Ecosystem process interactions between central Chilean habitats

Ecosystem process interactions between central Chilean habitats

Global Ecology and Conservation 3 (2015) 776–788 Contents lists available at ScienceDirect Global Ecology and Conservation journal homepage: www.els...

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Global Ecology and Conservation 3 (2015) 776–788

Contents lists available at ScienceDirect

Global Ecology and Conservation journal homepage: www.elsevier.com/locate/gecco

Original research article

Ecosystem process interactions between central Chilean habitats Meredith Root-Bernstein a,b,∗ , Fabián M. Jaksic a,c a

Department of Ecology, Pontificia Universidad Católica de Chile, Santiago, Chile

b

Bioscience Department, Aarhus University, Aarhus, Denmark

c

Center of Applied Ecology & Sustainability (CAPES), Pontificia Universidad Católica de Chile, Santiago, Chile

article

info

Article history: Received 18 December 2014 Received in revised form 13 April 2015 Accepted 14 April 2015 Available online 20 April 2015 Keywords: Acacia caven Ecosystem process Espinal Functional trait Matorral Silvopastoral system

abstract Understanding ecosystem processes is vital for developing dynamic adaptive management of human-dominated landscapes. We focus on conservation and management of the central Chilean silvopastoral savanna habitat called ‘‘espinal’’, which often occurs near matorral, a shrub habitat. Although matorral, espinal and native sclerophyllous forest are linked successionally, they are not jointly managed and conserved. Management goals in ‘‘espinal’’ include increasing woody cover, particularly of the dominant tree Acacia caven, improving herbaceous forage quality, and increasing soil fertility. We asked whether adjacent matorral areas contribute to espinal ecosystem processes related to the three main espinal management goals. We examined input and outcome ecosystem processes related to these goals in matorral and espinal with and without shrub understory. We found that matorral had the largest sets of inputs to ecosystem processes, and espinal with shrub understory had the largest sets of outcomes. Moreover, we found that these outcomes were broadly in the directions preferred by management goals. This supports our prediction that matorral acts as an ecosystem process bank for espinal. We recommend that management plans for landscape resilience consider espinal and matorral as a single landscape cover class that should be maintained as a dynamic mosaic. Joint management of espinal and matorral could create new management and policy opportunities. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Understanding ecosystem processes is essential to developing conservation and management interventions. Ecosystem processes are broadly controlled by the distribution of functional traits, habitat modification by species, and abiotic inputs controlling primary productivity (Crain and Bertness, 2006; Fischer et al., 2006; Mouillot et al., 2011; Maestre et al., 2012). Anthropogenic transformations due to agriculture, forestry and climate change, as well as natural successional processes, affect ecosystem processes by creating heterogeneity (Loreau et al., 2001; Fischer et al., 2006). Within habitat mosaics, each habitat type may harbor components of biodiversity, abiotic inputs and physical substrates needed for different ecosystem processes (Loreau et al., 2003; Fischer et al., 2006). Ecosystem processes, and their inputs, also travel beyond their immediate spatial distributions via biotic and abiotic fluxes and interactions (e.g. Rand et al., 2006; Alongi, 2008). Thus landscape-scale heterogeneity may contribute to providing more inputs for more ecosystem processes, yet many studies show that it can also reduce overall functioning due to island and matrix effects (Loreau et al., 2001, 2003; Fischer et al., 2006).



Corresponding author at: Department of Ecology, Pontificia Universidad Católica de Chile, Santiago, Chile. E-mail address: [email protected] (M. Root-Bernstein).

http://dx.doi.org/10.1016/j.gecco.2015.04.007 2351-9894/© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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One possible explanation for these contrasting outcomes is that successionally linked mosaic patches may increase overall functioning within the landscape, while mosaics not linked by a successional pathway may more frequently show decreased functioning as a result of fragmentation (Odum, 1969). By successional, we refer broadly to all endogenous habitat changes, including shifting mosaics, while by non-successional we mean land cover change created and maintained through high levels of disturbance and human niche construction, such as cropland or urban areas. Successional pathways between habitat types imply spatiotemporal dispersal of nutrients, propagules, and/or ecosystem functions at a landscape scale. Thus, when habitat types are linked via successional processes, the functional ‘‘insurance’’ effect of diverse taxon dispersal across landscapes (Loreau et al., 2003) may be facilitated, compared to habitat types not linked by succession. This is because cropland, for example, acts as a successional sink. Ecosystem processes leading to succession, e.g. seed dispersal, may enter cropland but the cropland is either physically inhospitable or maintained through anthropogenic disturbance in a state far from natural succession, until abandonment. While the flux of ecosystem functions across anthropogenically maintained mosaics with cropland, plantations, cities, etc. is of particular interest to conservationists taking an ecosystem services perspective, many semi-natural production systems, such as rangelands, silvopastoralism and agroforestry, can be managed for natural successional mosaics (Fischer et al., 2006). Ecosystem functions, and the ecosystem services they provide, can be difficult to define and measure, due to their complexity. Underlying ecosystem functions are multiple functional traits belonging to many species with different in spatial and temporal distributions, which together contribute at different rates and in different quantities to ecosystem function dynamics (Benggtson, 1998; de Groot et al., 2010). Functional traits contribute to ecosystem processes that, in turn, have difficult-to-assess spatiotemporal dynamics. Functional traits and other inputs each have a different strength of contribution to the ecosystem process, a different range and frequency of mobility, and a different lag or residence time at the destination. For example, if seed germination depends on seed production, exozoochory and nurse plant availability, then the spatiotemporal distribution of the seed germination process depends on the spatiotemporal dynamics of shrub seed production, animal movement, and nurse plant distribution. It is unlikely that ecosystem process distributions show a linear decay away from the area of origin (e.g. the seed producing shrubs). Rather, they are likely to distribute in complex and patchy ways. Although there is a great deal of literature on regional and continental-scale spatial mapping of ecosystem services, practical difficulties limit attempts to trace the spatiotemporal dynamics of ecosystem processes at a landscape or patch scale (for related approaches see Jordano et al. 2007; Root-Bernstein et al., 2013a). In addition, ecosystem processes are often cyclical, such as reproduction, the water cycle, or trophic energy transfer (Fath and Halnes, 2007; Scanlon et al., 2005). Because a given functional trait can both cause and be affected by a cyclical ecosystem process, functional traits are not only inputs to processes, but also outcomes. The input–outcome relationship is similar to the response-and-effect framework (Suding et al. 2008; Laliberté et al., 2010), but here we focus on the endogenous ecosystem processes underlying succession (Odum, 1969), rather than the functional responses uniquely associated with exogenous factors such as climate change. The functional traits that occur in a habitat as a result of successional processes (outcomes) are rarely studied in relation to functional trait inputs (but see Eldridge et al., 2011). An additional complication, from a community ecological point of view, may be that counting functional traits alone leaves out many important characteristics of ecosystems that interact closely with other traits, such as properties of the soil. Methods that mix functional traits and other functional non-trait elements fit better with diverse perspectives (e.g. ecosystem engineering, Crain and Bertness, 2006), and can provide good ecological models (e.g. Maestre et al., 2012; Dantas et al., 2013). We address the concept of ecosystem process interactions with a non-spatially explicit, non-temporally explicit approach, through a case study of the central Chilean mediterranean-climate habitats espinal and matorral (see Fig. 1; RootBernstein and Jaksic, 2013; Maestre et al., 2012). Espinal is a savanna dominated by Acacia caven, traditionally used as a silvopastoral system (Ovalle et al., 1990; Fuentes et al., 1989). Espinal can be found with or without a shrub understory, and often occurs next to matorral, a dense shrub habitat typical of the foothills of the Andes and the coastal mountain range (Donoso, 1982). The successional relationships between espinal habitats and matorral have been largely ignored after a few early studies (Armesto and Pickett, 1985; Fuentes et al., 1986) and are only recently attracting renewed interest as an element of land cover change (e.g. Hernández et al., 2015; Fuentes-Castillo et al., 2012; Newton et al., 2011). Espinal succession may be characterized as in Fig. 2. Both matorral and espinal have been described as degradations of native sclerophyllous forest (assumed to have been the dominant climax habitat in prehispanic Chile), implying a simple successional model in which forest is degraded, crosses a threshold and rarely recovers (see Ovalle et al., 1990; Aronson et al. 1993; Schulz et al., 2010; van de Wouw et al., 2011). A. caven can also be described as a slow-reproducing pioneer species that establishes after anthropogenic disturbance, suggesting that it is not a dead-end degraded state, but rather the initial stage of a successional pathway (Fuentes et al., 1989; Baranelli et al., 1995; Torres et al., 2002; Root-Bernstein and Jaksic, 2013). Espinal can serve as nurse plant habitats, making them important for regrowth of sclerophyllous forests in central Chile (Hernández et al., 2015; Fuentes-Castillo et al., 2012; pers. comm. C. Peña). Within espinal it is often possible to find some sclerophyllous tree species at low density (pers. obs. MR-B). However, anthropogenic disturbances of forest and matorral, and overgrazing of espinal areas preventing tree and shrub recruitment may result in the last part of the cycle, sclerophyllous forest regrowth, rarely occurring. The lack of a well-studied dynamic functional viewpoint on matorral–espinal–sclerophyllous forest relationships has effects on their conservation and management. The three habitats are formally considered as separate and unrelated, with widely differing protections and management regimes. From an institutional governance perspective, both matorral and

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Fig. 1. Top left, espinal in winter with 3 individual sclerophyllous trees (middle ground, identifiable by dark green leaves). Top right, shrub espinal in winter. Bottom left, shrub espinal in summer. Bottom right, matorral in winter (note individual sclerophyllous trees in the ravine, top left of photo). Top left photo© Adrien Lindon, other photos© MR-B.

Fig. 2. Potential successional pathways between sclerophyllous forest, espinal and matorral. Arrows indicate exogenous or endogenous processes, as appropriate. We have marked with question marks explanations that we consider to be uncertain or speculative. In this study we only consider the ecosystem processes underlying succession in espinal, shrub espinal and matorral.

espinal may be considered marginal ‘‘orphan’’ habitats (Buckingham et al., 2011) without clearly developed or implemented management and protection goals (see Root-Bernstein and Jaksic, 2013). Matorral has no conservation protections per se. Espinal areas are managed for silvopastoral production from a traditional agronomical perspective (Olivares, 2006a,b; Ovalle et al., 1990). Acacia caven is sometimes considered to be a protected native species that cannot be cut down, but interpretation in practice appears to be inconsistent (MR-B pers. obs.). By contrast, sclerophyllous forest is protected by the Native Forest law (N° 20.283). Here, we focus specifically on the functional links between matorral and espinal, the least conserved and most heavily exploited of the three habitats. We adopt an approach that allows us to combine functional traits and other functional elements, and to divide them between inputs to ecological processes and outcomes of those processes. We hypothesize that matorral and espinal with and without shrubs show exchanges of ecosystem processes. We look at the net effects of that exchange across habitat types, considering each habitat type as an abstract box. We do not attempt to account for how the net effects vary with habitat spatiotemporal distribution. Since matorral is usually considered to be the more natural and least degraded of the habitats, we predict that matorral will ‘‘subsidize’’ espinal (with and without shrubs) ecosystem processes, such that traits and other

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inputs found in matorral can account for ecosystem process outcomes in adjacent espinal. This assumes that espinal is part of the successional pathway. If the successional pathway in Fig. 2 is correct, then the greatest subsidization effect should be between matorral and espinal with shrub understory. By contrast, if espinal habitats are degraded dead ends, they should act as ecosystem process sinks. Ecosystem process inputs from matorral will have no outcomes in espinal habitats. We look for this ‘‘spillover’’ or subsidization effect with a measure (a convex hull, see Methods) that incorporates abundance and divergence (range) for groups of functional traits and other variables. Each variable exists on an independent axis, so that direct comparisons between groups of variables that are not all traits, or that have different units, is valid as long as the set of axes is the same for both groups. Input variable sets can then be compared to outcome variable sets across adjacent successional matorral–espinal habitat transitions to understand, inductively, the direction of ecosystem process flow. We focus on ecosystem processes that are targets of existing silvopastoral management in espinal, specifically (1) high quality herbaceous biomass production, (2) woody plant reproduction, and (3) soil fertility improvement. We predict that (1) matorral should have larger sets of input ecological variables than espinal, and (2) both espinal habitats (with and without shrubs) should show a disproportionately wide range of outcomes, relative to its inputs. We further examine whether outcomes are consistent with the goals of espinal conservation and management. 2. Materials and methods 2.1. Research site The study took place in the Pajaritos area of the Estación Experimental Rinconada de Maipú (33°23′ S , 70°31′ W, elevation 495 m), a field station of Universidad de Chile, Santiago, Chile. This is located within the semi-arid mediterranean-climate region of central Chile, characterized by a rainy winter season when most plant growth occurs, and a hot dry summer. Our study site encompassed a mosaic of espinal (Acacia caven savanna), with and without a shrub understory, and matorral (shrub habitat) remnants on and near hillsides. There is no sclerophyllous forest at the site, although we observed individuals of the sclerophyllous trees Lithrea caustica and Quillaja saponaria (by chance none occurred in the plots). The dominant tree characterizing espinal is A. caven, a small thorny native South American acacia with an inverse phenology to nonevergreen plants in central Chile, such that its leaves appear in late spring and remain green during the summer. Previous research at this site indicates that some invasive herbs from other mediterranean-climate regions are highly abundant, but that there are more than twice as many Chilean mediterranean-zone endemics as non-endemic species (Root-Bernstein et al., 2014). Common vertebrate species at the site include sheep, European rabbits (Oryctolagus cuniculus), culpeo foxes (Lycalopex culpaeus), rodents such as the degu Octodon degus (semifossorial) and the cururo Spalacopus cyanus (fossorial), several raptors and a few dozen granivorous and insectivorous bird species, and several species of lizard and snake. The Pajaritos section, which is partially fenced, is 525 ha of a total 898 ha used as pasture for Suffulk Down and Merino sheep. The sheep are kept at a stocking rate of 0.92 sheep equivalents per ha over the entire 898 ha, yielding 18.3 kg of meat/ha and 3.0 kg of wool/ha (C. Araneda pers. comm.). Although not part of the official management plan, cattle or their feces are sometimes observed in the area. 2.2. Plots Ecological variable data was collected at the plot scale. Each plot was a 2 m×2 m square. Plots were arranged into transects to facilitate fieldwork, and were placed at 10 m intervals. Transects were placed about 100 m apart using stratified random sampling, within a matorral–espinal transition area. 40 plots were placed within two matorral remnants in the foothills on the north side of Pajaritos. 50 plots were placed within the continuous espinal habitat south of the hills, between 200 and 300 m from the nearest matorral fragments. 70 plots were placed within espinal with a shrub understory (hereafter ‘shrub espinal’). 40 of these shrub espinal plots were in an area adjacent to a matorral remnant (within 25 m of the matorral–espinal transition), while the other 30 were 300–500 m from matorral, with a ∼100 m grassland gap between matorral and espinal edges. Since we are considering a range of ecosystem functions and variables (see Variables section below), in order to minimize pseudoreplication and maximize accuracy across variables, we have chosen small plots relatively far apart. Soil fertility varies at a fine scale due to factors such as slope, plant structure, and local nutrient inputs such as feces or decomposing matter, so 4 m2 plots spaced 1000 m2 blocks apart (10 m × 100 m) are likely to be independently affected for soil fertility inputs and outcomes. For forage provision inputs, at our site factors such as the combination of small mammal disturbances and woody vegetation structure also vary at a scale smaller than 1000 m2 blocks. For woody shrub establishment functions, birds and rabbits are both habitat selective during foraging at less than 1000 m2 scales (Root-Bernstein et al., 2013a,b; Jaksic et al., 1979). 2.3. Variables We collected two sets of ecological variables, inputs and outcomes. Inputs were chosen as those variables that we expect to move between habitats due to successional processes. Outcome variables were chosen as variables characterizing the

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Table 1 Variables used and how they were measured. Filled-in boxes indicate input or outcome variables. Ecological function Forage

Variable Input

provision

Data from literature

Data from field

Scale of data collected in field

Octodon degus disturbances

x

plot

Rabbit disturbances

x

plot

Bird disturbances

x

plot

x

plot

x

One site, exemplar samples One site, exemplar samples One site, exemplar samples One site, exemplar samples

Tree canopy cover Outcome

Soil fertility

Input

Secondary metabolites Rosette diameter

x

Hairy, thorny, stinging

x

Herbaceous biomass

x

Leaf area

x

Octodon degus disturbances

x

Rabbit disturbances

x

plot

Bird disturbances

x

plot

Bird feces

x

plot

Shrub canopy cover

x

plot

Hairy, thorny, stinging

x

One site, exemplar

Microarthropod detritivore

x

plot

Tree canopy cover

x

plot

Nitrate Ammonium Phosphorous Percent organic matter

x x x x

Plot; but analyzed by grouping plots into 11 habitatcover categories (Table 2).

x x

plot plot

plot

samples

biomass Outcome

Woody plant

Input

establishment

Granivorous birds Rabbit pits Bird pits Recent fire

Outcome

Tree canopy cover Shrub canopy cover Reproduction type Dispersal type Shade tolerance Root:shoot ratios 1-year sapling or shoot heights Seed diameters of woody species

x x x x x x

quality and quantity of the ecosystem process provided, with relevance to silvopastoral management. Input and outcome variables included functional traits, modified habitat features and abiotic variables (Maestre et al., 2012). We defined input variables as factors identified in the literature as initiating, facilitating, or otherwise making possible the ecosystem processes of interest. We defined outcomes as characterizations of the set of species life-history strategies and ecological conditions that result from the ecosystem processes identified as inputs. Since ecosystem processes can be cyclical or have multiple feedback mechanisms, we took a restoration perspective and defined outcomes as life-history strategies and ecological conditions expected to result at least a year after a set of ecological processes was initiated or restored to a habitat. We considered each variable to be either an input or an output of a given process, but not both. Here we give a succinct justification for each ecological variable used (see Table 1). For forage provision, we argue that ecosystem process movement primarily takes the form of plant species dispersal. Higher densities of a variety of microclimates and microsites suitable for plant establishment in a given habitat provide a larger species pool for dispersal to adjacent habitats. At the same time, diffusion of animals producing microsites through disturbance from a high-density habitat to a low-density habitat can contribute to some ecosystem process movement. Inputs included disturbances to the soil by the social rodent Octodon degus, pits dug by rabbits (O. cuniculus), cururos (S. cyanus) and birds (mainly Sturnella loyca), and tree canopy cover. The soil disturbances by small mammals and birds increase herbaceous species diversity in this region (Root-Bernstein et al., 2014; Root-Bernstein and Ebensperger 2012; Kelt, 2011). Higher herbaceous species diversity is likely to mean a broader range of functional traits (Loreau et al., 2001). Tree canopy

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Table 2 Number of plot-samples homogenized to form a combined soil sample for each woody cover class. The total volume of each combined soil sample is equal to approximately 100 g × the number of plot samples. From each combined sample, two 100 g subsamples were taken from the homogenized combined soil sample for lab analysis. Woody cover class

Matorral

Shrub espinal

Espinal

First quartile (0%) Second quartile (1%–10%) Third quartile (11%–45%) Fourth quartile (46%–100%)

16 12 6 6

40 17 7 6

46 3 1 0

cover increases herbaceous biomass in this semi-arid region (Olivares, 2006a). We did not consider abiotic variables such as rainfall or slope that, though relevant to overall biomass production, cannot be considered independent between plots. The tree canopy effect appears to have a larger local effect on available soil moisture than these other variables in any case (Olivares, 2006a; pers. obs. MR-B). Large-scale non-independent factors add to the background variance to which species adapt in each habitat, and will be reflected in other variable values. Forage provision outcomes sought to characterize the overall condition of the herbaceous community and its suitability as forage. Herbaceous communities that best support livestock in the long term will have a mix of palatable and unpalatable species, along with other adaptations to herbivory, and will have high biomass and large leaves. We obtained partial data from the literature on secondary metabolite presence in the herbaceous plant community (Bustamante et al., 2006; Niemeyer, 1995), and directly measured rosette diameter and presence of hairy, thorny or stinging traits. These variables were intended to indicate anti-herbivory adaptations that would generally reduce the value of the plant community as forage (although eating some plants with secondary metabolites can increase livestock weight gain Provenza and Papachristou, 2009). Herbaceous biomass and leaf area were intended as positive indicators of forage quality. The period during which forage biomass was available did not vary significantly within the annual species and thus was not considered as a variable. For soil fertility, we argue that an increase in soil turnover, soil moisture, and decompositional processes can leave a legacy of higher soil fertility. This is likely to affect other habitats partly through diffusion of these functions beyond the habitat of high density due to animal movements, and over longer time periods due to shifting mosaics of habitat types exchanging positions. Soil fertility inputs included several classes of variables. Soil disturbances and areas of activity of O. degus, rabbits and birds, and bird feces, are variables which represent the contribution to nutrient inputs from small mammal and bird feces and the trapping of moisture and litter in disturbed microsites (Dean and Milton, 1991). Shrub canopy and spines would also contribute to trapping litter prior to its decomposition (Padilla and Pugnaire, 2006; Gómez-Aparicio et al., 2004). Macroarthropod detritivore biomass was included because arthropod detritivores may affect the decomposition rate (cf. Vos et al., 2011; Coulis et al., 2013). Finally, A. caven cover was used as a proxy for nitrogen fixing by this leguminous tree (Sitters et al., 2013). Soil fertility outcomes were measured with four common variables of soil quality: nitrate concentration, ammonium concentration, phosphorous concentration, and percent organic matter. As these increase, soil fertility is considered to increase. For the movement of woody plant establishment processes we make a similar argument to soil fertility processes. In the short term, a high density of animals producing germination microsites and transporting seeds in one habitat type can diffuse out into adjacent ones. Fire can also move between habitats. In the longer term, the outcomes of these functional traits appear to have moved into another habitat because the other habitat has actually moved into the space where they were previously more abundant, due to succession. Woody plant establishment input variables included a measure of granivorous avian presence, abundance of pits made by rabbits and birds, and recent fire presence. Birds are the most important granivores and endozoochorous seed dispersers in central Chile, with ants collecting up to as many seeds, but during a shorter period of the year (late summer only) (Kelt et al., 2004; Vasquez et al. 1995; Jiménez and Armesto, 1992). Ants were not present in significant numbers during our field season. Pits made by rabbits and birds can act as seed traps that facilitate germination (Dean and Milton, 1991). Although naturally occurring fire is thought to be rare in central Chile, some shrubs and trees show vigorous vegetative resprouting after fire, suggesting that they are to some degree fire-adapted (see also Gómez-González et al., 2008). Anthropogenic fires are now common in central Chile. Consequently, recent fire can favor the establishment or regrowth of particular shrub species more than others. Woody plant establishment forms a species pool for future succession and ongoing ecosystem processes, so we sought to measure variables related to the reproductive strategies and capacities of established woody plants. Outcomes obviously included canopy cover of shrubs and trees. We also sought to understand whether woody plants were favoring vegetative or sexual reproduction, wind or endo- and ecto-zoochory dispersal, and whether they showed rapid or slow growth and were primarily shade or drought tolerant. These variables can help to characterize how shrub understories grow and spread from matorral, and how trees reproduce (Cornelissen et al., 2003). These variables included the abundance of phanerophytes and endozoochory-dispersed woody species (Arroyo and Uslar, 1993) and shade tolerant species (Badano et al., 2005), as well as the root:shoot ratios, 1-year sapling or shoot heights, and seed diameters of woody species (Hoffman and Kummerow 1978; Montenegro et al., 1979; Hoffmann et al., 1989; Aronson, 1992; Ginocchio and Montenegro, 1992; Torres et al., 2002).

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Although seed mass is considered a better functional trait than seed diameter for understanding plant adaptive strategies because it provides information on the investment in nutrients per seed by the parent plant, which may interact with dispersal strategy (Cornelissen et al., 2003), these data were not available in the literature and collection was not feasible. The root:shoot ratio is a rough measure of investment in water uptake vs. photosynthesis, and reflects a growth strategy to optimize across experienced abiotic limitations (Cornelissen et al., 2003). The 1-year sapling or shoot heights were used as a proxy for the ability to recover after a disturbance such as a fire, and/or establish from an independent seedling (Cornelissen et al., 2003). Five main types of data were collected in the field: (1) Abundances of herbaceous species and woody species canopy cover for each plot; (2) Invertebrate detritivore abundances; (3) Soil samples; (4) Measures of small mammal and bird presence or activity; (5) Plant functional traits (see Table 1). In designing data collection, we preferred methods that were efficient as well as accurate and that could potentially be developed into ecological indicators for adaptive management. This facilitates transfer to practice. (1) Herbaceous and canopy cover data were collected at the period of maximum diversity, abundance and biomass, throughout October 2012. Abundances of herbaceous species were estimated using an analogue of the pin-grid method adapted for use at our site (Jonasson, 1983). Herbaceous vegetation sampling consisted of placing a 20 cm × 20 cm plastic frame at 2 locations within each 2 m × 2 m plot. The plants growing within each frame were recorded by taking photographs with a digital camera in autofocus mode, from a distance of approximately 1 m above the ground. Plants were identified to species level in SamplePoint (R), which records the classification into user-defined categories at crosshairs (‘pins’) arranged in a regular grid over a digital photograph (Booth and Cox, 2011). We set our grid to 25 crosshairs per photo, the equivalent of one pin per 16 cm2 in a pin-point sampling protocol. Plants rarely completely overlapped due to an absolute low abundance in all plots and it was possible to identify each species from the photographs by the shape of cotyledons, leaves, and flowers. Overlap would lead to an overestimation of the abundances of large plants and an underestimate of the abundances of small rare plants, but previous studies at this site using this methodology found that overlap occurred at less than 10% of ‘pins’ (Root-Bernstein et al., 2014). Due to this possible bias, this method does not yield a true measure of herbaceous populations. We measured a mean of 5.43 ± 0.01 (standard error) herbaceous species in each grid, with 32 species observed in total. Woody species were identified in the field and canopy cover was estimated as the area of its projection onto the ground. (2) Invertebrate detritivore abundances were measured via pitfall traps. One pitfall trap was set in the center of each plot. Pitfall traps consisted of two sturdy plastic disposable coffee cups, one inside the other, set in a hole so that the rim was level with the soil surface. Digging-in effects were avoided by placing the traps between 12–19 October, but only filling them with liquid on the 25th and 26th of October 2012. The liquid consisted of a 50:50 mixture of glycerine and ethanol. Invertebrates were collected from the traps on the 7–9 November, after having been left open for approximately 10 days. Detritivores were identified to family level and separated from other invertebrates on the basis of a key developed during past research at the same site (Root-Bernstein et al., 2013a,b). Detritivores were then dried for 48 h at 60° C and immediately weighed to determine the dry biomass per plot. (3) Soil samples were taken from the first 10 cm depth, with one sample of around 100 g per plot (the resultant hole was used to install the pitfall traps). The cost of analyzing each sample individually was prohibitive. Consequently we assigned each plot to a habitat type (espinal, shrub espinal, matorral) and four woody cover classes divided on the quartile values of percent woody cover, yielding 12 habitat-cover categories (Table 2). There were no samples in the category espinalhighest cover class, so only 11 categories were used. Feces, fresh plant material, litter and stones were excluded from the samples. Soil samples were stored in plastic bags at 5–8° C for a month during collection and mixing. The soil samples were mixed together according to the habitat-cover category to which the plot belonged (Robertson et al. 1999). Two 100 g subsamples were taken from each soil mixture, except for one habitat-cover category in which the single 100 g sample from one plot was the only sample. This yielded 21 soil samples. These samples were sent to the Soil Laboratory at the Universidad de Concepción, Chile, for analysis. The samples were analyzed for percent organic material, mg/Kg nitrate, mg/Kg ammonium, and mg/Kg Olsen phosphorous. A mean value per category was calculated for each measure and then attributed to each plot in that category. (4) Degu (Octodon degus) herbivory and biopedturbation occur primarily on and adjacent to their runways, and is associated with changes in the plant community (Root-Bernstein et al., 2014). Degu runways connect degu burrows. To estimate degu activity levels we thus counted the number of runways (identified as linear 8-cm wide paths) crossing each plot and the number of burrow entrances in each plot. Number of cururo (S. cyanus) burrow entrances was counted as a measure of cururo disturbance activity (Contreras and Gutiérrez, 1991; Contreras et al., 1993). We also counted the number of pits dug by rabbits and birds in each plot. Pits are funnel-shaped and between 2 and 15 cm deep. We did not distinguish between rabbit and bird pits during counting. As most bird pits are made during winter by Sturnella loyca, we assayed all other recent bird activity in each plot by counting bird feces. To estimate how much of this activity could be attributed to granivorous birds, we conducted two bird surveys on the 13th of October and the 11th of November. Surveys took place along six transects, two in each habitat type. Surveys took place between dawn and 10:45. Birds were identified to species level visually, which was adequate due to the open habitat. Only birds within 100 m of the transect were included; a previous bird survey in this study site showed no significant effect of habitat on detectability (Root-Bernstein et al., 2013a,b). The percent of granivorous birds by abundance was calculated for each transect, and

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Fig. 3. Example of convex hulls using sites as the data points. The three axes are the same between habitats A and B, making the convex hulls comparable. Each sampled site for each habitat is plotted along the axes representing the variables collected. The n-dimensional convex hull (here, 3 dimensional because there are 3 axes) for each habitat is a surface that contains the data points, here all the sites. The volume of each hull can be calculated and directly compared across habitats. This gives a measure of the amount of variable space needed to describe all the sites of each habitat. The centroid, here shown as a white dot, lies at the n-dimensional center of each hull. The linear distance in n dimensions between each centroid can be calculated comparing habitats A and B, e.g. (xi , yi , zi ) – (xj , yj , zj ). This gives an indication of the tendency towards certain variable values, or more generally a difference in variable distribution between each habitat. An imprecise one-dimensional analogy would be to comparing the means and ranges of two box and whisker plots.

the mean was calculated for each habitat type. We multiplied the percent granivorous birds in each habitat type by the number of bird feces in each plot to get an estimate of granivorous bird activity in each plot. (5) Plant functional traits were estimated by selecting well-grown but not extreme individuals (Weiher et al. 1999). We selected specimens from a transitional area between matorral and espinal that was not within a plot. Rosette diameter and spine length were measured on 10–15 individuals of all species displaying these traits. Leaf area was measured by collecting 5 adult leaves of each species, each from a different individual. Leaves were photographed on grid paper and their area was estimated. Mean leaf area was calculated for each species. Biomass was estimated for each species by collecting 5 individual plants, excluding roots, which were dried at 60o C for 72 hr and then weighed to 0.01 g to determine the dry mass. A mean was calculated for each species. These data were then attributed to plots by calculating the abundance weighted means. At the plot scale, all data were presented either as an abundance weighted mean, or as an abundance, as appropriate. For analysis, each plot was treated as an independent data point. 3. Analysis and statistics We used convex hulls to assess the size of the variable ranges for inputs and outcomes. Rather than treating each species as a data point, as in studies where only functional traits are considered (e.g. Lin et al., 2011), we treated each plot as a data point. This forms a multidimensional space, where the dimensions in the space represent the range of values of each variable included in an ‘input’ set or an ‘output’ set. The convex hull is a multidimensional shape that fits the outermost points along all axes. The relative volumes and centroid positions of convex hulls with the same variable axes can be compared to assess the position in variable space and amount of variation present across habitat types (Fig. 3). We used the qhull package in R version 2.15.2 to calculate convex hulls. The centroids and the distance between centroids were calculated using code provided by one of the authors of Lin et al. (2011), D. Flynn (pers. comm. 2013). Under the null hypothesis that no ecosystem processes are exchanged between habitats, we expect outcome volumes to be proportional to input volumes across habitats. We compared null hypothesis expected outcomes to observed outcome volume distributions across habitats using the χ 2 test in Graphpad (http://graphpad.com/quickcalcs/chisquared2/, used 3 April 2014). 4. Results 4.1. Convex hull volumes and centroids The convex hull relative volumes and centroid distances are summarized in Fig. 4. Larger convex hulls represent larger ranges across all variables. Centroids measure the median point across all variables, so centroid distances indicate the similarity, between convex hulls, of the ranges of all variables together. Shorter centroid distances indicate more overlap between all variable values. For forage quality, input variables showed the greatest centroid distance between shrub espinal and espinal. Shrub espinal had the largest range of variable values, with a hull volume that was 6 times larger than espinal and 11 times larger than matorral. This general pattern was reflected in the outcomes, although the volume of outcome pools was significantly biased towards matorral and away from espinal compared to the expected null distribution from inputs (χ 2 = 11763, n = 32164 (sum of output volumes), p < 0.0001). Shrub espinal retained the largest range of variable

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Fig. 4. Convex hull volumes and centroid distances for input and outcome variables of forage quality, soil quality and woody plant establishment. The convex hull volumes are represented by the circles. Volumes are shown as relative, not absolute. The centroid distances are represented by the lines, with the distances shown.

values, with a hull volume 27 times larger than espinal and 3.5 times larger than matorral. Matorral and espinal outcome centroids became more distanced, compared to input centroids. For soil quality, the matorral centroid was closer to espinal than shrub espinal, and shrub espinal and espinal were more than twice as distanced as matorral and espinal. The pool of input variables was largest for matorral, which had a convex hull about 2 times larger than the convex hull of shrub espinal and nearly 500% larger than that of espinal. This pattern was not reflected in outcome variables (χ 2 = 211.5, n = 140 (sum of output volumes), p < 0.0001). The matorral outcome convex hull volume was the smallest, with the espinal convex hull 1.3 times larger, and the shrub espinal convex hull 2.5 times larger. At the same time, the matorral centroid became relatively much closer to shrub espinal and farther away from espinal. For woody plant establishment, the centroids of shrub espinal and espinal were closest together, with the matorral centroid nearly twice as far away from each. Matorral had the largest pool of input variables, with a convex hull volume about 1.4 times larger than the convex hull of shrub espinal, and about 4.7 times larger than the convex hull of espinal. As for soil and forage quality, this pattern was not reflected in outcome variables (χ 2 = 90985.6, n = 214756 (sum of output volumes), p < 0.0001). Rather, as for soil and forage quality, shrub espinal had the largest range of variable values, with a convex hull volume 2 times larger than for matorral, and about 96 times larger than for espinal. In addition to being relatively extremely small in volume, the espinal convex hull showed a large shift in its centroid away from shrub espinal. 4.2. Ecological variable values For forage quality in espinal, quality decreased relative to matorral on some measures but increased on others. Herbaceous plant defenses against herbivory were most abundant in espinal. Plants with secondary metabolites were more abundant in espinal than in shrub espinal, and least abundant in matorral (F = 29.1, df = 2, p < 0.001, ANOVA). Rosette growth showed a similar pattern (F = 17.7, df = 2, p < 0.001, ANOVA). Plants with physical defenses (hairy, thorny or stinging) were of equal and low abundance across habitat types (F = 0.8, df = 2, p = 0.437, ANOVA). On the other hand, the abundance weighted means of herbaceous plant biomass and leaf area were slightly but significantly higher in espinal, and not different between shrub espinal and matorral (biomass: F = 10.1, df = 2, p < 0.001; leaf area: F = 3.7, df = 2, p = 0.026, ANOVA). For soil, shrub espinal had the highest quality on most measures, compared to espinal and matorral. Nitrate concentration was highest in espinal (F = 18.0, df = 2, p < 0.001, ANOVA). However, ammonium concentration was highest in shrub espinal (F = 35.0, df = 2, p < 0.001, ANOVA), as was phosphorous concentration (F = 107, df = 2, p < 0.001, ANOVA). Percent organic material was also highest in shrub espinal, and not different between espinal and matorral (F = 3.3, df = 2, p = 0.038, ANOVA). For woody plant establishment in espinals, shrub espinal appeared to have more variables that together favored rapid shrub or tree dispersal, establishment and growth within that habitat compared to espinal and matorral. The abundance weighted mean of seed size was highest in shrub espinal and lowest in espinal (F = 6.5, df = 2, p = 0.002, ANOVA). Correspondingly, endozoochory dispersed species were more abundant in shrub espinal and least abundant in espinal, with no difference in wind dispersed species between habitats (endozoochory: F = 15.3, p < 0.001, df = 2; wind: F = 1.5, p = 0.227, df = 2, ANOVA). The tree canopy cover did not differ between plots in each habitat, reflecting the

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wide spacing between trees in the espinal (F = 0.7, p = 0.506, df = 2, ANOVA). As expected, shrub canopy was highest in matorral and almost nonexistent in espinal (F = 20.7, p < 0.001, df = 2, ANOVA). Correspondingly, abundance of shade tolerant woody species was highest in shrub espinal, which had both trees and shrubs (F = 13.7, p < 0.001, df = 2, ANOVA). The abundance weighted mean of the one-year height of saplings or shoots, and the abundance of phanerophytes, were also highest in shrub espinal and lowest in espinal, but there was no difference in root:shoot ratios between habitats (sapling or shoot height: F = 30.7, p < 0.001, df = 2; phanerophytes: F = 16.1, p < 0.001, df = 2; root/shoot ratio: F = 2.4, p = 0.091, df = 2, ANOVA). 5. Discussion The relative volumes and centroid positions of matorral input and outcome ecological variables were consistent with the hypothesis that matorral acts as an ecological process subsidizer or source from which espinal habitats gain ecological function outputs. Shrub espinal was overall the main beneficiary of the three ecosystem functions measured. Shrub espinal was the main provider of inputs to forage quality, while inputs for soil quality and woody plant establishment functions originated primarily in matorral. The main flow of these ecosystem functions was thus from matorral to shrub espinal and within shrub espinal. This is consistent with our prediction that matorral interacts most closely with shrub espinal. The results also suggest that at this site, succession is primarily moving from matorral to shrub espinal, one of the least understood successional transitions (Fig. 2). Espinal without shrub understory appeared to gain little spillover effect from espinal with shrubs or matorral. This may be consistent with espinal without shrubs being an ecosystem process sink at least for some processes. For example, both forage quality and woody plant establishment outcomes may be reduced due to intensive grazing by livestock. However, we are not aware of any examples of savanna, wood pasture, or open woodland with and without shrub understories where habitat with and without shrub understory arise via unconnected degradation trajectories (e.g. Olff et al., 1999; Barnes & Archer 1999; Peterson & Reich 2001; Pinto-Correia et al. 2010). In light of this, our results strongly suggest that all three habitats are successionally linked. An alternate explanation is that no ecosystem process movement is taking place. We did not directly measure ecosystem process movement, so each habitat may experience outputs caused only by its own inputs. In this scenario, inputs have outputs disproportionate to input values across habitats due to other, unmeasured factors that differ between habitats. Similarly, variance in one input might have a disproportionately large effect on outcomes. In this case, the results simply tell us that shrub espinal is the best converter of inputs to outputs, and management should focus on the production of shrub espinal. Under this scenario, as there is no interaction between habitats, there is no succession between them, and so the conditions under which shrub espinal is produced should be studied further. Functional traits and other variables were broadly distributed in a way favorable to management of espinals for silvopastoralism. Although the largest set (convex hull volume) of herbaceous forage quality variables was in shrub espinal, biomass and leaf area were highest in espinal, which is good for silvopastoral forage provision. On the other hand, there were more plants with anti-herbivory adaptations in espinal. Relative increases in the set of soil outcome variables were associated with higher values of nutrients in the soil and organic material. Although nitrogen fixation of A. caven may have made a large contribution to soil fertility outcomes in the espinal habitats, shrub espinal soil variables were more similar to (had a shorter centroid distance to) matorral. Shrub espinal showed the largest set of ecological variables for woody plant establishment, and also a wide range of dispersal, establishment and growth strategies likely to favor A. caven and nurse plant reproduction. Shrub espinal showed the largest abundance of plants investing in larger seeds dispersed by birds or mammals, as well as vegetatively reproducing plants. Consequently shrub espinal had a higher abundance weighted mean of sapling and shoot growth, a larger canopy, and more shade-tolerant species. In this case the greater variance in ecological variables corresponds to a wider range of plant reproductive strategies, and greater cover of established woody plants. While we do not show that a wider range of strategies has led to the observed higher woody plant cover, a greater range of possible reproductive and growth strategies should help shrub espinal to recover more quickly from a wider range of disturbances (e.g. livestock grazing, fire, wood collection). Recent work has suggested that at least one kind of successional process driven by natural disturbances is characterized by mosaic patches with non-overlapping functional traits and soil properties (Dantas et al., 2013). Breakpoints are created along a gradient of habitat transition via feedback from a filter such as fire frequency, and distinguish between communities (Dantas et al., 2013). What remains unclear is whether ecosystem processes move across breakpoints. One possibility is that they do, but they have no outcomes, generating the lack of functional overlap: rather than allowing exchange of ecosystem processes along a successional gradient, the disturbance regime creates a partial or total ecosystem process sink at the breakpoint (either directionally or mutually). This should have implications for the ecological systems in which functional ‘‘insurance’’ can operate. Landscape heterogeneity is expected to contribute to insurance against catastrophic regime shifts to degraded states (van Nes & Scheffer 2005). However, we predict that only mosaics with low levels of disturbance favor conditions that buffer against disturbance events. The landscape-scale management recommendation that emerges from these results is to maintain espinal in a mosaic with and without shrub understory, near patches of matorral. This contrasts with the current focus in Chile on protecting non-degraded native forests, excluding matorral and espinal. This research could be extended by examining temporal trends in anthropogenic uses, as well as climate change and ENSO events, to determine whether matorral also offers espinal ecosystem functioning ‘‘insurance’’ and whether matorral and espinal together buffer ecosystem functioning in other habitat

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types (Folke, 2006; Loreau et al. 2007; Maestre et al., 2012). Newton et al. (2011) modeled land-use scenarios suggest that moderate anthropogenic disturbances maintaining open habitats with A. caven, among other species, increase landscapelevel tree diversity. Spatially explicit modeling to assess the potential ranges of ecosystem process outcomes under these scenarios could assist in landscape-scale land use planning. Many landscapes are recommended for management as mosaics, due to benefits to livestock grazing, cultural landscape values, resilience of ecosystem processes, or habitat needs over the life histories of species (Law and Dickman, 1998; Fuhlendorf and Engle, 2001; Fischer et al., 2006; Brady et al., 2009; Newton et al., 2012; Kumaraswamy and Kunte, 2013). The costs and benefits of prioritizing the conservation of mosaics versus continuous habitats are specific to the habitat types, species present, and the management context (Bennett et al., 2006; Estrada and Coates-Estrada, 2002a,b; Ghazoul, 1996). In particular, it should be important to establish that where natural successional processes take place, ecosystem processes are shared between successional stages rather than forming breakpoints (e.g. Dantas et al., 2013). It should also be important to understand the direction of ecosystem processes of interest from input to outcome at the site in question. Here we have argued that espinal and matorral form a successional mosaic exchanging key ecosystem processes, of which shrub espinal is the primary beneficiary at our site. How shrub espinal in particular is successionally linked to sclerophyllous forest establishment is not clear and requires further research (Fig. 2). However, maintaining large mosaics of espinal–matorral could safeguard future sclerophyllous forest recovery, ecological processes and landscape resilience (Newton et al., 2011). Implementing conservation and management non-forest and anthropogenic habitats in contexts where environmental policy is weak presents multiple challenges (e.g. Root-Bernstein et al., 2013a,b; Sodi et al. 2010; Gockowski and Sonwa, 2011). Private and public actors need to be convinced why habitats in mutually exclusive cultural and policy categories (e.g. agriculture [espinal] vs. wilderness [matorral, sclerophyllous forest]) should be managed jointly. Inter-agency communication about policy could be argued to be necessary where there is inter-habitat exchange of key ecosystem processes underlying the flow of ecosystem services to agriculture (Villamagna et al., 2013). Our modified convex hull approach is a suitable tool for showing the flows and dependencies of ecosystem processes between habitat classes. As we show in this case study, this approach is sensitive to smaller-scale ecological processes, making it suitable for communication with private landowners as well as national planners. Quantifying ecosystem process interactions between orphan habitats can have management and policy implications. Within the central Chilean context, these results make an argument for considering espinal and matorral as a single land cover class for official conservation planning. Such a class would have the largest land cover of any forest or agricultural category (Schulz 2010; CONAF, 2011). A joint espinal–matorral landcover class could further provide an argument for creating a new ‘‘semi-natural habitat’’ or ‘‘cultural landscape’’ planning category, whose creation could bring numerous policy opportunities (Aronson et al., 1998; Root-Bernstein and Jaksic, 2013), including better protection of ecosystem services (Durán et al., 2013). Tying the management of espinal to the protection of matorral could also help to broaden the scope of silvopastoral management in central Chile to include considerations of ecosystem service resilience. There are many orphan habitats around the world e.g. Caatinga in Brazil, (Paes Marangon et al., 2013); Mediterranean forests of North Africa, Eastern Guinean forests and Nigerian lowland forests (Burgess et al., 2004); Indonesian beech forests (IUCN, 2008); and bamboo forests (Buckingham et al., 2011). Clearly demonstrating the magnitude of the ecological interlinkages across habitat types may help to raise the profile of many underprotected natural habitats. In addition, harmonizing agricultural and conservation policies is a challenge everywhere. 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