type, e.g. in areas with low tree richness (Martínez Pastur et al. 2024 ). We found that the grouping of phenocluster categories captured a gradient in forest tree species composition, where N. antarctica dominance was as follows from highest to lowest; PG-A > PG-B > PG-C > PG-D. Evergreen mixed forests followed an inverse pattern. N. antarctica forests had lower tree height and more open canopies, allowing a more abundant and richer understory (Quinteros et al. 2010 ; Martínez Pastur et al. 2020a ; Alonso et al., 2020 ; Lencinas et al. 2024 ) than the other forest types (e.g. N. pumilio and mixed evergreen forests) which had closed canopies (Promis et al. 2012 ; Mestre et al. 2017 ). Nothofagus antarctica trees grow in contrasting environments, presenting a remarkable adaptability with different morphotypes according to the different natural environments (Ramírez et al. 1985 ; Soliani et al. 2021 ), e.g. they can grow as shrubs in tree-line forests and peatlands, and as trees with > 20 m height in flat zones with deep soils (Martínez Pastur et al. 2021 ; Martínez et al. 2023 ; Fajardo et al. 2024 ). These differences affect ecosystem services provision, especially regulating (e.g. closed intact forests presented higher values) and provisioning (e.g. a balance between livestock uses and timber values). Additionally, cultural values of these forests are related to proximity and accessibility to urban areas (Carrasco et al. 2021 ). Differences in forest structure and function also influenced the potential biodiversity, where landscapes with higher forest diversity (e.g. more forest types) were associated with more species, as has been described for other Patagonian forests (Rosas et al. 2022 ; Lencinas et al. 2024 ). Our analyses showed that the higher provisioning ecosystem services occurred in forests with low cultural values but with higher regulating services and potential biodiversity. This relationship can lead to potential trade-offs in land use planning, such as between provisioning of ecosystem services and biodiversity, which was found elsewhere in Tierra del Fuego (Martínez Pastur et al. 2017 ; Carrasco et al. 2021 ). Our results support the hypothesis that functional characteristics based on land surface phenology and climate variables (Silveira et al. 2022 ) directly explain the response of ecosystem services provision and potential biodiversity. In consequence, forest management and conservation planners must set geographic priorities not only based on the compositional or structural dimensions of biodiversity but also on the functional dimensions (Cazorla et al. 2021 ). Capturing functional diversity using satellite imagery allows us to understand the mechanisms of ecosystem functioning and biodiversity, where spatial and spectral resolution are the key components when scaling diversity assessments from regional to continental scales (Helfenstein et al. 2022 ). Understanding the main causes and consequences of spatial heterogeneity in ecosystem functions allows the development of management and conservation strategies (Hao et al. 2020 ; Brooks et al. 2020 ), and elucidation of positive synergies among ecosystem multifunctionality, ecosystem services, and ecological stability (Oliver et al. 2015 ; Manning et al. 2018 ; Cazorla et al. 2021 ). 4.2. Forest structure changes among forest phenocluster categories Environmental characteristics of forest stands significantly varied across the different phenocluster categories. Nutrients in forest soils (carbon, nitrogen, phosphorus) of Tierra del Fuego vary across the landscape relative to climate, parent material, and natural erosion processes (e.g. glacial soils) (Hildebrand-Vogel et al. 1990 ; Decker and Boerner 2003 ; Romanyà et al. 2005 ; Martínez Pastur et al. 2022a ; Peri et al. 2024 ). This also influences forest type occurrence (e.g. phosphorus is a limiting factor for N. pumilio forests) and forest structure values (e.g. site quality) (Bahamonde et al. 2018 ; Martínez Pastur et al. 2022b , 2023 ; Aravena Acuña et al. 2023 ). In our analyses, forest structure influenced by land surface phenology and climate variables defined the different phenocluster types. This supports the general concept that functionality affects ecological processes associated with biomass allocation and site quality (proxy: productivity) (Hao et al. 2020 ; Muller-Landau et al. 2020 ; Augusto and Boča 2022 ). Also, this directly influences available resources at the understory level, e.g. open canopies increasing light availability, soil moisture, wind and temperature dynamics across the growing season (Mestre et al. 2017 ; Huertas Herrera et al. 2023 ; Lencinas et al. 2024 ). In this context, we again found support for the hypothesis that phenoclusters directly influence the forest structure at stand level, and in ecosystem provisioning services. 4.3. Animal use of forest phenocluster categories We found that understory biomass varied greatly, but did not significantly differ among phenocluster categories. Understory biomass has been the main factor associated with native ( Lama guanicoe ) and domesticated herbivore (e.g. cattle) stocking rate (Soler et al. 2012 ; Alonso et al. 2020 ), however we observed different trends between biomass and animal stocking rates among phenoclusters. We found higher values of animal stock in areas of low forest canopy cover (e.g. PG-A). However, great variations in forest characteristics at landscape level can be influenced by many other non-measured factors such as management and other human impacts (e.g. poaching) (Peri et al. 2017 ; Martínez Pastur et al. 2022b ). Forest functionality is often related to forest fauna (e.g. Yuan et al. 2020 ; Enquist et al. 2020 ; de Bello et al. 2021 ); however, detailed studies must be conducted in this context to accept or refute the hypothesis that phenoclusters are linked to the animal stocking rate as one of the most important provisioning ecosystem services. 4.4. Plant species assemblage differences among forest phenocluster categories Plant understory richness that significantly varied with forest functionality was related to phenocluster categories. These differences were mainly associated with exotic plant richness and cover, which modified the natural richness and cover of N. antarctica understory due to management and other human-related impacts (Quinteros et al. 2010 ; Peri et al. 2016a ; Alonso et al. 2020 ). These changes were also associated with the plant species cover, both for monocots and dicots. Species richness and cover are influenced by forest functionality and productivity (Waide et al. 1999 ; Maestre et al. 2012 ; Enquist et al. 2020 ; de Bello et al. 2021 ). Our results show that some forest phenoclusters can be more resilient to the species invasion (e.g. phenoclusters in PG-D presenting a lower number of invasive plants). This finding may assist conservation strategies for different functional forests with contrasting forest structure and stand values. Community structure, ecosystem functioning and services can influence species invasion (e.g. Renault et al. 2022 ). Thus, ecosystem functions that sustain key-ES should be identified and prioritised for conservation action (Mori et al. 2013 ). In this context, the use of classifications based on phenoclusters can help to develop better resilience-based management strategies. The ability to detect an ecological threshold under a disturbance gradient should therefore be essential to establish a backstop for preventing forest degradation. These perspectives can take us beyond simply invoking the precautionary principle of conserving biodiversity to a predictive science that informs practical solutions to cope with uncertainties and ecological surprises in a changing world (Sasaki et al. 2015 ). Our analyses showed that nearly half of understory richness was shared among the different phenocluster categories, and the other half occurred in more narrow habitats. Southern Patagonian forests are known to have different understory richness across the landscape (Martínez Pastur et al. 2016 ; Rosas et al. 2022 ; Lencinas et al. 2024 ), but the differences were not fully explained. For example, understory richness was higher in more productive stands (e.g. higher site quality N. pumilio forests; Gallo et al. 2013 ), and richness was greater in uneven-aged N. antarctica mature stands compared to even-aged stands (Martínez Pastur et al. 2020a ). On the other hand, species richness was intermediate in N. antarctica forests compared to other forest types, and was greatest in north-western Tierra del Fuego, probably related to landscape heterogeneity and fragmentation by enrichment through dispersion from neighbouring habitats (Lencinas et al. 2024 ). The identity of plant species was associated with specific functional forests, supporting the idea that planning must include different phenocluster categories to achieve more effective conservation strategies, consistent with strategies for different forests across the world (Pesaresi et al. 2020 ; Witt et al. 2022 ; Silveira et al. 2024 ), including Argentina (Silveira et al. 2022 ; Martínez Pastur et al. 2024 ; Lencinas et al. 2024 ). In this context, we found support for the hypothesis that differences in functional rather than structural or compositional characteristics of ecosystems were related to potential biodiversity and conservation values of the studied forests. 4.5. Functional forests categorization can improve management and conservation strategies Conservation strategies must be designed and coordinated at a large scale (landscape, national or continental scale) and must balance economic development and provision of ecosystem services (Poulsen et al. 2011 ; Lhoest et al. 2020 ). Understanding the processes behind forest ecosystem service provision, as well as their trade-offs with biodiversity conservation, is a useful tool to support spatial planning and land management (Poirazidis et al. 2011 ; Carvalho-Santos et al. 2015 ; Carrasco et al. 2021 ). Conservation strategies must include land sparing (e.g. creation of national parks and provincial reserves) and land sharing (e.g. increasing the conservation values in managed landscapes) (Fischer et al. 2013 ; Lencinas et al. 2019 , 2024 ; Rosas et al. 2022 , 2023 ). Argentina promulgated the National Law 26,331/07 called the “Minimum Budgets for Environmental Protection of Native Forests”. This law includes forest management proposals with a social awareness, changes in forest cover, administrative restrictions for forest removal proposals, and long-term forest policies (Martínez Pastur et al. 2020b ). Thus, the proposals for sustainable forest management and conservation need accurate tools to maximise the implementation efficiency across the country (Koff et al. 2020 ; Xu et al. 2021 ). Mapping forest types for large areas (e.g. regional level) using ground-based data is rarely logistically feasible (Zhu and Liu 2014 ; Wadoux et al. 2020 ), and is more common in temperate cold forests with simple and predictable stand structures (Harris et al. 2021 ; Portillo-Quintero et al. 2022 ). Current global forest maps provide valuable information without considering differences in forest types (Hansen et al. 2013 ; Potapov et al. 2021 ), and are mostly based on floristic and physiographic characteristics (e.g. Cabrera 1971 ). However, these tools are limited in their effectiveness to capture the ecosystem structure variability at landscape level. More recently, the use of remote sensing and landscape modelling improved these first mapping efforts (Morello et al. 2012 ; Oyarzabal et al. 2018 ). Recently, the addition of vegetation phenology variations (e.g. event timing and greenness; Silveira et al. 2022 ), and species or climate variation have further improved forest type mapping efforts (Martínez Pastur et al. 2024 ). Nothofagus antarctica forests provide diversification of farm products by sustaining sheep and cattle production under silvopastoral systems, which provides income from meat, wool, and a range of wood products including poles, firewood, and timber for rural construction purposes. In addition, silvopastoral systems in the region provide other ES such as water regulation, biodiversity conservation, improved soil and water quality, carbon sequestration, recreation, and cultural identity. These forests grow in a mosaic with different structure and floristic compositions as a result of livestock grazing and silvicultural management, in interaction with natural (e.g. drought) and anthropogenic (e.g. fires, introduction of plant species) factors (Peri et al. 2016a , 2017 ; Martínez Pastur et al. 2017 ; Fajardo et al. 2024 ). Recent proposals have improved the assessment of synergies and trade-offs among ecosystem services and biodiversity at different spatio-temporal scales (Raudsepp-Hearne et al. 2010 ; Martínez Pastur et al. 2017 ; Peri et al. 2017 ; Rosas et al. 2022 ), allowing for better land use planning and effectiveness of natural reserve networks (Rosas et al. 2023 ). However, these strategies are limited in their ability to capture most forest landscape variability (e.g. Martínez Pastur et al. 2016 ; Carrasco et al. 2021 ). In this context, suitable forest reserve networks should take into consideration (i) different forest types and phenoclusters categories; (ii) unique potential biodiversity that these categories can host; (iii) provision of different ecosystem services, as well as the consideration of potential synergies and trade-offs among these services and biodiversity; and (iv) the human impacts or economic activities (Rosas et al. 2022 , 2023 ). Monitoring vegetation phenology is a way to detect differences in ecosystem functions, providing baseline data to monitor vegetation dynamics related to events such as drought, fire, spring frost, land use changes, and climate oscillations (Peñuelas et al. 2004 ; Workie and Debella 2018 ; Bajocco et al. 2019 ). Our results allow the definition of better strategies for management and conservation planning, e.g. differential management proposals or representation in reserve networks based on phenoclusters rather than forest types based on canopy-cover composition. Conclusions In this paper, we define the conservation value of different functional forest types based on phenoclusters categories for N. antarctica forests at the landscape level. The approach relies on the integration of landscape and stand level data with forest classification based on regional climate and phenological characterization through satellite images. The use of phenocluster categories can divide the forest types in sub-types with different characteristics, including (i) ecosystem services, (ii) soil, (iii) forest structure, and (iv) biodiversity values. The cyclic and seasonal greenness information provided by the phenoclusters are directly related to plant understory biodiversity, where functional rather than structural or compositional characteristics of forest ecosystems are the main explanatory variable. These findings can support better forest management and conservation, for example through differential management of each phenocluster category, or selection of representative forests of each category into the reserve network design based on phenoclusters rather than forest types defined by tree canopy-cover composition. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: This research was conducted with the financial support of (i) Proyectos de Desarrollo Tecnológico y Social (PDTS-0398) MINCyT (Argentina) (2020–2023), and (ii) Proyectos Interinstitucionales en Temas Estratégicos (PITES-03) MINCyT (Argentina) (2022–2024). Author Contribution G.M.P., M.V.L. and P.L.P. conceived and designed the experiments; J.R.S., Y.M.R., E.M.O.S. and A.M.O. collaborated in fieldwork and data analyses and in review and editing of the manuscript; N.P., L.R. and A.M.P. helped in writing-review and editing the manuscript, and supporting the research; project administration was led by G.M.P. and P.L.P. All authors have read and agreed to the published version of the manuscript. Acknowledgement To the researchers, technicians, students and landowners (ranch and sawmill companies) that supported this research, without which it would have been impossible to obtain the valuable information used in this work. Data Availability Data availavility will be provided by my institution (CONICET Argentina) at:https://ri.conicet.gov.ar/First, we need to publish the article, and then upload the data linked to this article.This is compulsory for us at CONICET, but I cannot upload the files "before" the publication. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4958863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360306278,"identity":"4a978a4e-54b8-4760-b653-64cc57a1c2a5","order_by":0,"name":"Guillermo José Martínez Pastur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBADORBxgHj1QKXGUNqAeC2JDRAmEVrko5sff/7Ydi+9v7334IEfFX8Y5N17DBh+7sGtxfDOMTOJg23FuTPOnEs42HPGgMHwzBkDxp5neLTMSDBjONiWkLtBIsfgMGMbUMuMtAQGngP4tKR//gDUkm4A1vIPooXxDx4t8kCVQIclJEC0NBgARZIPMOOzxUDmTJkE0BuGM4BeONhzzJjHgOfwgcMy+GyZ3b75Q0VZgjx/e4/xhx81cnLy7Y2ND9/gs+UGmgCPwQECyUB+BoZIAz71o2AUjIJRMBIBAJg/VvBIJnxLAAAAAElFTkSuQmCC","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"José Martínez","lastName":"Pastur","suffix":""},{"id":360306279,"identity":"c6322508-22a7-46c3-8741-02d24a23840c","order_by":1,"name":"Julián Rodríguez-Souilla","email":"","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":false,"prefix":"","firstName":"Julián","middleName":"","lastName":"Rodríguez-Souilla","suffix":""},{"id":360306280,"identity":"065e6b90-4fba-43f2-be37-f77e2e3d1633","order_by":2,"name":"Yamina Micaela Rosas","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Yamina","middleName":"Micaela","lastName":"Rosas","suffix":""},{"id":360306281,"identity":"9fbfcf10-91e2-4105-8607-9acbfcc698b7","order_by":3,"name":"Natalia Politi","email":"","orcid":"","institution":"Instituto de Ecoregiones Andinas (INECOA)","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Politi","suffix":""},{"id":360306282,"identity":"ca18f571-a97e-4d57-a18f-4f78bc251363","order_by":4,"name":"Luis Rivera","email":"","orcid":"","institution":"Instituto de Ecoregiones Andinas (INECOA)","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Rivera","suffix":""},{"id":360306283,"identity":"bdf33a28-cfe6-4769-ac43-2d12a9ef22fe","order_by":5,"name":"Eduarda M. O. Silveira","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Eduarda","middleName":"M. O.","lastName":"Silveira","suffix":""},{"id":360306284,"identity":"c515e68c-315f-400d-9cfe-077651204628","order_by":6,"name":"Ashley M. Olah","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"M.","lastName":"Olah","suffix":""},{"id":360306285,"identity":"ef545762-6622-4d9c-8ce0-9881a3bf648d","order_by":7,"name":"Anna M. Pidgeon","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"M.","lastName":"Pidgeon","suffix":""},{"id":360306286,"identity":"7f809cf6-6e91-491f-b0a8-137549fe5ec4","order_by":8,"name":"María Vanessa Lencinas","email":"","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Vanessa","lastName":"Lencinas","suffix":""},{"id":360306287,"identity":"740e4dba-9bdf-46b4-a0ea-dd2a4e4596fd","order_by":9,"name":"Pablo Luis Peri","email":"","orcid":"","institution":"National Agricultural Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"Luis","lastName":"Peri","suffix":""}],"badges":[],"createdAt":"2024-08-22 14:41:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4958863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4958863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65700462,"identity":"3e26f1d7-4f1c-4154-89f0-7926e51cac1c","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69597,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis (complete linkage and Euclidean distance) of phenocluster classes defined for Tierra del Fuego (Argentina) by Silveira et al. (2022), showing the phenocluster groups (PG) proposed for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in the present study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/452f87a658ffd256fe630705.png"},{"id":65700464,"identity":"9b11fc91-4fe9-4090-a725-b5ecd3f2f473","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":540339,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina) classified according to the studied phenocluster groups (PG). Other forest types are shown in grey\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/3a05a7d0ad1c8a526f9a514e.png"},{"id":65700465,"identity":"3acb0285-2851-4cd5-90d4-30071a9e4af4","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1001173,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis (complete linkage and Euclidean distance) of phenocluster groups (PG) defined for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina) based on understory species cover.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/9fd9a647cb32e39d67d5e675.png"},{"id":65700467,"identity":"0c4a301c-a111-4ad0-8034-8ed3385d9cb0","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51823,"visible":true,"origin":"","legend":"\u003cp\u003eQuantity of understory species (native + exotic) shared among the phenocluster groups (PG) defined for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina). Percentage indicates the species contribution to the total species richness.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/4b3dcb9569cf4ccc21abb42a.png"},{"id":65700466,"identity":"6b9985b7-f7a5-4a64-8fd8-d8cfb51e1eb1","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5434451,"visible":true,"origin":"","legend":"\u003cp\u003eBi-plots of Canonical Correspondence Analysis for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina) showing samples and selected explanatory variables (upper panel), and vascular plant species and selected explanatory variables (lower panel). Samples are coloured by phenocluster groups (PG-A, PG-B, PG-C, PG-D). SOC = soil organic carbon (%), CC = crown cover (%), UC-I = understory cover of inferior plants (%), UC-F = understory cover of ferns (%), UC-M = understory cover of monocots (%), UC-N = understory cover of native species (%), UC-E = understory cover of exotic species (%), RCH-E = richness of exotic understory species (species per plot). Species codes are shown in Appendix 2, and significance and marginal effects of the explanatory variables are shown in Appendix 3.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/f8973ef9a7880f7d41357df6.png"},{"id":65701609,"identity":"f18ff6ea-c325-4527-8c34-45436d0e256e","added_by":"auto","created_at":"2024-10-01 12:28:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10888327,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/c8d9e4d4-4f27-42a7-afb5-4799c0fd3c21.pdf"},{"id":65700463,"identity":"13845479-eabe-4d54-8517-4d9bc3ecaf0f","added_by":"auto","created_at":"2024-10-01 12:19:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49965,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixs.docx","url":"https://assets-eu.researchsquare.com/files/rs-4958863/v1/b4bd2d71517c66a5ed748484.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conservation value and ecosystem service provision of Nothofagus antarctica forests based on phenocluster categories","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNatural forests host most of the Earth's terrestrial biodiversity (Gardner et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Scheffers et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where intensified anthropogenic activities induce a drastic biodiversity loss with major ecological consequences (Poulsen et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lhoest et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Most conservation strategies are focused on preserving intact forests with unique characteristics inside National Parks or Reserves (set aside strategy or land-sparing), or protecting High Conservation Value Forests (HCVF) in managed forests landscapes, e.g. following The Forest Stewardship Council (FSC) or others proposals, like land-sharing strategy (Benitez et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Areendran et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gr\u0026ouml;nlund et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rosas et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The conservation values of these protected forests were mostly defined according to species uniqueness, ecosystem types, environmental novelties, ecosystem services (ES) or specific needs of local communities (Halmy and Salem \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pour et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in regions with limited ground-based field data, conservation strategies are mainly designed according to forest intactness (e.g. null or low human foot-print), or forest types (FT) defined on the basis of the most dominant tree species (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martinuzzi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of ES (cultural, regulating, provisioning) refers to the goods and benefits that society obtains from natural ecosystems (Braat and de Groot \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), both monetary and non-monetary (Chivulescu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which can be linked to specific FT, e.g. timber values (Peri et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the provision of these ES differ across the landscape for each FT, e.g. timber values or cultural heritage change due to intrinsic characteristics of the forests (timber tree species, heritage, landscape beauty, accessibility) (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peri et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, biodiversity also changes in conjunction with forest type characteristics (e.g., Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and according species groups (e.g. in Patagonia, birds are more abundant in forests while plants are more frequent in humid open-lands) (Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, protected areas defined according to FT based on dominant tree species may not capture all the biodiversity variability across the landscape, requiring other approaches to achieve protection of HCVF. Because different forests have different threats and conservation opportunities (Lhoest et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the understanding of these differences could allow to improve the design of more effective conservation strategies across the landscape (Poulsen et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Panlasigui et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and along different landscapes for widely distributed forests.\u003c/p\u003e \u003cp\u003eOne alternative to basing conservation strategies on forest type or forest intactness is to incorporate variables based on alternative proxies linked to conservation values. In Argentina, natural ecosystem maps (e.g. forest and non-forest ecosystems) were mainly developed based on their floristic and physiographic characteristics (Cabrera \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Paruelo et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Remote sensing and landscape modelling improve these first forest classifications (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), e.g. by including social and biophysical perspectives, or by integrating climate and soil characteristics (Morello et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oyarzabal et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Derguy et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A new approach based on phenoclusters, which combines variables related to forest phenology (e.g. vegetation event timing and greenness) and climate has further improved forest classifications in Argentina (Siveira et al. 2022) as well as in Wisconsin, United States (Silveira et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Phenoclusters can be conceptualised as a vegetation assemblage, or the structure and composition of one area relative to a continuum of vegetation conditions (Hoagland et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The cyclic and seasonal greenness information provided by the phenoclusters is useful for forest planning and management, and biodiversity conservation, particularly in regions where forest ecological information is limited, such as in developing countries (Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another advantage of using phenoclusters to characterise native forests is the capacity to capture phenology and climate gradients within a single FT allowing the sub-classification of forest types at the landscape level (Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main challenge to map FT is finding correspondence between remote-sensing data and specific forest communities that sometimes differ one from each other by a few species, that are difficult to capture by remote sensors (Roelofsen et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bajocco et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pesaresi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Greenness dynamics based on vegetation indexes assist in detecting small-scale forest communities for forest management and conservation planning (D\u0026rsquo;Odorico et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Revermann et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grabska et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, monitoring vegetation phenology helps to detect changes in ecosystem functions, providing solid baseline data to evaluate vegetation dynamics, e.g. droughts, fires or climate oscillations (Bascietto et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Workie and Debella \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, remote sensing provides a useful tool to define regional vegetation phenology classifications as it measures vegetation processes and functions in time and space (Bajocco et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Silveira et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZoning is one of the planning tools used by the Argentinian government to regulate human activities in native forests, as is stated in the National Law 26,331/07, and the provinces are obligated to define land use zones every five years (Martinuzzi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The most traditional approach is to classify the forest ecosystems into different zones by FT according to the present situation and needs of forest planning (Prodan et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), e.g. taxonomy, assemblage of species, phenology, growth and development phases, soil, or topography (Cajander \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1949\u003c/span\u003e; Blasi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Larsen and Nielsen \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Barbati et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). An alternative approach suggests classifying forests based on phenoclusters and the basal area (BA) contribution of each species in the stand (Huertas Herrera et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Mart\u0026iacute;nez Pastur el al. 2024). This alternative approach generates a unified methodological proposal to define and characterise different native forest types at different scales in Argentina based on metrics that are easily measured during forest inventories (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eNothofagus antarctica\u003c/em\u003e (common name \u0026ntilde;ire) forests, one of the main forest types in Patagonia, occur variously from mixed stands with \u003cem\u003eAraucaria araucana\u003c/em\u003e (36\u0026deg;30\u0026rsquo; SL) near the tree-line to monospecific stands in Cape Horn near the marine shoreline (56\u0026deg;00\u0026rsquo; SL) (Steinke et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morello et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oyarzabal et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is one of the most plastic species that can adapt to a great variety of environmental conditions (Ram\u0026iacute;rez et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Soliani et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, this species cannot be managed or conserved as a unique FT (Peri et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soler et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, \u003cem\u003eN. antarctica\u003c/em\u003e is a suitable species to analyse the new proposed classification alternative (phenoclusters) (Huertas Herrera et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our objective was to define the conservation value of one forest type (\u003cem\u003eN. antarctica\u003c/em\u003e forests) in Tierra del Fuego, Argentina, classified according to different functional forest (phenocluster) categories at landscape level. Specifically, we analysed: (i) the provision of ES and potential biodiversity of the different forest phenocluster categories, based on available data at the landscape level; (ii) stand and forest structure characteristics of different forest phenocluster categories based on field surveys at stand level; (iii) animal stocking rate in the different forest phenocluster categories based on field surveys at stand level; and (iv) plant understory characteristics of the different forest phenocluster categories, these last three based on field surveys at stand level. We hypothesised that: (i) one forest type can present different functional characteristics based on land surface phenology and climate variables (Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which can directly affect forest structure and ES provision; (ii) the cyclic and seasonal greenness information provided by the phenoclusters are directly related to biodiversity, particularly to plant understory species, influencing other components (e.g. animal grazing); and (iii) the differences in functional rather than structural or compositional characteristics of ecosystems directly influences the potential biodiversity and conservation values of these forests. We expect that these findings allow us to define better strategies for management and conservation planning, e.g. differential management proposals or better forest representation in reserve networks based on phenoclusters rather than forest types based on canopy-cover composition.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe study area covers the natural distribution of \u003cem\u003eN. antarctica\u003c/em\u003e forests in the Argentinean portion of Tierra del Fuego (53\u0026deg;38\u0026rsquo; to 54\u0026deg;37\u0026rsquo; SL, 66\u0026deg;28\u0026rsquo; to 68\u0026deg;36\u0026rsquo; WL), including 181.5 thousand ha of pure forests (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These deciduous native forests are mainly used for livestock and firewood extraction, and occasionally were managed under silvopastoral systems (Peri et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the study area, the Andes Mountains run from west to east, and define the relief and climate of the region under the influence of Antarctica and both Pacific and Atlantic oceans (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The climate is cold oceanic with strong winds, mean annual temperature of 5.5\u0026deg;C, and rainfall evenly spread over the year, with an annual average of 300 to 500 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Allu\u0026eacute; et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The soils are from glacial origin, where acid brown soils are the most common (Gea Izquierdo et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Modelling outputs used at landscape level\u003c/h2\u003e \u003cp\u003eOur analyses were conducted using a database of unmanaged and managed \u003cem\u003eN. antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina) belonging to the PEBANPA network (Peri et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e), as well as methodologies and modelling previously developed for these forests. These models include GRIDs that were standardised and homogenised at 90-m spatial resolution in ArcMap 10.0 software (ESRI \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e): (i) Cover of the different forest types (FTC), including pure \u003cem\u003eN. antarctica\u003c/em\u003e forests (NA), pure \u003cem\u003eN. pumilio\u003c/em\u003e forests (NP), and mixed evergreen forests (MIX) (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). (ii) Phenoclusters classes (PC) specifically defined for Tierra del Fuego (extracted from Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with 27 different categories based on land surface phenology, climate patterns, normalised difference vegetation index (NDVI), and other related indices. (iii) Provision of ecosystem services (ES) at the landscape level, including cultural (ES-C), regulation (ES-R), provisioning (EC-P), and the combination of all three ES types into total ES (ES-T) (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ES-C includes aesthetic, existence, local identity, and recreational values; ES-R was defined through the net primary productivity of natural ecosystems and habitat quality; ES-P was characterised through timber (e.g. wood for sawmills) and silvopastoral values (e.g. livestock capacity and wood for different uses as firewood) (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peri et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); and ES-T was defined as the average of the three ES types after each GRID was rescaled from 0 to 100 using a linear scale by a function tool into ArcMap 10.0 software (ESRI \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). (iv) Potential biodiversity (BIODIV) at the landscape level (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), was calculated using Environmental Niche Factor Analysis (Hirzel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) to model habitat suitability of the most common understory plant species in each forest type based on 41 explanatory variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Field sampling and data analyses\u003c/h2\u003e \u003cp\u003eWe obtained stand-level field data from 145 plots (even- and uneven-aged stands with \u0026gt;\u0026thinsp;2 ha each) across the landscape, including: (v) Soil properties, using soil organic carbon (%, SOC), soil nitrogen (%, SN), and soil phosphorus (ppm, SP; Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). (vi) Forest structure, characterised by tree dominant height (m, DH), overstory crown cover (%, CC), diameter at breast height (cm, DBH), basal area (m\u0026sup2; ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, BA), and total over bark volume (m\u0026sup3; ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, TOBV) (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aravena Acu\u0026ntilde;a et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). (vii) Animal stocking rate expressed in sheep equivalents (SE) per hectare to make possible further comparisons, including native wild (\u003cem\u003eLama guanicoe\u003c/em\u003e) (SE ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, AS-LG) and domestic mammals (cattle, sheep, horses) (SE ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, AS-CAT) (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Finally, we used previously collected plant understory data (Mart\u0026iacute;nez Pastur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003eb\u003c/span\u003e), to calculate total richness (species per plot, RCH-T), richness of native species (species per plot, RCH-N), richness of exotic species (species per plot, RCH-E), plant understory total biomass (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, UB), total understory cover (%, UC-T), cover of native species (%, UC-N), cover of exotic species (%, UC-E), cover of dicots (%, UC-D), cover of monocots (%, UC-M), cover of ferns (%, UC-F), and cover of inferior plants (mosses and liverworts) (%, UC-I). We identified plant species according to Correa (1969\u0026ndash;1998) and Moore (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), and classified them by their family, taxonomic groups, origin (N\u0026thinsp;=\u0026thinsp;native, E\u0026thinsp;=\u0026thinsp;exotic), and lifeform (T\u0026thinsp;=\u0026thinsp;tree, S\u0026thinsp;=\u0026thinsp;shrub, DS\u0026thinsp;=\u0026thinsp;dwarf-shrub, C\u0026thinsp;=\u0026thinsp;cushion, PH\u0026thinsp;=\u0026thinsp;prostrate herb, EH\u0026thinsp;=\u0026thinsp;erect herb, CG\u0026thinsp;=\u0026thinsp;caespitose grass, RG\u0026thinsp;=\u0026thinsp;rhizomatous grass). For each plant species, we calculated the occurrence (%, OCC) and mean cover at each sampling plot (%, COV). We determined the importance value of each species based on the average values of OCC and COV (IV-OC) after rescaling the values between 0 and 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analyses\u003c/h2\u003e \u003cp\u003eWe conducted a cluster analysis with complete linkage and Euclidean distances among the different phenocluster classes (PC) using the variables employed in the original modelling of Silveira et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Appendix 1). We prioritised the phenocluster classes dominated by \u003cem\u003eN. antarctica\u003c/em\u003e trees and cluster groups obtained in the multivariate analysis, and defined four new phenocluster groups (PG, A to D) for further analyses. We used one-way analysis of variance (ANOVA) to test for differences among phenocluster groups, using Fisher and Tukey tests at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We analysed: (i) ecosystem services provision (ES-C, ES-R, EC-P, ES-T) and potential biodiversity (BIODIV); (ii) soil properties and forest structure variables (SOC, SN, SP, DH, CC, DBH, BA, TOBV); and (iii) plant understory and animal stocking rate (RCH-T, RCH-N, RCH-E, UB, AS-LG, AS-CAT, UC-T, UC-N, UC-E, UC-D, UC-M, UC-F, UC-I). We also analysed the plant understory assemblage among different PG categories through: (i) a cluster analysis with complete linkage and Euclidean distances using mean species cover by sampling plot; and (ii) an overlapping analysis represented with Venn diagrams comparing the three most important groups according the cluster analysis (PG-A, PG-B, PG-C\u0026thinsp;+\u0026thinsp;PG-D) analysing the species occurrence and origin (native or exotic).\u003c/p\u003e \u003cp\u003eFinally, we conducted multivariate analyses to compare the defined phenocluster groups. We compared understory vascular plant assemblages of phenocluster groups using Multi-Response Permutation Procedures (MRPP) with Bray-Curtis distance (McCune et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), including pairwise grouping tests to determine significance of differences (Zimmerman et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). We also conducted a Canonical Correspondence Analysis (CCA) (Ter Braak \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) to evaluate the effect of our potential explanatory variables at the stand level. We excluded dominant height and diameter at breast height from this analysis because some plots had no trees. Due to the low importance of the species with low cover in our matrix of cover of all vascular plant species (156 species x 148 samples; see Appendix 2), we removed any species with \u0026lt;\u0026thinsp;5% cover in our dataset, resulting in a final matrix of 89 species x 148 samples. We tested the significance of our analysis with a Monte-Carlo test with 499 permutations. We used PC-ORD (McCune and Mefford \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) for our MRPPs, and CANOCO 5.4 \u0026copy; Biometrics 1997\u0026ndash;2014 (Ter Braak and Šmilauer \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) for our CCA.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Changes in phenocluster values across the landscape\u003c/h2\u003e \u003cp\u003eThe 27 phenocluster classes (PC) of Tierra del Fuego included different proportions of \u003cem\u003eN. antarctica\u003c/em\u003e forests (0\u0026ndash;96.5%), which represented different functional values due to the regional climate across the landscape (Appendix 1). The cluster analyses based on these differential characteristics allowed us to define different forests phenocluster groups (PG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which included different tree species compositions for the main forest types (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The functionality of phenocluster groups is not totally related to the tree canopy composition, but follows a clear landscape pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We focused on \u003cem\u003eN. antarctica\u003c/em\u003e forests to define four different PG: (i) The first group (PG-A) consisted of two phenoclusters dominated in basal area by pure \u003cem\u003eN. antarctica\u003c/em\u003e (90%) forests intermixed with \u003cem\u003eN. pumilio\u003c/em\u003e patches (10%), and occurred in lowlands under the Atlantic Ocean influence. (ii) The second group (PG-B), consisted of three phenoclusters, dominated by pure \u003cem\u003eN. antarctica\u003c/em\u003e (88%) forests intermixed with \u003cem\u003eN. pumilio\u003c/em\u003e patches (12%) and a few mixed evergreen forests, and occupied inland hilly areas. These two phenocluster groups were located in the ecotone area between the Andes Mountains and open-lands (dry grasslands and shrublands). (iii) The third group (PG-C) consisted of one phenocluster characterised by heterogeneous forested landscape that was heavily degraded (e.g. clear-cuts, fires, pastures) interspersed with closed secondary \u003cem\u003eN. antarctica\u003c/em\u003e forests. (iv)The last group (PG-D) consisted of 21 phenoclusters, and was characterised by \u003cem\u003eN. antarctica\u003c/em\u003e forests with marginal occurrence, in a landscape dominated by \u003cem\u003eN. pumilio\u003c/em\u003e and mixed evergreen forests (71.6%) that occurred near and across the Andes Mountains in the southern areas of Tierra del Fuego.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportional tree canopy composition (NA\u0026thinsp;=\u0026thinsp;\u003cem\u003eNothofagus antarctica\u003c/em\u003e, NP\u0026thinsp;=\u0026thinsp;\u003cem\u003eN. pumilio\u003c/em\u003e, MIX\u0026thinsp;=\u0026thinsp;mixed deciduous-evergreen forests) of phenocluster groups (PG) and classes (PC) in Tierra del Fuego (Argentina) defined for our study. The area of each PC is shown in thousand ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Grey-shaded cells indicate the most important forest type at each PC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(thousand ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMIX\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e551.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e730.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Provision of ecosystem services and potential biodiversity of the phenocluster groups\u003c/h2\u003e \u003cp\u003eWe found significant differences among the provision of ecosystem services (cultural, regulation, provision) between our phenocluster groups, but not for total ecosystem services (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This indicates that different ecosystem services varied across the landscape and phenocluster groups. Cultural ecosystem services were higher in PG-A where ranching and accessibility (e.g. roads) were higher, and lower in PG-B and PG-C where distances to urban areas were greater. Regulation ecosystem services were higher in PG-B and lower in PG-A, and were influenced by topography and ecosystem integrity (e.g. lower human uses). Provisioning ecosystem services were higher in PG-A and PG-D due to ranching and timber uses, respectively. Potential biodiversity at the landscape level differed among phenocluster groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), following a rainfall gradient, being lower in dry open-lands and higher in mountainous landscapes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalyses of variance of ecosystem services provision (ES-C\u0026thinsp;=\u0026thinsp;cultural, ES-R\u0026thinsp;=\u0026thinsp;regulating, EC-P\u0026thinsp;=\u0026thinsp;provisioning, ES-T\u0026thinsp;=\u0026thinsp;total) and potential biodiversity (BIODIV) in each phenocluster group (PG) defined for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eES-C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eES-R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eES-P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eES-T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBIODIV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.5 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.1 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.1 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.9 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.4 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.8 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.9 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.0 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.7 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.9 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.46\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.24\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.19\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.55\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;Fisher test, p\u0026thinsp;=\u0026thinsp;probability. Different letters show differences by Tukey test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Stand characteristics associated to the phenocluster groups\u003c/h2\u003e \u003cp\u003eSoil properties varied among the phenocluster groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Soil organic carbon, nitrogen and phosphorus were highest in PG-D in \u003cem\u003eN. antarctica\u003c/em\u003e stands associated with other forest types (\u003cem\u003eN. pumilio\u003c/em\u003e and mixed evergreen). These soil characteristics influenced other forest structure variables (DH, CC, BA, TOBV) that showed the lowest values in PG-A (e.g. open-forests with lower height and forest volume).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance of soil properties and forest structure variables in each phenocluster group (PG) defined for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina). SOC\u0026thinsp;=\u0026thinsp;soil organic carbon (%), SN\u0026thinsp;=\u0026thinsp;soil nitrogen (%), SP\u0026thinsp;=\u0026thinsp;soil phosphorus (ppm), DH\u0026thinsp;=\u0026thinsp;tree dominant height (m), CC\u0026thinsp;=\u0026thinsp;overstory crown cover (%), DBH\u0026thinsp;=\u0026thinsp;diameter at breast height (cm), BA\u0026thinsp;=\u0026thinsp;basal area (m\u0026sup2; ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), TOBV\u0026thinsp;=\u0026thinsp;total over bark volume (m\u0026sup3; ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.1 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDBH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTOBV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.5 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.1 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122.8 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.3 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e207.5 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.4 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132.1 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003cp\u003e(0.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;Fisher test, p\u0026thinsp;=\u0026thinsp;probability. Different letters show differences by Tukey test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eWe found 156 understory vascular plant species in our sampling plots (full list of plant species, occurrence, cover, and importance values can be found in Appendix 2). The five most common species in \u003cem\u003eN. antarctica\u003c/em\u003e forests were two exotics (\u003cem\u003ePoa pratensis\u003c/em\u003e with IV-OC\u0026thinsp;=\u0026thinsp;0.942 and \u003cem\u003eTaraxacum officinale\u003c/em\u003e with IV-OC\u0026thinsp;=\u0026thinsp;0.785), one native dicot (\u003cem\u003eOsmorhiza depauperata\u003c/em\u003e with IV-OC\u0026thinsp;=\u0026thinsp;0.296), and two native monocots (\u003cem\u003eFestuca magellanica\u003c/em\u003e with IV-OC\u0026thinsp;=\u0026thinsp;0.409 and \u003cem\u003eTrisetum spicatum\u003c/em\u003e with IV-OC\u0026thinsp;=\u0026thinsp;0.192). Total understory vascular plant richness per plot differed among phenocluster groups, being greater in PG-B and decreasing in the other phenocluster groups (coastal and mountainous areas). This trend was concordant with exotic species response per plot due to native richness per plot remaining stable across the landscape (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The understory covers also varied among the phenocluster groups, where exotic and monocot species were higher in drier areas with more farming activities (PG-A and PG-B) than in the other PGs. Dicot cover increased with rainfall (from PG-A to PG-D), but decreased in degraded areas (PG-C) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Finally, we found no significant differences in understory biomass and animal stocking rate (wild or domestic) among phenocluster groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalyses of variance of plant understory and animal stock variables in the phenocluster groups (PG) defined for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina). RCH-T\u0026thinsp;=\u0026thinsp;total richness of the understory (species per plot), RCH-N\u0026thinsp;=\u0026thinsp;richness of native species in the understory (species per plot), RCH-E\u0026thinsp;=\u0026thinsp;richness of exotic species in the understory (species per plot), UB\u0026thinsp;=\u0026thinsp;understory total biomass (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), AS-LG\u0026thinsp;=\u0026thinsp;animal stock considering native mammals (\u003cem\u003eLama guanicoe\u003c/em\u003e) (sheep equivalent, SE ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), AS-CAT\u0026thinsp;=\u0026thinsp;animal stock considering domestic mammals (cattle, sheep and horses) (SE ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), UC-T\u0026thinsp;=\u0026thinsp;total understory cover (%), UC-N\u0026thinsp;=\u0026thinsp;understory cover of native species (%), UC-E\u0026thinsp;=\u0026thinsp;understory cover of exotic species (%), UC-D\u0026thinsp;=\u0026thinsp;understory cover of dicots (%), UC-M\u0026thinsp;=\u0026thinsp;understory cover of monocots (%), UC-F\u0026thinsp;=\u0026thinsp;understory cover of ferns (%), UC-I\u0026thinsp;=\u0026thinsp;understory cover of inferior plants (mosses and liverworts) (%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCH-T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCH-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRCH-E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAS-LG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAS-CAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1533.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.1 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1494.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e674.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1117.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003cp\u003e(0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003cp\u003e(0.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003cp\u003e(0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUC-T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eUC-N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eUC-E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUC-D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUC-M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUC-F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eUC-I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.1 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.9 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.7 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.2 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.1 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.1 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.8 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.3 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.9 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF(p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003cp\u003e(0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003cp\u003e(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003cp\u003e(0.062)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;Fisher test, p\u0026thinsp;=\u0026thinsp;probability. Different letters show differences by Tukey test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Understory species assemblage associated to the phenocluster groups\u003c/h2\u003e \u003cp\u003eOur cluster analysis based on species cover (Appendix 2) showed that coastal forests (PG-A) were different from hilly forests (PG-B), which joined with a greater linkage distance to southern \u003cem\u003eN. antarctica\u003c/em\u003e forests (PG-C and PG-D) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Total richness changed according to different phenocluster groups, being higher in northern forests (PG-A\u0026thinsp;=\u0026thinsp;120 species, PG-B\u0026thinsp;=\u0026thinsp;125 species) than in southern forests (PG-D\u0026thinsp;=\u0026thinsp;111 species), and decreasing in degraded forests (PG-C\u0026thinsp;=\u0026thinsp;53 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The different phenocluster groups shared 53.2% of the total richness (72 native and 11 exotic species) 23.7% of species were shared between two phenocluster groups (12.2% between PG-A and PG-B, 6.4% between PG-A and PG-C\u0026thinsp;+\u0026thinsp;D, 5.1% between PG-B and PG-C\u0026thinsp;+\u0026thinsp;D), and 23.1% were found in only one group (5.1% for PG-A, 9.6% for PG-B, 8.3% for PG-C\u0026thinsp;+\u0026thinsp;D). The exclusive species in PG-A included 4 native monocot species, and 3 native and one exotic dicot species, while in PG-B exclusive species included 3 native and 2 exotic monocots, and 9 native and one exotic dicot species. Moreover, the exclusive species in PG-D included 4 native monocot species, and 6 native and 3 exotic dicot species. PG-C did not harbour any species exclusively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBi-plots of Canonical Correspondence Analysis showed a great overlap in species assemblages among plots of the phenocluster groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the overlap followed the gradient in the landscape pattern observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The significance of all canonical axes was F\u0026thinsp;=\u0026thinsp;5.744 (p\u0026thinsp;=\u0026thinsp;0.002), and the cumulative percentage variance of species cover and explanatory variables was 52.1% (axes 1 and 2). The Multi-Response Permutation Procedure (MRPP) detected significant differences among pairwise comparisons, except for PG-C vs. PG-B, and PG-C vs. PG-D (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These differences showed that the three main groups were different, while degraded forests (PG-C) presented intermediate characteristics between PG-B and PG-D. The most important explanatory variables were understory cover of plant groups, overstory crown cover, soil organic carbon, and richness of exotic understory species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Appendix 3). Cover of exotic species (UC-E and RCH-E) and monocots were associated with northern \u003cem\u003eN. antarctica\u003c/em\u003e forests (PG-A and PG-B), while inferior plant cover (mosses and liverworts), native species cover (UC-I and UC-N), and soil organic carbon were associated with southern forests close to mountains. The cover of vascular understory species was also associated with these explanatory variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), where most of the species occurred at the centre of the bi-plot graph. However, some species were particularly associated with some phenocluster group categories. For example, \u003cem\u003eStellaria media\u003c/em\u003e, \u003cem\u003eDactylis glomerata\u003c/em\u003e, \u003cem\u003eTrifolium repens\u003c/em\u003e and \u003cem\u003eAgrostis capillaris\u003c/em\u003e were associated with open forests with intensive farming uses, while \u003cem\u003eMarsipospermum grandiflorum\u003c/em\u003e, \u003cem\u003eCarex magellanica\u003c/em\u003e, \u003cem\u003eEmpetrum rubrum\u003c/em\u003e and \u003cem\u003eGunnera magellanica\u003c/em\u003e were associated with humid forests near peatlands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of vascular plant vegetation assemblages among phenocluster groups (PG-A, PG-B, PG-C, PG-D) by Multi-Response Permutation Procedure (MRPP) in \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego (Argentina).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMRPP statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-A vs. PG-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-A vs. PG-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-A vs. PG-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-B vs. PG-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-B vs. PG-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG-C vs. PG-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;statistic of MRPP, A\u0026thinsp;=\u0026thinsp;chance-corrected within-group agreement, (p)\u0026thinsp;=\u0026thinsp;probability associated with T.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Provision of ecosystem services and potential biodiversity for different phenocluster categories\u003c/h2\u003e \u003cp\u003ePhenocluster classes are a useful tool to separate fine categories within forest types, and particularly to increase the number of categories in each forest type, e.g. in areas with low tree richness (Martínez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We found that the grouping of phenocluster categories captured a gradient in forest tree species composition, where \u003cem\u003eN. antarctica\u003c/em\u003e dominance was as follows from highest to lowest; PG-A \u0026gt; PG-B \u0026gt; PG-C \u0026gt; PG-D. Evergreen mixed forests followed an inverse pattern. \u003cem\u003eN. antarctica\u003c/em\u003e forests had lower tree height and more open canopies, allowing a more abundant and richer understory (Quinteros et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Alonso et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) than the other forest types (e.g. \u003cem\u003eN. pumilio\u003c/em\u003e and mixed evergreen forests) which had closed canopies (Promis et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mestre et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cem\u003eNothofagus antarctica\u003c/em\u003e trees grow in contrasting environments, presenting a remarkable adaptability with different morphotypes according to the different natural environments (Ramírez et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Soliani et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), e.g. they can grow as shrubs in tree-line forests and peatlands, and as trees with \u0026gt; 20 m height in flat zones with deep soils (Martínez Pastur et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martínez et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fajardo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These differences affect ecosystem services provision, especially regulating (e.g. closed intact forests presented higher values) and provisioning (e.g. a balance between livestock uses and timber values). Additionally, cultural values of these forests are related to proximity and accessibility to urban areas (Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Differences in forest structure and function also influenced the potential biodiversity, where landscapes with higher forest diversity (e.g. more forest types) were associated with more species, as has been described for other Patagonian forests (Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our analyses showed that the higher provisioning ecosystem services occurred in forests with low cultural values but with higher regulating services and potential biodiversity. This relationship can lead to potential trade-offs in land use planning, such as between provisioning of ecosystem services and biodiversity, which was found elsewhere in Tierra del Fuego (Martínez Pastur et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results support the hypothesis that functional characteristics based on land surface phenology and climate variables (Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) directly explain the response of ecosystem services provision and potential biodiversity. In consequence, forest management and conservation planners must set geographic priorities not only based on the compositional or structural dimensions of biodiversity but also on the functional dimensions (Cazorla et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Capturing functional diversity using satellite imagery allows us to understand the mechanisms of ecosystem functioning and biodiversity, where spatial and spectral resolution are the key components when scaling diversity assessments from regional to continental scales (Helfenstein et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding the main causes and consequences of spatial heterogeneity in ecosystem functions allows the development of management and conservation strategies (Hao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Brooks et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and elucidation of positive synergies among ecosystem multifunctionality, ecosystem services, and ecological stability (Oliver et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Manning et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cazorla et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Forest structure changes among forest phenocluster categories\u003c/h2\u003e \u003cp\u003eEnvironmental characteristics of forest stands significantly varied across the different phenocluster categories. Nutrients in forest soils (carbon, nitrogen, phosphorus) of Tierra del Fuego vary across the landscape relative to climate, parent material, and natural erosion processes (e.g. glacial soils) (Hildebrand-Vogel et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Decker and Boerner \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Romanyà et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Peri et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This also influences forest type occurrence (e.g. phosphorus is a limiting factor for \u003cem\u003eN. pumilio\u003c/em\u003e forests) and forest structure values (e.g. site quality) (Bahamonde et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aravena Acuña et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our analyses, forest structure influenced by land surface phenology and climate variables defined the different phenocluster types. This supports the general concept that functionality affects ecological processes associated with biomass allocation and site quality (proxy: productivity) (Hao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Muller-Landau et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Augusto and Boča \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, this directly influences available resources at the understory level, e.g. open canopies increasing light availability, soil moisture, wind and temperature dynamics across the growing season (Mestre et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huertas Herrera et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, we again found support for the hypothesis that phenoclusters directly influence the forest structure at stand level, and in ecosystem provisioning services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Animal use of forest phenocluster categories\u003c/h2\u003e \u003cp\u003eWe found that understory biomass varied greatly, but did not significantly differ among phenocluster categories. Understory biomass has been the main factor associated with native (\u003cem\u003eLama guanicoe\u003c/em\u003e) and domesticated herbivore (e.g. cattle) stocking rate (Soler et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), however we observed different trends between biomass and animal stocking rates among phenoclusters. We found higher values of animal stock in areas of low forest canopy cover (e.g. PG-A). However, great variations in forest characteristics at landscape level can be influenced by many other non-measured factors such as management and other human impacts (e.g. poaching) (Peri et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Forest functionality is often related to forest fauna (e.g. Yuan et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Enquist et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); however, detailed studies must be conducted in this context to accept or refute the hypothesis that phenoclusters are linked to the animal stocking rate as one of the most important provisioning ecosystem services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Plant species assemblage differences among forest phenocluster categories\u003c/h2\u003e \u003cp\u003ePlant understory richness that significantly varied with forest functionality was related to phenocluster categories. These differences were mainly associated with exotic plant richness and cover, which modified the natural richness and cover of \u003cem\u003eN. antarctica\u003c/em\u003e understory due to management and other human-related impacts (Quinteros et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peri et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These changes were also associated with the plant species cover, both for monocots and dicots. Species richness and cover are influenced by forest functionality and productivity (Waide et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Maestre et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Enquist et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results show that some forest phenoclusters can be more resilient to the species invasion (e.g. phenoclusters in PG-D presenting a lower number of invasive plants). This finding may assist conservation strategies for different functional forests with contrasting forest structure and stand values. Community structure, ecosystem functioning and services can influence species invasion (e.g. Renault et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, ecosystem functions that sustain key-ES should be identified and prioritised for conservation action (Mori et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this context, the use of classifications based on phenoclusters can help to develop better resilience-based management strategies. The ability to detect an ecological threshold under a disturbance gradient should therefore be essential to establish a backstop for preventing forest degradation. These perspectives can take us beyond simply invoking the precautionary principle of conserving biodiversity to a predictive science that informs practical solutions to cope with uncertainties and ecological surprises in a changing world (Sasaki et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur analyses showed that nearly half of understory richness was shared among the different phenocluster categories, and the other half occurred in more narrow habitats. Southern Patagonian forests are known to have different understory richness across the landscape (Martínez Pastur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but the differences were not fully explained. For example, understory richness was higher in more productive stands (e.g. higher site quality \u003cem\u003eN. pumilio\u003c/em\u003e forests; Gallo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and richness was greater in uneven-aged \u003cem\u003eN. antarctica\u003c/em\u003e mature stands compared to even-aged stands (Martínez Pastur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). On the other hand, species richness was intermediate in \u003cem\u003eN. antarctica\u003c/em\u003e forests compared to other forest types, and was greatest in north-western Tierra del Fuego, probably related to landscape heterogeneity and fragmentation by enrichment through dispersion from neighbouring habitats (Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The identity of plant species was associated with specific functional forests, supporting the idea that planning must include different phenocluster categories to achieve more effective conservation strategies, consistent with strategies for different forests across the world (Pesaresi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Witt et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Silveira et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including Argentina (Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, we found support for the hypothesis that differences in functional rather than structural or compositional characteristics of ecosystems were related to potential biodiversity and conservation values of the studied forests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Functional forests categorization can improve management and conservation strategies\u003c/h2\u003e \u003cp\u003eConservation strategies must be designed and coordinated at a large scale (landscape, national or continental scale) and must balance economic development and provision of ecosystem services (Poulsen et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lhoest et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the processes behind forest ecosystem service provision, as well as their trade-offs with biodiversity conservation, is a useful tool to support spatial planning and land management (Poirazidis et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Carvalho-Santos et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conservation strategies must include land sparing (e.g. creation of national parks and provincial reserves) and land sharing (e.g. increasing the conservation values in managed landscapes) (Fischer et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lencinas et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Argentina promulgated the National Law 26,331/07 called the “Minimum Budgets for Environmental Protection of Native Forests”. This law includes forest management proposals with a social awareness, changes in forest cover, administrative restrictions for forest removal proposals, and long-term forest policies (Martínez Pastur et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Thus, the proposals for sustainable forest management and conservation need accurate tools to maximise the implementation efficiency across the country (Koff et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mapping forest types for large areas (e.g. regional level) using ground-based data is rarely logistically feasible (Zhu and Liu \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wadoux et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and is more common in temperate cold forests with simple and predictable stand structures (Harris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portillo-Quintero et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Current global forest maps provide valuable information without considering differences in forest types (Hansen et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Potapov et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and are mostly based on floristic and physiographic characteristics (e.g. Cabrera \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). However, these tools are limited in their effectiveness to capture the ecosystem structure variability at landscape level. More recently, the use of remote sensing and landscape modelling improved these first mapping efforts (Morello et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oyarzabal et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recently, the addition of vegetation phenology variations (e.g. event timing and greenness; Silveira et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and species or climate variation have further improved forest type mapping efforts (Martínez Pastur et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests provide diversification of farm products by sustaining sheep and cattle production under silvopastoral systems, which provides income from meat, wool, and a range of wood products including poles, firewood, and timber for rural construction purposes. In addition, silvopastoral systems in the region provide other ES such as water regulation, biodiversity conservation, improved soil and water quality, carbon sequestration, recreation, and cultural identity. These forests grow in a mosaic with different structure and floristic compositions as a result of livestock grazing and silvicultural management, in interaction with natural (e.g. drought) and anthropogenic (e.g. fires, introduction of plant species) factors (Peri et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fajardo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent proposals have improved the assessment of synergies and trade-offs among ecosystem services and biodiversity at different spatio-temporal scales (Raudsepp-Hearne et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Martínez Pastur et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peri et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), allowing for better land use planning and effectiveness of natural reserve networks (Rosas et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these strategies are limited in their ability to capture most forest landscape variability (e.g. Martínez Pastur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Carrasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, suitable forest reserve networks should take into consideration (i) different forest types and phenoclusters categories; (ii) unique potential biodiversity that these categories can host; (iii) provision of different ecosystem services, as well as the consideration of potential synergies and trade-offs among these services and biodiversity; and (iv) the human impacts or economic activities (Rosas et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Monitoring vegetation phenology is a way to detect differences in ecosystem functions, providing baseline data to monitor vegetation dynamics related to events such as drought, fire, spring frost, land use changes, and climate oscillations (Peñuelas et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Workie and Debella \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bajocco et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results allow the definition of better strategies for management and conservation planning, e.g. differential management proposals or representation in reserve networks based on phenoclusters rather than forest types based on canopy-cover composition.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this paper, we define the conservation value of different functional forest types based on phenoclusters categories for \u003cem\u003eN. antarctica\u003c/em\u003e forests at the landscape level. The approach relies on the integration of landscape and stand level data with forest classification based on regional climate and phenological characterization through satellite images. The use of phenocluster categories can divide the forest types in sub-types with different characteristics, including (i) ecosystem services, (ii) soil, (iii) forest structure, and (iv) biodiversity values. The cyclic and seasonal greenness information provided by the phenoclusters are directly related to plant understory biodiversity, where functional rather than structural or compositional characteristics of forest ecosystems are the main explanatory variable. These findings can support better forest management and conservation, for example through differential management of each phenocluster category, or selection of representative forests of each category into the reserve network design based on phenoclusters rather than forest types defined by tree canopy-cover composition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was conducted with the financial support of (i) Proyectos de Desarrollo Tecnol\u0026oacute;gico y Social (PDTS-0398) MINCyT (Argentina) (2020\u0026ndash;2023), and (ii) Proyectos Interinstitucionales en Temas Estrat\u0026eacute;gicos (PITES-03) MINCyT (Argentina) (2022\u0026ndash;2024).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.M.P., M.V.L. and P.L.P. conceived and designed the experiments; J.R.S., Y.M.R., E.M.O.S. and A.M.O. collaborated in fieldwork and data analyses and in review and editing of the manuscript; N.P., L.R. and A.M.P. helped in writing-review and editing the manuscript, and supporting the research; project administration was led by G.M.P. and P.L.P. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eTo the researchers, technicians, students and landowners (ranch and sawmill companies) that supported this research, without which it would have been impossible to obtain the valuable information used in this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availavility will be provided by my institution (CONICET Argentina) at:https://ri.conicet.gov.ar/First, we need to publish the article, and then upload the data linked to this article.This is compulsory for us at CONICET, but I cannot upload the files \"before\" the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams B, Iverson L, Matthews S, Peters M, Prasad A, Hix D (2020) Mapping forest composition with Landsat time series: An evaluation of seasonal composites and harmonic regression. 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Ecology 66(2):606-611. https://doi.org/10.2307/1940409.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Conservation](https://link.springer.com/journal/44353)","snPcode":"44353","submissionUrl":"https://submission.springernature.com/new-submission/44353/3","title":"Discover Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"functional forests, potential biodiversity, forest structure, soil characteristics, forest management and conservation planning, Patagonia","lastPublishedDoi":"10.21203/rs.3.rs-4958863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4958863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRemote sensing and landscape modelling improve forest classification. One approach combines variables based on forest phenology and climate to characterisefunctional rather than structural or compositional characteristics of ecosystems (phenoclusters). However, there are few studies about the correlation between these new modelling approaches and forest classifications based on ground surveys. Our objective was to define the conservation value of different functional forests, based on phenocluster categories, for \u003cem\u003eNothofagus antarctica\u003c/em\u003e forests in Tierra del Fuego. We used different available features model outputs standardised and homogenised at 90-m spatial resolution (phenoclusters, ecosystem services, potential biodiversity), and ground truthdata from 145 stands (soil characteristics, forest structure, animal stocking rate, understory biodiversity). The phenocluster categories were compared using uni- and multivariate analyses. The use of phenocluster categories allowed sorting of the \u003cem\u003eN. antarctica\u003c/em\u003e forest type into contrasting subtypes with different characteristics, including (i) cultural, regulating, and provisioning ecosystem services and potential biodiversity at landscape level (F = 1.8-87.6), (ii) soil organic carbon, nitrogen, and phosphorous properties (F = 4.2-5.2), (iii) tree dominant height, overstory crown cover, basal area, and bark volume forest structure (F = 0.1-6.3), animal stock (F = 1.0-1.9), and (iv) understory plant richness (F = 1.0-9.4) at stand level. Significant differences were detected in the multivariate analyses (classifications and ordinations) supporting the split of this forest type into four functional forest subtypes: (i) coastal forests near the Atlantic Ocean, (ii) highland forests close to the steppe, (iii) ecotone areas associated with \u003cem\u003eN. pumilio\u003c/em\u003eforests, and (iv) degraded and secondary forests. The cyclic and seasonal greenness information provided by the phenoclusters were directly related to plant understory diversity, where functional rather than structural or compositional characteristics of forest ecosystems were the main explanatory variable. Our findings can support better management and conservation proposals, e.g. different management strategies for each phenocluster category, or selection of representative forests into a reserve network design based on phenoclusters rather than forest types defined by tree canopy-cover composition.\u003c/p\u003e","manuscriptTitle":"Conservation value and ecosystem service provision of Nothofagus antarctica forests based on phenocluster categories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 12:19:48","doi":"10.21203/rs.3.rs-4958863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-08T06:00:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-05T17:24:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-27T08:53:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191639178094676475375759582816313933453","date":"2024-09-12T11:37:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55727486461931773102510794635013528233","date":"2024-09-12T07:32:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-10T07:26:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-09T05:20:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-02T04:12:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Conservation","date":"2024-08-22T14:40:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Conservation](https://link.springer.com/journal/44353)","snPcode":"44353","submissionUrl":"https://submission.springernature.com/new-submission/44353/3","title":"Discover Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22c082d5-2171-47ab-98ad-aea328d5b3d1","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-19T04:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-01 12:19:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4958863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4958863","identity":"rs-4958863","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}