Potential biodiversity map of understory plants for Nothofagus forests in Southern Patagonia: Analyses of landscape, ecological niche and conservation values

Science of the Total Environment 682 (2019) 301–309

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Potential biodiversity map of understory plants for Nothofagus forests in Southern Patagonia: Analyses of landscape, ecological niche and conservation values Yamina Micaela Rosas a,⁎, Pablo L. Peri b, María Vanessa Lencinas a, Guillermo Martínez Pastur a a

Laboratorio de Recursos Agroforestales, Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Houssay 200, 9410 Ushuaia, Tierra del Fuego, Argentina b Instituto Nacional de Tecnología Agropecuaria (INTA), Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CC 332, 9400 Río Gallegos, Santa Cruz, Argentina

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Habitat suitability models allowed define habitat requirement for studied species. • Map of potential biodiversity synthetizes the information for the indicator species. • Understory potential biodiversity changed across the different forest types. • Nothofagus antarctica forest presented the highest potential biodiversity. • Ecotone areas with different vegetation types supported more potential biodiversity.

a r t i c l e

i n f o

Article history: Received 16 April 2019 Received in revised form 13 May 2019 Accepted 13 May 2019 Available online 16 May 2019 Keywords: Habitat suitability ENFA Forest landscape Marginality/specialization Conservation

a b s t r a c t The role of understory plants in native forests is critical for ecosystem function, wildlife protection and ecosystem productivity. The interest to estimate biodiversity increased during the last decades at landscape level. The objective was to elaborate a map of potential biodiversity (MPB) of understory species of Nothofagus forest using potential habitat suitability maps (PHS) of 15 plants in Santa Cruz province, Argentina. Additionally, we asked the following questions: (i) Were plant species differentially distributed according to the forest types?, (ii) do forest types represent different plant species assemblage with specific ecological niche requirements?, and (iii) is it possible to detect hotspots in the MBP according to the forest types? We used 721 plots database of vascular plants, from where 15 indicator species were identified. The assemblage species for different forests (Nothofagus antarctica, N. pumilio and evergreen mixed) were analysed using a detrended correspondence analysis. Also, we explored 41 potential explanatory variables to develop PHS, and combined these maps to obtain one MPB (1–100%). Finally, we analysed the outputs into a GIS through different landscapes alternatives to detect hotspot areas. Marginality and specialization values allowed identifying species assemblage that presented similar variability in the habitat requirements. MPB varied across the landscape, with higher values in the south and lower values near glaciers. MPB had the highest values in N. antarctica forest with N50% cover at landscape level. N. antarctica present more hotspots than N. pumilio forests, mainly in the south, compared to mixed

⁎ Corresponding author. E-mail addresses: [email protected] (Y.M. Rosas), [email protected] (P.L. Peri), [email protected] (M.V. Lencinas), [email protected] (G. Martínez Pastur).

https://doi.org/10.1016/j.scitotenv.2019.05.179 0048-9697/© 2019 Published by Elsevier B.V.

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evergreen forests which present few hotspots near glaciers. These results can be used as a tool to design new management and conservation strategies at landscape level. © 2019 Published by Elsevier B.V.

1. Introduction In forest ecosystems, the role of understory vegetation are critical as regulator of ecosystem function, wildlife protection, ecosystem productivity, and other ecological services (Gilliam, 2007; Antos, 2009; Lencinas et al., 2011; Wang et al., 2018). Usually, plant species of forest understory present heterogeneous distribution patterns associated with the tree canopy structure, composition (Barbier et al., 2008; Antos, 2009), microenvironmental characteristics and site conditions (Lencinas et al., 2008; Wang et al., 2018). Topographic factors (e.g. elevation or slope) affect the spatial availability of solar radiation, temperature and precipitation (Wang et al., 2018). Low light levels provide less energy for plant growth, and when canopy trees are remove (silviculture practices), understory plants growth significantly increase (Antos, 2009). Physical properties of soils (e.g. water and nutrient contents) and competition with overstory affect the understory plants development (Gilliam, 2007; Barbier et al., 2008). Other landscape characteristics, such as distance to rivers can also affect the distribution of the understory due to a drought tolerance gradient (Antos, 2009). It is not clear the mechanisms involved in the effects of tree species on understory vegetation diversity (Barbier et al., 2008), and few studies had related the distribution of plants species richness with regional environmental variables to characterize the potential biodiversity of forests (Martínez Pastur et al., 2016). Traditionally, understory biodiversity studies relied on groundbased observations using field data (Hakkenberg et al., 2018). The efforts and costs to sample large-scale field (Rocchini et al., 2015), mainly in remote areas such as in Patagonia, represent a challenge for studies at landscape level. In context, an increase of landscape ecology studies including qualitative and quantitative models to predict habitat and species distributions has been developed since the mid-1990s (Wiersma et al., 2011). Most of the biodiversity studies have focused on empirical relationships between remotely sensed data and species diversity measured in the field (Hakkenberg et al., 2018). Remote sensing is one of the most powerful tools to estimate biodiversity hotspots and to predict potential habitability (Rocchini et al., 2015). Some software (e.g. Biomapper and MaxEnt) are identify as useful to develop models of potential habitat suitability (PHS) (Hirzel et al., 2001; Phillips et al., 2006; Rodríguez et al., 2007; Boria et al., 2014). These models relate environmental characteristics (climatic, topographic and landscape variables) with species occurrence (Guisan and Zimmermann, 2000; Hirzel et al., 2002). One advantage of Biomapper software and the new available remote sensing alternatives is the development of PHS maps in areas of lack databases and with only presence data of the species (Rosas et al., 2017). Modelling the PHS have been widely used for management planning of common tree species (Falk and Mellert, 2011) and for conservation strategies of threatened tree species (Rupprecht et al., 2011; Silva et al., 2017). However, modelling multiple habitat suitability of forest understory species are less common (Zaniewski et al., 2002; Martínez Pastur et al., 2016). The combination of different PHS provides a unique map of potential biodiversity (MPB) and synthetizes the information for several species (Martínez Pastur et al., 2016; Rosas et al., 2018). In addition, these maps allowed to: (i) understand the ecology of the species (Martínez Pastur et al., 2016), (ii) identify potential hot-spots of biodiversity (Zaniewski et al., 2002), (iii) variations in potential habitat due to climate change (Falk and Mellert, 2011), and (iv) develop new management and conservation planning proposals at landscape scale (Martínez Pastur et al., 2016; Silva et al., 2017). In Southern Patagonia, Nothofagus forests are distributed from 46° to 52° SL and are presented in a narrow strip from 88 m.a.s.l. near the steppe to 1461 m.a.s.l in the Andean mountains. These forests occur

along environmental gradients, mainly defined by rainfall patterns, elevations, temperatures and soil quality conditions (Veblen et al., 1996; Ohlemüller and Wilson, 2000). These forests, also present exceptional ecological characteristics by growing at extreme latitudes (Premoli et al., 2007; Martínez Pastur et al., 2016) and deliver important provisioning ecosystem services (e.g. livestock and timber) for local communities (Martínez Pastur et al., 2000, 2017; Peri et al., 2016a). In the landscape three main forest types developed (Veblen et al., 1996; Martínez Pastur et al., 2016; Lencinas et al., 2008): (i) Nothofagus antarctica (G. Forster) Oerst (NA) forests occupy 859 km2 and were mainly associated with grassland areas (Gargaglione et al., 2014). This forest type was important for cattle grazing (e.g. silvopastoral system) being one of the most important economic alternative for the ranches (Peri et al., 2016a); (ii) Nothofagus pumilio (Poepp. & Endl.) Krasser (NP) forests occupy 2294 km2 and were growing associated with mountain environments, and was important for timber production (Martínez Pastur et al., 2000); and (iii) mixed forests (MIX) dominated by evergreen N. betuloides (Mirb.) Oerst. and other Nothofagus species, as well as other secondary tree species (Drimys winteri Forster et Foster F., Embothrium coccineum Forster et Foster F. and Maytenus boaria Molina). This last forest type occupy 210 km2 in wet areas associated to mountain environments, mainly located in protected natural areas. The management and conservation strategies for these forests are based on economic values or location (accessibility or national policies for international borders) (Rosas et al., 2017), rather than based on biodiversity values (Lindenmayer and Franklin, 2002; Luque et al., 2011). In this sense, improve the prediction of potential biodiversity is crucial to develop better alternatives of landscape management and conservation planning (Martínez Pastur et al., 2016; Silva et al., 2017). The objective of this paper was to elaborate a map of potential biodiversity (MPB) of understory species of Nothofagus forests using potential habitat suitability maps of 15 plants with the highest cover-occurrence index in Santa Cruz province, Argentina. Additionally, we asked the following questions: (i) plant species are differentially distributed according to the forest types?, (ii) do forest types present different plant species assemblage with specific ecological niche requirements?, and (iii) is it possible to detect hot-spots in the MBP for the different forest types? 2. Methods The study area included the Nothofagus forests and the alpine vegetation region (Oliva et al., 2004), which occupy a narrow strip in the west area of Santa Cruz province (Argentina) (46°00′ to 52°00′ S, 71°10′ to 73°20′ W) (Fig. 1). Nothofagus forests presented greater variations in their overstory dominant species along the latitude gradient, where north and central areas are dominated by N. pumilio and mixed evergreen forests, while N. antarctica prevails in the southern area (Fig. 1C). We used a 721 plots database of understory plants of Nothofagus forests and alpine vegetation region, where 86 plots belong to PEBANPA Network (Parcelas de Ecología y Biodiversidad de Ambientes Naturales en Patagonia Austral) (Peri et al., 2016b), and 635 plots belong to native forests provincial inventory and FAMA INTA laboratory (Forestal, Agricultura y Manejo del Agua research group). These databases include species cover (%) and frequency of occurrence (%). We calculated a cover-occurrence index (COI) (0 to 1) as a combination of the relative cover (average cover of each plant species and maximum value of average cover of all plants in the region) and relative frequency (relative occurrence of each plant and maximum value of relative occurrence of all plants in the region) of the species. With this COI indexes we select the 15 most important species (Appendix A). The database was complemented with

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Fig. 1. Characterization of the study area: (A) location of Argentina (dark grey) and Santa Cruz province (black); (B) main ecological areas (light grey = dry steppe, grey = humid steppe, medium grey = shrub-lands, dark grey = sub-Andean grasslands, black = forests and alpine vegetation) (modified from Oliva et al., 2004); (C) Nothofagus forests, light grey = N. pumilio, grey = N. antarctica, black = mixed forests (CIEFAP-MAyDS, 2016).

presence data of the selected species using the Sistema Nacional de Datos Biológicos of Ministerio de Ciencia, Tecnología e Innovación Productiva (http://www.datosbiologicos.mincyt.gob.ar). The selected species were: (i) six shrubs belong to Asteraceae (Baccharis magellanica and Chiliotrichum diffusum), Berberidaceae (Berberis microphylla), Ericaceae (Empetrum rubrum and Gaultheria mucronata) and Escalloniaceae (Escallonia rubra) families; (ii) four perennial herbs belong to Rosaceae (Acaena magellanica), Cyperaceae (Carex andina), Apiaceae (Osmorhiza chilensis) and Violaceae (Viola magellanica) families; (iii) four grasses belong to Poaceae family (Avenella flexuosa, Festuca gracillima, Festuca magellanica, Festuca pallescens); and (v) one fern belong to Blechmaceae family (Blechnum penna-marina). Firstly, a detrended correspondence analysis (DCA) using CANOCO 5.0 (Ter Braak and Šmilauer, 2009) and an indicator value analyses using PC-ORD 5 (McCune and Mefford, 1999) were used to determine

the associated understory species for each forest type (NA, NP, and MIX). Secondly, based on the Environmental Niche Factor Analysis (ENFA) (Hirzel et al., 2002), we elaborated a series of spatially explicit PHS models for each selected understory species using Biomapper 4.0 (Hirzel et al., 2004). ENFA compares the eco-geographical variables distribution for a presence data set consisting of locations where the species has been detected with the predictor distribution of the whole study area (Hirzel et al., 2001). For this, we followed the methodology proposed for Martínez Pastur et al. (2016) and Rosas et al. (2018). Using ENFA, we calculated two indexes: (i) global marginality, where higher values (≥1) indicates that species' s requirements differ from the average habitat conditions and lower values (close to 0) shows that the species is found anywhere else (Hirzel and Le Lay, 2008), and (ii) global tolerance or specialization (tolerance−1) (from 0 to infinite) where lower values indicate that species tends to live in a wide range

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of environment conditions (Hirzel et al., 2002; Martínez Pastur et al., 2016). We used a distance of geometric-mean algorithm to perform each PHS, which provides a good generalization of the niche model (Hirzel and Arlettaz, 2003). The resulting PHS maps had scores that varied from 0 (minimum) to 100 (maximum habitat suitability). Biomapper software, use a cross-validation analyses when database used to modelling is only presence and compare the model results with a random modelling through (Hirzel et al., 2006): (i) Boyce index (B) (−1 to 1) is the Spearman rank correlation on the Fi = Pi/Ei, which measure of the monotonicity of the curve, where positive values indicate that the predictions model are consistent with the presences distribution in the evaluation dataset and negative values indicate an incorrect model. (ii) The continuous Boyce index (Bcont) is based on a moving window. (iii) The proportion of validation points (P) are those observations left out during the cross-validation process (Boyce et al., 2002; Hirzel et al., 2006). (vi) The absolute validation index (AVI) (0 to 1) is the proportion of presence evaluation points falling above a fixed PHS threshold (e.g. 0.5), values near to one indicate trust modelling. And (v) the contrast validation index (CVI) (0.0 to 0.5) is AVI minus the AVI-AVI N 50, values near to cero means that model accuracy does not outperform a random model (Hirzel and Arlettaz, 2003; Hirzel et al., 2004). For PHS modelling, we explored 41 potential explanatory variables (Appendix B), which were rasterized at 90 × 90 m resolution using the nearest resampling technique on ArcMap 10.0 software (ESRI, 2011). For further details of climatic, topographic and landscape variables (Del Valle et al., 1998; Hijmans et al., 2005; Farr et al., 2007; ORNL DAAC, 2008; Zomer et al., 2008; Zhao and Running, 2010; McGarigal et al., 2012; Peri et al., 2018; Rosas et al., 2017, 2018). We visualized the PHS maps for each species into a GIS project, and we combined them with a mask based on NDVI (normalized difference vegetation index) b0.05 to detect bare soil, ice-fields and water bodies (Lillesand and Kiefer, 2000). Then, the fifteen PHS maps were combined (average values for each pixel) into one MPB for Nothofagus forests based on understory plant species. This map had scores that varied from 0% to 69% (average values of PHS for all the studied species), and it was re-scaled by a lineal method from 0% to 100%. For further comparisons, we classified the MPB values as: (i) low (b60%), (ii) medium (61–78%), and (iii) high (79–100%) potential biodiversity, where each class contain an equal quantity of the total pixels of the study area. We analysed the MPB considering the influence of landscape matrix (grasslands and the different forest types) (CIEFAP-MAyDS, 2016) and the potential biodiversity to determine potential hot-spots areas using a hexagonal binning processes (each hexagon = 5000 ha). Firstly, the average of MPB for each hexagon of each class was compared through one-way ANOVAs and Tukey post-hoc test. For this we considered: (i) three group variables including grasslands (G) (grasslands N70%), a mix of grasslands and forests (G F) (forest cover between 30% and 50%), and forests (F) (forest cover N50%); (ii) four treatments including grasslands and forested areas N80% discarding those hexagon with less forest cover, where treatments include pure and mixed forests: G NA, G NP, G NP-MIX, and G NA-NP; and (iii) one analysis where we excluded the grasslands: NA, NO, NP-MIX, and NA-NP. Secondly, the average of MPB for each hexagonal area was considered to identify potential hot-spots areas considering different levels of MPB values (N90%, N80%, N70%, N60%, and N 50%) with good representation for each forest type (N500 pixel per hexagon). For this analysis, NA forests presented 51 hexagons, NP presented 186 hexagons, and MIX 13 hexagons. 3. Results 3.1. Potential habitat suitability The species showed a wide range of COI values, where the greatest indexes were obtained for two dicots (Osmorhiza chilensis with COI = 0.69 and Empetrum rubrum with COI = 0.65), and the lowest for one monocot (Carex andina with COI = 0.06) and one dicot (Acaena

magellanica with COI = 0.04) (Appendix A). DCA analysis showed differential plant species assemblages related to each forest types (Fig. 2). Total variance for this DCA reached 0.616, where Axis 1 presented an eigenvalue of 0.45 (73.2% of variance explained), and Axis 2 an eigenvalue of 0.16 (26.8% of variance explained, reaching to 100%). DCA and the indicator value analyses determined the indicator plant species for each forest type. Viola magellanica was an indicator species (IV = 50.8; p = 0.008) for NP forests, where Empetrum rubrum, Escallonia rubra, Acaena magellanica were also associated to these forests. Carex andina, Festuca gracillima, Avenella flexuosa and Baccharis magellanica were mainly present in NA forests, and Festuca pallescens and Festuca magellanica were shared between NA and NP. Mixed forests presented two indicator species, Blechnum penna-marina (IV = 88.6; p b 0.001) and Gaultheria mucronata (IV = 54.4; p = 0.003), and Osmorhiza chilensis were shared between MIX and NP. Finally, Chiliotrichum diffusum and Berberis microphylla were shared among the three forest types. For the PHS modelling, we selected eight environmental with the lowest Pearson's correlation index (Appendix C): annual mean temperature (AMT), minimum temperature of coldest month (MINCM), annual precipitation (AP), global potential evapo-transpiration (EVTP), elevation (ELE), distance to lakes (DLK), distance to rivers (DR) and normalized difference vegetation index (NDVI). The correlation index varied between 0.03 and 0.96, where the lowest correlation was between MINCM and DR (0.03), and the maximum between AMT and EVTP (0.96), but also considering the biological significance of the values for the species modelling. The outputs of the fifteen PHS models explained 100% of the information in the first four axes (average eigenvalues of the four axes were 193.95 N 110.17 N 6.13 N 1.64) (Appendix D). Crossvalidation indicates that the models presented the following fitting: (i) B varied between 0.36 and 0.94, (ii) Bcont varied between −0.07 and 0.74, (iii) P varied between 0.10 and 0.64, (iv) AVI varied between 0.42 and 0.52, and (v) CVI varied between 0.47 and 0.50. The best cross-validation statistics were obtained for Blechnum penna-marina,

Fig. 2. DCA analyses of understory plants species of Nothofagus forests in Santa Cruz province, considering three forest types (NP = N. pumilio, NA = N. antarctica, MIX = mixed forests) and studied understory plant species (ACMA = Acaena magellanica, AVFL = Avenella flexuosa, BAMA = Baccharis magellanica, BEMI = Berberis microphylla, BLPE = Blechnum penna-marina, CAAN = Carex andina, CHDI = Chiliotrichum diffusum, EMRU = Empetrum rubrum, ESRU = Escallonia rubra, FEGR = Festuca gracillima, FEMA = F. magellanica, FEPA = F. pallescens, GAMU = Gaultheria mucronata, OSCH = Osmorihiza chilensis and VIMA = Viola magellanica). The colour of the plant species corresponding to the Fig. 3 groups.

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where: (i) B = 0.94 indicated that this model had the best fit to the distribution data and is different from a random model, which is coincident with values of AVI = 0.52 and CVI = 0.52 that indicate the species is specialist. Nevertheless, Osmorhiza chilensis presented the worst crossvalidation, where: (i) B = 0.36 indicated that this model is similar to a random model, nevertheless the values of AVI = 0.42 and CVI = 0.41 that indicate that the species can be more generalist than specialist (Appendix E). The PHS maps (Appendix F) presented differential distribution and habitat requirements, where ten species (Acaena magellanica, Avenella flexuosa, Baccharis magellanica, Blechnum penna-marina, Chiliotrichum diffusum, Empetrum rubrum, Escallonia rubra, Gaultheria mucronata, Osmorhiza chilensis, Viola magellanica) presented highest values in a narrow area located in the mountain environments. The others species (Berberis microphylla, Carex andina, Festuca gracillima, F. magellanica, F. pallescens) presented a wider distribution from the forests to the steppe. The ENFA indexes (marginality and specialization) allowed us to define three groups (Fig. 3): (i) the first group (B. magellanica, B. microphylla, C. andina, F. gracillima, F. magellanica and F. pallescens) presented low specialization (2.61 to 6.04) and marginality (1.61 a 2.16) values; (ii) the second group (A. flexuosa, A. magellanica, Ch. diffusum, E. rubrum and O. chilensis) presented highest values of specialization (8.76 to 11.46) and middle values of marginality (2.33 to 2.53); and (iii) the third group (B. penna-marina, G. mucronata, V. magellanica, E. rubra) had middle values of specialization (5.54 to 10.97) and highest values of marginality (2.72 to 2.89). 3.2. Potential biodiversity map The combination of PHS maps allowed to obtain the unique MPB (Fig. 4). Lago Buenos Aires, Lago San Martin and Lago Argentino had medium and lower values, and Lago Pueyrredón and Río Turbio presented the highest values. In general, MPB increased with latitude (north to south), presenting the highest values in N. antarctica forests, and decreased with longitude (east to west) with medium values in N. pumilio forests and the lowest values near glaciers and mixed evergreen forests. ANOVAs showed that MPB significantly changed across the vegetation types (Table 1). Considering grasslands and forests (F = 94.93; p b 0.001), F and G F presented the highest MPB values (59.3% and 55.8%), while G presented the lowest values (30.3%). When grasslands and forest types were considered (F = 22.43; p b 0.001), the highest MPB values were found in G NA-NP forests (71.4%) and G NA forests (70.1%), while the lowest values were presented in G NP forests (46.0%) and G NP-MIX forests (46.8%). Finally, when forest types were considered (F = 18.67; p b 0.001), the highest MPB values were

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found in NA forests (75.5%) and lowest values in NP-MIX forests (46.0%). Hot-spot analyses showed different potential areas of conservation interest through the landscape (Appendix G). There are only two hotspots with MPB N90% (black), and 11 hot-spots with MPB N80% (dark grey) located near Río Turbio and Lago Argentino. New hot-spots can be identified with MPB N70% (grey), 9 located near Río Turbio, one near Lago San Martin and three near Lago Pueyrredón. Also, 12 new hot-spots appear with MPB N60% (light grey) near Río Turbio, 9 near Lago Argentino, 6 near Lago San Martin and 12 near Lago Pueyrredón. Finally, 8 new hot-spots were identified with MPB N50% (diagonal line) near Rio Turbio, 7 near Lago Argentino, 12 near Lago San Martin, and 3 near Lago Pueyrredón. No one hot-spot was identified in the northern area of Santa Cruz province. Hot-spot areas changed with forest types (Fig. 5). Hotspot areas with MPB N90% was only identified in NA forests (black hexagons, see Appendix GE) near to Rio Turbio. In addition, when MPB N80% was considering, NA forests presented more hot-spot areas (27%) near to Lago Argentino and Río Turbio compared to NP (1%) in their southernmost distribution (dark grey hexagons, see Appendix GD and E). When MPB N70% was considered NA forests also presented more hot-spot areas (53%) located near Rio Turbio compared to NP forests (11%) located near Lago Pueyrredón and Lago San Martin (grey hexagons, see Appendix GB, C and E). When MPB N60% was considered, NA forests also present more hot-spot areas (69%) compared to NP forests (22%), both located near Río Turbio, compared to mixed evergreen forests (8%) located near to glaciers (light grey hexagons, see Appendix GB and E). Finally, when MPB N50% was considered, NA forests also presented more hot-spot areas (88%) compared to NP forests (34%) and mixed evergreen forests (15%) across the Nothofagus forests (diagonal line, see Appendix G). 4. Discussion 4.1. Modelling issues Biodiversity changes in space and time increased during the last decades (Rocchini et al., 2015). Understory biodiversity had an important role by providing ecosystem function and services (Gilliam, 2007; Antos, 2009; Wang et al., 2018). Their distribution mainly depended on the environmental and overstory conditions (Lencinas et al., 2008; Wang et al., 2018), and interactions among the different climatic, topographic and landscape factors that affects plant growth and understory plant assemblage (Gilliam, 2007; Barbier et al., 2008; Antos, 2009; Wang et al., 2018). Biomapper software, based on ENFA, allowed to created several PHS to predict the occurrence of the different species

Fig. 3. Specialization (low species' variance compared to global variance of all sites) vs. marginality (large difference of species' mean compared to the mean of all sites) of understory plant species. ACMA = Acaena magellanica, AVFL = Avenella flexuosa, BAMA = Baccharis magellanica, BEMI = Berberis microphylla, BLPE = Blechnum penna-marina, CAAN = Carex andina, CHDI = Chiliotrichum diffusum, EMRU = Empetrum rubrum, ESRU = Escallonia rubra, FEGR = Festuca gracillima, FEMA = F. magellanica, FEPA = F. pallescens, GAMU = Gaultheria mucronata, OSCH = Osmorihiza chilensis, VIMA = Viola magellanica. Plants were grouped according their similarity.

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Fig. 4. Map of potential biodiversity of understory plants of Nothofagus forests in Santa Cruz province. Low potential = red (1–60%), medium potential = orange (61–78%), high potential = green (79–100%). (A) Lago Buenos Aires, (B) Lago Pueyrredón, (C) Lago San Martín, (D) Lago Argentino, (E) Río Turbio. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 1 Simple ANOVA analyses of MPB of understory plant species considering: (i) grasslands and forests, (ii) grasslands and forests types, and (iii) forest types, where: G = grasslands, F = forests, NA = Nothofagus antarctica, NP = N. pumilio y MIX = mixed forests. Category

(i) Grasslands and forests

(ii) Grasslands and forest types

(iii) Forest types

Treatments

MPB

G G F F F(p) G NP G NP-MIX G NA G NA-NP F(p) NP-MIX NP NA-NP NA F(p)

30.25 a 55.81 b 59.27 b 94.93 (b0.001) 45.99 a 46.82 a 70.10 b 71.42 b 22.43 (b0.001) 46.04 a 62.07 b 70.06 bc 75.54 c 18.67 (b0.001)

F = Fisher test, (p) probability. Different letters showed differences with Tukey test at p b0.05.

(Guisan and Zimmermann, 2000) defining their ecological niche (Hirzel and Le Lay, 2008) and ecological requirements (Hirzel et al., 2001; Phillips et al., 2006; Rodríguez et al., 2007). There are several studies that used PHS to describe niche issues of single common, threatened or invasive species (Rupprecht et al., 2011; Falk and Mellert, 2011; Silva et al., 2017), and few studies have yet addressed issues considering species assemblages (Zaniewski et al., 2002; Martínez Pastur et al., 2016). In this study, we used a traditional approach (cover and frequency of occurrence) (Martínez Pastur et al., 2016) to select the most important species in Nothofagus forests that combine the PHS to obtain the MPB. ENFA follows the concept of ecological niche, which links the fitness of individuals (presence data) to the environment (Hirzel and Le Lay, 2008). The efforts and costs to sample large-scale field (Rocchini et al., 2015) mainly in remote areas (e.g. Patagonia) is one of the main challenge for this kind of landscape studies. Here, species data were very scarce, and in consequence, the use of only presence data was the unique alternative (Zaniewski et al., 2002; Anderson and Gonzalez Jr, 2011). Biomapper software allowed us to extrapolate the limited available information (Hirzel et al., 2002) to estimate the PHS

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Fig. 5. Percentage of forest cover according to different MPB values (potential hotspot) considering different forest types. NA = N. antarctica, NP = N. pumilio and MIX = mixed forests.

in large areas. However, frequently biodiversity sampling occurs only in accessible areas (e.g. near major roads or towns) leading to a geographic clusters of localities. For this, it is necessary to considerer the potential spatial auto-correlation among the species data, using spatial filters to avoid biases (Boria et al., 2014). Nevertheless, the implementation of these filters for data removal is more difficult in extend areas with low available data, as in southern Patagonia. Beside this, one option is to check this auto-correlation using human related variables (e.g. distance to localities and routes) (Phillips et al., 2009). In the present study, human related variables were not significant in the fitted models (e.g. eigenvalues). Biomapper employed two indexes (AVI and CVI) for only-presence data, which use a cross validation process to compare modelling outputs with a random occurrence of the observation data (Hirzel et al., 2006). This was one of the main reasons to model PHS using ENFA (Hirzel et al., 2002), and particularly in Patagonia (Martínez Pastur et al., 2016; Rosas et al., 2017, 2018). To achieve a balance between simple and complex models (Anderson and Gonzalez Jr, 2011), we included different types of variables (n = 7) (climatic, topographic and landscape variables). Among these variables, two climatic variables presented a high correlation value (annual mean temperature and global potential evapo-transpiration) (see Appendix C), due to the variations in mountain environments which define the differences among the different forest types. 4.2. Potential habitat suitability Nothofagus forests occurred across a latitudinal gradient (north to south) where mean annual temperature change from 0.9 °C to 9.2 °C, and across a longitudinal gradient (east to west) where annual precipitation increase from 215 to 1479 mm.yr−1. These environmental gradients influence Nothofagus forests through the landscape (Fig.1C), and therefore on the associated understory vegetation. Some studies described the interaction among topographic and climatic variables that influence growth, composition and distribution of understory plants (e.g. elevation can change the temperature and precipitation, or distance to rivers can affect soil water or nutrient availability) (Barrera et al., 2000; Lencinas et al., 2008; Wang et al., 2018). In addition, the structure and composition of the tree canopy determinate the abiotic understory conditions that influence plants distribution (Barbier et al., 2008; Antos, 2009; Peri and Ormaechea, 2013; Peri et al., 2019). For these reasons, the most representative plant indicator species belong to different families and life-forms (Appendix A). In temperate forests, different types of herbs and shrubs represent the plant understory composition, and these specific assemblages are particular for forested areas (Antos, 2009). In our study, the forest types showed assemblages with different indicator species (Fig. 2). DCA analyses highlighted these assemblages, being coincident with other studies: (i) Lencinas et al. (2008) found high frequency of Acaena magellanica and Viola

magellanica in NP forests in Tierra del Fuego and Peri et al. (2019) reported for Santa Cruz that Osmorhiza chilensis was the specie most frequent; (ii) Peri and Ormaechea (2013) determine that grasses (e.g. Festuca sp.) were the main component in NA forests, but the composition changes according to the overstory canopy cover; (iii) Gargaglione et al. (2014) indicated that two species (Baccharis magellanica and Avenella flexuosa) in NA forests were also related to associated open environments (e.g. grasslands); (iv) humid areas determined the MIX distribution with the occurrence of vascular species and ferns with high moisture requirements (Gaultheria mucronata and Blechnum penna-marina) and low understory cover due to the scarce light availability (Martínez Pastur et al., 2012); and (v) the generalist species were also cited for different forest types and transitional ecosystems (e.g. edges between forests and grasslands) (Lencinas et al., 2008; Peri and Ormaechea, 2013; Martínez Pastur et al., 2016). These species were observed in the PHS maps that showed species with a narrow habitat and others that presented a large habitat related with the steppe (Appendix F). The marginality and specialization analyses determined three plant groups (Fig. 3), which was coincident with the DCA analysis and the outputs of the PHS maps. The first group presented species related to NA forests near to grassland (e.g. Carex andina), and some species shared with NP forests (e.g. Festuca magellanica), as it was cited by Martínez Pastur et al. (2016) in Tierra del Fuego. The low marginality and specialization values showed that these species live in general climate condition and in large environmental niches. The second group presented species related to NP forests (e.g. Acaena magellanica) and species shared with other forest types (e.g. Osmorhiza chilensis), as was also cited by Martínez Pastur et al. (2016) and Peri et al. (2019). Finally, the third group represented the indicator species of MIX (e.g. Blechnum penna-marina) and NP forests (e.g. Viola magellanica), with high marginality and specialization values. 4.3. Potential biodiversity map The MPB have been used to understand which is the relationship among the different species assemblages and their environmental conditions around the world (e.g. Zaniewski et al., 2002; Hirzel and Le Lay, 2008; Martínez Pastur et al., 2016; Rosas et al., 2018). Several authors described changes in forest types along latitudinal gradients (Veblen et al., 1996; Ohlemüller and Wilson, 2000), influenced mainly by the climate changes and topography (e.g. Andes mountain) (Smith and Evans, 2007). The obtained MPB captured these changes associated with biotic (forest type) and abiotic (climatic) variables. The present study detected changes in the potential biodiversity across the landscape for the same forest types (e.g. Nothofagus pumilio forests changed the potential biodiversity from north to south, and west to east). These changes for the same vegetation type using similar methodology were

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also reported in previous studies (Martínez Pastur et al., 2016; Rosas et al., 2018). The higher potential biodiversity was found in landscapes with N50% forest cover in NA and the lowest values occurred in NPMIX forests. These outputs are coincident with those findings reported in other studies (Lencinas et al., 2008; Martínez Pastur et al., 2012, 2016). In ecotone areas between grasslands and forests (e.g. grasslands and NA) and between different forest types (e.g. NA and NP), the potential biodiversity greatly increased due to multiple micro-environments that allowed the survival of a higher number of species (Lencinas et al., 2008; Antos, 2009), as well as the existence of potential synergies among the species occurrence (Gargaglione et al., 2014). Beside this, multiple vegetation stratification promoted more species diversity, e.g. NA open forests support more shrubs and grasses species (Peri and Ormaechea, 2013; Peri et al., 2016a) than other close Nothofagus forests with one tree stratum supporting scarce understory biodiversity (Martínez Pastur et al., 2000; Lencinas et al., 2008, 2011).

conservation values in the managed forests (Lindenmayer and Franklin, 2002; Lindenmayer et al., 2012). 5. Conclusions PHS models using Biomapper software allowed us to develop maps using limited data (species and environment). Multivariate analyses were coincident with ENFA indexes (marginality and specialization), supporting ecological studies of the species assemblage, their variations across the landscape, and to detect potential indicator species. MPB synthetized the information for indicator species, generating a powerful tool for decision-making at landscape level, and contributing to: (i) identify hot-spot forests, (ii) evaluate the representativeness of high quality forests inside the protected areas and define new potential areas for biodiversity conservation, (iii) identify areas for monitoring due to potential trade-offs between economical activates (cattle grazing and timber production) and biodiversity conservation, and (iv) develop new sustainable management proposals at landscape level.

4.4. MPB as a tool for management planning and conservation strategies Acknowledgements Biodiversity loss occurring at global scale, especially in forest ecosystems (Gilliam, 2007; Luque et al., 2011; Lindenmayer et al., 2012) has been related to degradation processes (Gunn et al., 2019) generating different trade-offs (e.g. cattle, harvesting, invasive species) with other ecosystem services provision. In the study area, timber was related to NP forests and cattle grazing to NA forests (Martínez Pastur et al., 2000; Peri et al., 2016a), with conservation trade-offs (e.g. Hippocamelus bisulcus occurrence and livestock) (Rosas et al., 2017). Conservation strategy in the study area priories the conservation of intact large uninhabited areas instead their biodiversity values (e.g. frontline policy strategies decided to locate reserves near the borders). However, this strategy has two weakness: (i) land-sparing has been considered ineffective for biodiversity conservation (Todd et al., 2016; Coetzee, 2017); and (ii) not all the forest types were equally included in natural reserves (84% of protected forests belongs to NP, 9% to MIX and only 7% to NA). Thus, most of the high biodiversity values were left outside the reserves (e.g. high quality stands are located in private lands with high biodiversity values) (Gallo et al., 2013). PHS maps and MPB allowed to detect hot-spots and areas without relevant biodiversity values. In Santa Cruz it was determined that: (i) near 50% of the high PHS for Hippocamelus bisulcus are inside natural reserves, but the other 50% still remains in private lands (Rosas et al., 2017); and (ii) MPB for lizard species showed that only 3.2% of the higher quality areas occurred inside the natural reserves network (Rosas et al., 2018). The MPB for understory plants informed by Martínez Pastur et al. (2016) determine that: (i) MIX forests with high potential biodiversity were correctly represented in the natural reserves, (ii) NP forests with higher values were mainly located in private ranches without protection, and (iii) NA forests were under-represented in the natural protected network. Our study also indicated at landscape level that the southern area of the province presents more hot-spots than the north (MPB N50% to N90%) (see Appendix G). In addition, considering each forest type: (i) NA forests presented more hotspot areas considering different values of MPB, and where the highest hot-spot (MPB N90%) was located near Río Turbio, (ii) NP forests presented important hotspot areas (MPB N 80%) in the southernmost distribution of the species near the NA sites, and (iii) MIX forests presented few hot-spot areas (MPB N50%) located near glaciers and Lago San Martin. Increase the knowledge about biodiversity values of the natural forests is crucial to develop sustainable landscape management (Gilliam, 2007; Martínez Pastur et al., 2016; Silva et al., 2017) and to predict the consequences of biodiversity loss due to anthropogenic activities (Wang et al., 2018). The MPB for Santa Cruz forests can assist to the selection of new areas for new natural reserves, or to detect forests with hot-spots with potential biodiversity values outside the natural reserves, e.g. promoting land-sparing strategies to improve the

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