Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Influence of Anthropogenic Disturbances on Stand Structural Complexity in Andean Temperate Forests: Implications for Managing Key Habitat for Biodiversity

  • Julián Caviedes ,

    Contributed equally to this work with: Julián Caviedes, José Tomás Ibarra

    Affiliations Centre for Local Development, Education and Interculturality (CEDEL), Villarrica Campus, Pontificia Universidad Católica de Chile, Villarrica, La Araucanía Region, Chile, Fauna Australis Wildlife Laboratory, Department of Ecosystems and Environment, School of Agriculture and Forest Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile

    ⨯
  • José Tomás Ibarra

    Contributed equally to this work with: Julián Caviedes, José Tomás Ibarra

    jtibarra@uc.cl

    Affiliations Centre for Local Development, Education and Interculturality (CEDEL), Villarrica Campus, Pontificia Universidad Católica de Chile, Villarrica, La Araucanía Region, Chile, Fauna Australis Wildlife Laboratory, Department of Ecosystems and Environment, School of Agriculture and Forest Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile

    ⨯

Correction

13 Mar 2017: The PLOS ONE Staff (2017) Correction: Influence of Anthropogenic Disturbances on Stand Structural Complexity in Andean Temperate Forests: Implications for Managing Key Habitat for Biodiversity. PLOS ONE 12(3): e0174147. https://doi.org/10.1371/journal.pone.0174147 View correction

Abstract

Forest attributes and their abundances define the stand structural complexity available as habitat for faunal biodiversity; however, intensive anthropogenic disturbances have the potential to degrade and simplify forest stands. In this paper we develop an index of stand structural complexity and show how anthropogenic disturbances, namely fire, logging, livestock, and their combined presence, affect stand structural complexity in a southern Global Biodiversity Hotspot. From 2011 to 2013, we measured forest structural attributes as well as the presence of anthropogenic disturbances in 505 plots in the Andean zone of the La Araucanía Region, Chile. In each plot, understory density, coarse woody debris, number of snags, tree diameter at breast height, and litter depth were measured, along with signs of the presence of anthropogenic disturbances. Ninety-five percent of the plots showed signs of anthropogenic disturbance (N = 475), with the combined presence of fire, logging, and livestock being the most common disturbance (N = 222; 44% of plots). The lowest values for the index were measured in plots combining fire, logging, and livestock. Undisturbed plots and plots with the presence of relatively old fires (> 70 years) showed the highest values for the index of stand structural complexity. Our results suggest that secondary forests < 70-year post-fire event, with the presence of habitat legacies (e.g. snags and CWD), can reach a structural complexity as high as undisturbed plots. Temperate forests should be managed to retain structural attributes, including understory density (7.2 ± 2.5 # contacts), volume of CWD (22.4 ± 25.8 m3/ha), snag density (94.4 ± 71.0 stems/ha), stand basal area (61.2 ± 31.4 m2/ha), and litter depth (7.5 ± 2.7 cm). Achieving these values will increase forest structural complexity, likely benefiting a range of faunal species in South American temperate forests.

Citation: Caviedes J, Ibarra JT (2017) Influence of Anthropogenic Disturbances on Stand Structural Complexity in Andean Temperate Forests: Implications for Managing Key Habitat for Biodiversity. PLoS ONE 12(1): e0169450. https://doi.org/10.1371/journal.pone.0169450

Editor: Dusan Gomory, Technical University in Zvolen, SLOVAKIA

Received: May 24, 2016; Accepted: December 16, 2016; Published: January 9, 2017

Copyright: © 2017 Caviedes, Ibarra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: We thank the support from the Centre for Intercultural and Indigenous Research-CIIR (CONICYT/FONDAP/15110006), The Peregrine Fund, Ministry of the Environment (FPA 9-I-009-12), Rufford Small Grants for Nature Conservation (14397-2), Cleveland Metroparks Zoo, Cleveland Zoological Society, and Environment Canada for funding this research. JCP and JTI received a post-graduate scholarship from Comisión Nacional de Investigación Científica y Tecnológica de Chile (CONICYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: Despite we received funding from a commercial source, Cleveland Metroparks Zoos, this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

Forests are complex systems composed by multiple attributes that interact with each other across different spatial levels [1]. At the stand-level, structural complexity is the measure of a number of attributes present in a forest stand and the relative abundance of each of these attributes [2]. Studies have shown that stands with higher structural complexity generally harbor higher species diversity while promoting greater ecosystem functioning and stability in comparison to less complex ones [3–6]. Stand structural complexity can vary among different habitat types and successional stages [4,7,8]. For example, old-growth forests are positively related with stand structural complexity in Douglas-fir forests in North America [7]. However, structural complexity in certain secondary forest stages may be higher than in old-growth stands due to the presence of structural habitat legacies (i.e. standing dead trees, or snags, and coarse woody debris) remaining after disturbance [8,9].

Modification and adaptation of forests after disturbances make the quantification of structural attributes an extremely complicated task. Therefore, the combination of different key structural attributes into an index of stand structural complexity is a practical approach to guide forest conservation and management [2,3,10,11]. The rationale behind using an index of stand structural complexity is that higher values for the index indicate a more complex array (e.g. higher presence and relative abundances) of structural attributes, providing greater niches for a broad array of organisms while ameliorating negative relationships among them, such as depredation and competition [12–15].

Intensive anthropogenic disturbances, including fire, logging, and livestock farming have the potential to degrade the composition and availability of structural attributes in forests [16–18]. These disturbances can degrade the density of the understory, volume of coarse woody debris (CWD), density of snags, stand basal area, and litter depth [16–20]. The loss of these forest attributes are known to affect biodiversity including mammals, birds, reptiles, amphibians, and invertebrates that depend on these key structural habitat attributes for their survival [21–24]. For instance, a dense understory of herbs, bamboo, shrubs, and tree saplings, in combination with a deep leaf-litter, provide habitat for endemic mammals of temperate forests such as the kodkod cat (Leopardus guigna), the austral opossum (Dromiciops gliroides), and the southern pudu (Pudu puda) [25–27]. Coarse woody debris is a critical habitat for great crested flycatchers (Myiarchus crinitus), amphibians, and reptile species in Loblolly Pine Forests [22,28] and moist forests in the Pacific Northwest [29]. For its part, Siitonen [30] and Grove [31] found a strong correlation between coarse woody debris and invertebrate species richness in boreal forests of Finland and lowland rainforests in Australia, respectively. Many invertebrates, cavity nesting birds, mammals, and herpetofauna depend largely on the availability of snags for their survival [24,29,30,32]. For example, Lohr [28] reported that the reduction of snags had detrimental effects on bird communities in Loblolly Pine forests. For its part, Ross et al. [33] described that the relative abundance and species richness of salamanders were correlated with the retention of tree basal area across forest stands in Pennsylvania. Other studies have reported a positive correlation between litter depth and richness and density of herpetofauna in tropical forests of Costa Rica [34]. Similarly, Uetz [35] reported that more species of spiders were found in areas with greater litter depth in North American temperate deciduous forests.

South American temperate forests, found between 35° - 55° south latitude, are considered one of the 35 Global Biodiversity Hotspots [36,37]. Despite their ecological importance, temperate forests of South America have experienced a long history of anthropogenic disturbances (deforestation and degradation) [37,38]. Nearly 70% of Chilean temperate forests have been lost due to large-scale and small-scale disturbances [39]. Management practices that enhance structural complexity should be a priority in South American temperate forest stands, mainly because a large proportion of their biodiversity is located in private lands beyond protected areas [40]. An index of stand structural complexity, that could guide forest management and conservation policy, has never been proposed for South American temperate forests.

In this paper, we develop an index of stand structural complexity and identify if anthropogenic disturbances (fire, logging, and livestock), and their combined presence, influence stand structural complexity in South American temperate forests. We specifically address the following questions: (1) Is it feasible to generate a simple but integrative index of stand structural complexity based on important attributes for the habitat of forest biodiversity? (2) Is there an effect of anthropogenic disturbances on stand structural complexity? (3) Which of the three tested anthropogenic disturbances, or their combined presence, affects stand structural complexity more strongly? Answering these questions will provide valuable theoretical insights into the assessment of structural complexity and forces disturbing forest stands in a Global Biodiversity Hotspot. Further, it will contribute with practical information for the design of policy decisions regarding forest management to enhance faunal biodiversity in areas under forest management beyond protected areas.

Materials and Methods

Study area

We conducted this study between 2011 and 2013 within the Araucarias Biosphere Reserve [41]. Specifically, we conducted vegetation surveys in an area of 2,585 km2 within the Villarica watershed in the Andean zone of the La Araucanía Region, southern Chile (39° 15’S 71°W). The area has a temperate climate with a short dry season (< 4 months) in the summer and mean precipitations of 1,945 mm/year. The vegetation comprises three vegetation types distributed along an elevational gradient from 200 masl (meters above sea level), up to the tree line at ~1,500 masl. At lower elevations (200–500 masl), forests are dominated by species like Lophozonia obliqua and Nothofagus dombeyi, associated mainly with Laurelia sempervirens, Eucryphia cordifolia, Persea lingue, and Aextoxicon punctatum. Mid-elevation forests (500–900 masl) are mixed stands dominated by the evergreen species Saxegothaea conspicua, Laureliopsis philippiana, and N. dombeyi. At higher elevations (> 900 masl), forests are dominated by Araucaria araucana and Nothofagus pumilio [42]. The majority of public protected areas and large forest tracks are at high elevations (> 700 masl) with a topography characterized by mountains and volcanoes. Valley floors are mostly used for agriculture in combination with human settlements in small to medium-sized villages and towns. This has caused lowland forest remnants (< 700 masl) to experience intense anthropogenic disturbances, mainly fires, logging, and livestock farming [43].

Selection of structural attributes to be included in the index

We constructed the index of stand structural complexity adapting the methodology proposed by McElhinny et al. [44]. Constructing an index of stand structural complexity requires a four-step approach involving: (1) establishing a comprehensive suite of stand structural attributes, (2) identifying the core set of attributes to be included in the index based on the specific purpose of the index, (3) surveying these attributes in a set of different stands, including different vegetation communities and habitat types, and (4) combining the core set of attributes into an additive index. To address point one, we selected those attributes that were considered a structurally important attribute for the habitat of mammals, birds, reptiles, amphibians, and invertebrates in both Andean temperate and forests elsewhere (Table 1). The core set of attributes should (i) have a low kurtosis (< 2), (ii) work as a surrogate for other structural attributes, and (iii) be easily measured in the field [44]. The five selected attributes included density of the understory, volume of coarse woody debris (CWD), density of dead standing trees (i.e. snags), stand basal area, and litter depth (Fig 1). These attributes can be readily surveyed in areas under forest management practices [10,44–46].

thumbnail
Download:
Fig 1. Five selected stand structural attributes included in this study: understory density (A), volume of coarse woody debris (B), snag density (C), stand basal area (D), and litter depth (E).

https://doi.org/10.1371/journal.pone.0169450.g001

thumbnail
Download:
Table 1. List of five core attributes selected to be included in the index of stand structural complexity and their ecological importance for different faunal groups.

https://doi.org/10.1371/journal.pone.0169450.t001

Study design and forest sampling

We selected101 sites (N = 17 inside a public or private protected area) along an elevational gradient from 221 masl to 1,361 masl, including different habitat types such as old-growth forests (> 200 years old), secondary forests, arboreal shrublands, openfields, and exotic plantations. We selected the sites using ArcGIS 10.1 by identifying all the headwaters of smaller basins, within the Villarrica watershed, that could be accessed either by rural roads or hiking trails [76]. We randomly selected 13 out of the total 19 basins, placing the first site near the headwater of each basin [76]. We systematically located all the remaining sites every 1.5 km descending from the headwaters [76]. At each of the 101 sites, we established five vegetation plots (22.4 m diameter; 0.04 ha; N = 505 plots) using an L-shaped transect [76]. The first plot for each site was located at the vertex of the L-shaped transect. The remaining four plots were established with a distance of 125 m between each, along two 250 m lines directed outwards from the vertex [77]. Corporación Nacional Forestal (CONAF) allowed us to work on public protected areas (Permit # 11/2012 IX). In private areas, owners of the land gave permission to conduct the study on their properties. Field studies did not involve disturbing endangered or protected species.

In each vegetation plot, we measured a set of structural attributes, including: (1) understory density, (2) CWD, (3) number of snags, (4) tree diameter at breast height (DBH), and (5) litter depth (S1 File). We established two 11.2 m linear transects north to south from the center of the plot. Along this transect, we quantified understory density and litter depth at five points. We measured understory density by the number of contacts by the vegetation intercepting a 3 m vertical pole for each vertical meter up to 3 m height [78]. For each CWD with a diameter ≥ 7.5 cm, we measured the diameter at the center and the length. We calculated the volume of CWD (m3/ha) using the formula for volume of a cylinder. We measured tree DBH for all trees with DBH ≥ 12.5 cm. We counted all snags with DBH ≥ 12.5 cm and height ≥ 1.3 m [76]. We averaged the structural attributes within each plot in order to emphasize the unique structural conditions of each plot that differentiate it from adjacent areas [79]. In addition, we determined the presence or absence of anthropogenic disturbances (fire, logging, and livestock) for each plot. A series of high-severity human-set fires, that destroyed a large amount of stands in South American temperate forests, occurred in the early 20th century with the last stand-destroying event occurring between 1944 and 1945 [43,80,81]. Hence, fire occurring more than 70 years ago, was considered as the oldest disturbance and it was measured every time there were signs of fire-scars in a plot. We recorded logging when a plot had rests of logged wood or stumps without resprouts. We considered livestock activity when any signs of manure, pats, or browsed vegetation were present. We assigned each plot, in a 50 m radius, to one the following habitat types: (1) Old-growth forest (> 200 years old, N = 64), (2) mid-successional forest (< 70 years old with bamboo understory, N = 102), (3) mid-successional forest (< 70 years old with understory different than bamboo, N = 99), (4) mid-successional forest (< 70 years old without understory, N = 44), (5) early successional forest (< 20 years old, N = 75), (6) mixed shrubland (N = 87), (7) openfield (N = 28), and (8) exotic plantation (N = 6).

Data analysis

Developing an index of stand structural complexity.

To improve the distribution of the attributes showing a high kurtosis (> 2), we transformed the raw data to logarithm (x+1). We performed a regression analysis through quartiles to rescale each of the five selected stand structural attributes to a score ranging from 0 to 10. We set a score of 2.5, 5, 7.5, and 10 to the quartile midpoints corresponding to the 12.5, 37.5, 62.5, and 87.5 percentiles of the raw data distribution [10,44]. We attributed a maximum score of 10 to the 87.5 percentile while the equation was constrained so that the minimum score was 0. We obtained the index of stand structural complexity for each plot by adding all the rescaled values for the five measured structural attributes, with 0 being the minimum and 50 the maximum additive value. Thus, the total value of a plot with high structural complexity would approach a value of 50. Finally, we converted the index of stand structural complexity to percentage [10,44].

Relationship between disturbances and the index of stand structural complexity.

Exotic plantations covered < 2% of the study area; therefore, we excluded exotic plantations from all analyses. We allocated each of the remaining plots (N = 499) to one of eight treatments representing anthropogenic disturbance, as follows: no disturbance or control (Treatment one, T1), fire (T2), logging (T3), livestock (T4), combined presence of fire and logging (T5), combined presence of fire and livestock (T6), combined presence of logging and livestock (T7), combined presence of fire, logging, and livestock (T8). We used non-parametric Kruskal-Wallis tests, with a Holm-Sidak multiple comparison post hoc approach, to assess significant differences in the mean values of the index of stand structural complexity among both the seven habitat types and the eight different disturbance treatments. We performed all the statistical analyses using the statistical software R [82].

Results

Stand structural attributes and stand structural complexity across habitat types

The mean values of the five selected stand structural attributes—understory density, volume of CWD, snag density, stand basal area, and litter depth—varied according to habitat type (Table 2). The highest mean values of understory density (7.55 ± 9.1 # contacts), volume of CWD (13.74 ± 23.79 m3/ha), stand basal area (75.28 ± 35.62 m2/ha), and litter depth (6.57 ± 3.7 cm) were measured in ‘old-growth forest’ plots while the highest mean value of snag density (46.02 ± 33.2 stems/ha) was measured in ‘mid-successional forest with bamboo understory’ plots. Conversely, the lowest mean values for each of the five stand structural attributes were measured in ‘openfield’ plots.

thumbnail
Download:
Table 2. Results showing the mean values (SD) of the five stand structural attributes included in the index of stand structural complexity and the values of index of stand structural complexity expressed in percentage in relation to the seven different habitat types.

https://doi.org/10.1371/journal.pone.0169450.t002

There were significant differences in the mean values of the index of stand structural complexity among the seven habitat types (X2 = 302.0071, df = 6, p-value = 2.2e-16; Fig 2). The highest mean values for the index of stand structural complexity (80.03 ± 9.10) were measured in ‘old-growth forest’ while the lowest mean values for the index of stand structural complexity (34.38 ± 3.27) were measured in ‘openfield’ plots (Table 2).

thumbnail
Download:
Fig 2. Relationship between the mean values of the index of stand structural complexity and seven habitat types.

Bars with different letters were significantly different according to a Holm-Sidak post hoc test. Small bars are ± standard deviation.

https://doi.org/10.1371/journal.pone.0169450.g002

For easier implementation in forest conservation and management programs, we calculated the values of the five stand structural attributes associated with predicted low (0–33%), moderate (34–66%), and high (67–100%) values of the index of stand structural complexity in Andean temperate forests (Table 3).

thumbnail
Download:
Table 3. Mean and range (in brackets) values of the five stand structural attributes associated with the estimated index of stand structural complexity categorized as low (0–33%), moderate (34–66%), and high (67–100%) in Andean temperate forests.

https://doi.org/10.1371/journal.pone.0169450.t003

Anthropogenic disturbances and stand structural complexity

Ninety-five percent of the plots showed signs of anthropogenic disturbances (N = 475), with the combined presence of fire, logging, and livestock being the most common disturbance (N = 222; 44% of plots), and the combined presence of fire and livestock being the least common disturbance (N = 20). Only 24 plots did not show any signs of anthropogenic disturbance, from which 92% of these plots (N = 22) corresponded to ‘old-growth forests’.

The resulting mean values of the stand structural attributes that were selected to be included in the index of stand structural complexity varied according to the disturbance treatment (Table 4). The highest mean values of understory density (6.18 ± 2.87), volume of CWD (15.75 ± 30.35), and stand basal area (68.15 ± 28.34) were measured in plots that did not show any signs of disturbance. For its part, the highest mean values of snag density (60.93 ± 61.88) and litter depth (7.04 ± 3.1) were measured in plots that were subject to the combined presence of fire and livestock and fire respectively. Conversely, plots that were subject to the combined presence of logging and livestock showed the lowest mean values of understory density (2.72 ± 2.58) and volume of CWD (1.23 ± 2.24). Plots that were subject to the combined presence of fire, logging, and livestock showed the lowest mean values for litter depth (2.89 ± 2.76) and stand basal area (20.23 ± 24.46). The lowest mean value for snag density (18.13 ± 33.24) was measured in plots that were subject to livestock alone (Table 4).

thumbnail
Download:
Table 4. Results showing the mean (SD) values of both the stand structural attributes and the index of stand structural complexity for the eight disturbance treatments.

https://doi.org/10.1371/journal.pone.0169450.t004

The mean values for the index of stand structural complexity were significantly different among the eight disturbance treatments (X2 = 148.8512, df = 7, p-value = 2.2e-16) (Fig 3). The highest mean value of the index of stand structural complexity (77.67 ± 11.15) was measured in plots that did not show any signs of anthropogenic disturbances (control plots) while the lowest mean value of the index of stand structural complexity (52.47 ± 16.28) was measured in plots that were subject to the combined presence of fire, logging, and livestock. However, there were no significant differences in the mean values of the index of stand structural complexity among undisturbed plots (control) and plots that with the presence of fire alone (p = 0.73; Fig 3). The mean values of the index of stand structural complexity were lower in all the other six disturbance treatments compared to undisturbed plots. In addition, the presence of logging alone, livestock alone, the combined presence of logging and livestock, and plots with the presence of fire, logging, and livestock showed a significant difference with undisturbed plots (p < 0.05).

thumbnail
Download:
Fig 3. Relationship between the mean values of the index of stand structural complexity along a disturbance gradient.

Bars with different letters were significantly different according to a multiple comparison post hoc test. Small bars are ± standard deviation.

https://doi.org/10.1371/journal.pone.0169450.g003

Discussion

Indexes of stand structural complexity based on important attributes for the habitat of faunal species can be used to rank stands in terms of their potential contribution to biodiversity [4,44]. Anthropogenic disturbances, however, can degrade the composition and availability of these important structural attributes [16–18]. This work provides a novel index of stand structural complexity for South American temperate forests based on the quantification of key structural habitat attributes for faunal biodiversity: understory density, volume of CWD, snag density, stand basal area, and litter depth. Our results indicate that stand structural complexity is affected by logging and livestock, and the combined presence of fire, logging, and livestock. Nevertheless, the sole presence of fire occurring more than 70 years ago was the anthropogenic disturbance that showed the lowest effect on structural complexity. We suggest that specific forest management practices regarding the maintenance of structural attributes should be considered to enhance stand structural complexity in order to better conserve South American temperate forest biodiversity.

Variation of attributes across habitat types helps determining stand structural complexity

To conserve temperate forest biodiversity, the maintenance of different habitat types must become a priority for forest managers [8]. However, early successional forest stages, shrublands, and open areas frequently dominate landscapes undergoing anthropogenic disturbances [7,15,83]. Old-growth stands tend to present a larger availability of structural attributes such as understory density, volume of coarse woody debris, snag density, stand basal area, and litter depth than other habitat types [4,7,8,84]. However, we found that some dead-tree attributes (snags) may be higher in successional forest stands [4,8].

Similar to McElhinny et al. [44], we suggest that there is no single ideal stand structure but, instead, a combination of different attributes could produce a variety of structurally complex stands. Díaz et al. [45] and Hansen et al. [4] suggested that understory density was positively associated with old-growth stands in South American and North American temperate forests, respectively. The values of understory density reported in this study may be of great conservation importance for some endemic species such as the threatened rufous-legged owl (Strix rufipes) and some understory specialist birds from the Rhynocriptidae and Furnariidae families, with densities positively associated with a dense understory vegetation [23,78,85].

Coarse woody debris (CWD) increases structural complexity [2,86], having a positive effect on forest biodiversity [55,56,87]. Our results concurs with previous studies reporting that the volume of CWD tends to accumulate in less disturbed habitats [8,19,87,88]. The retention of CWD after disturbances is critical for the conservation of different forests species. For example, Siitonen [30] proposed that reduction of CWD at a landscape level might led to the disappearance of more than 50% of saproxylic species in managed forests in Finland.

Regarding snag density, our results were similar to the ones reported by Franklin [8], Carmona et al. [89], and Veblen et al. [90], for northern and southern temperate forests. Even though disturbances kill many trees, they often do not consume all wood structure, and thus a large proportion of the dead remnants (e.g. snags) are available for faunal species after disturbance. We found the highest mean values of snag density in ‘mid-successional forest with bamboo understory’. These snags readily become habitat legacies of disturbance events, and generally positively influence breeding site selection and fecundity of several faunal species [9,49,91].

Armesto and Figueroa [92] showed that stand basal area was directly correlated with less disturbed habitats. These findings support our results because the higher values of stand basal area were measured in old-growth plots. In addition, our values of stand basal area for Andean temperate forests are similar to the ones reported for other areas in southern temperate forests, where stand basal area was considerably higher in old-growth stands in comparison to early and mid-successional stands [45,89,93]. Similarly, basal area in northern temperate forests was an important structural attribute to discriminate among habitat types [88].

The high mean values of litter depth measured in this study for old-growth and mid-successional forest with bamboo understory plots illustrate the importance of bamboo plants for increasing stand structural complexity. Armesto and Fuentes [94], Christie and Armesto [87], and Veblen et al. [95] also observed changes in stand structure with higher litter depth produced by bamboo leaves. Spies and Franklin [88] reported higher values of litter depth in old-growth stands in comparison to young stands for northern temperate forests. However, even though litter depth is an easily measurable attribute, few studies have been undertaken in South American temperate forests regarding litter. This makes difficult to make comparisons among our results and other studies.

For its part, the lowest mean values of the five stand structural attributes measured in openfield plots are a direct consequence of land clearance for agriculture and livestock farming [80,92,96]. A common practice of land clearing is to remove CWD and snags for usage as timber-derived products or firewood [8,89].

Anthropogenic disturbances and stand structural complexity in Andean temperate forests

Stand structure has been shaped by a continuous history of anthropogenic disturbances in South American temperate forests [80,96]. The large extent of these disturbances helps to explain the high proportion of plots (N = 475; 95% of plots) that were subject to disturbance [43]. When comparing the writings of Charles Darwin about South American temperate forests with forest condition at present (150 years after Darwin′s observations), Willson and Armesto [97] stated that forest structural complexity described by Darwin is greatly absent nowadays because of logging, burning, and land clearing for agriculture.

Different authors have acknowledged the resilience capacity of temperate forests to moderate levels of anthropogenic disturbances [8,37,98]. Our results show that plots burned more than seven decades ago can achieve high to moderate levels of structural complexity, suggesting that South American temperate forests may have some resiliency to fire. Similar results were found for the structure of abandoned pastures in Puerto Rico, where basal area, aboveground biomass, and species richness in secondary forest achieved similar values to those in old-growth forest 40 years after fire [99]. A greater array of structural attributes would provide greater ecosystem resilience [100] and recovery after anthropogenic disturbances [101]. This hypothesis is supported by previous studies where the values of understory density and basal area increased in relation to the elapsed time since disturbance [45]. In addition, our unexpected high mean values of post-fire understory density may be explained by the proliferation of a dense bamboo understory. Following fire events, bamboos have been described to dominate forest understory, forming extensive thickets impeding the regeneration of other vegetative species [80,102].

The continuous cutting of snags and removal of logs for firewood, may be a potential explanation for the lower values of the index of stand structural complexity in plots subject to the combined presence of fire and logging. For instance, the low values of the volume of CWD measured in this study for plots that were subject to fire may be explained by the removal of logs and snags by landowners to be used or sold as firewood; a very common practice in southern Chile [89,98,103]. In addition, logging practices have resulted in the alteration and elimination of the understory due to the opening of roads for timber extraction with animals and machinery [104]. Studies in northern temperate forests have demonstrated that snags were three to five times more dense in undisturbed plots in comparison with logged plots [4]. Similarly, Aravena et al. [98] indicated that basal area increased significantly in undisturbed plots in comparison to plots that showed the combined presence of fire and logging. This supports findings of a reduction in stand structural attributes such as understory density, volume of CWD, and snag density in plots that were subject in areas under simultaneous fire and logging activities [4,50,89,105,106].

Interestingly, there was not a significant difference in the mean values of the index of stand structural complexity between plots that were subject to the presence of logging alone or in combination with fire. This result is comparable to previous studies [89,107–109], in which mean values of CWD volume, snag density, and basal area showed little differences between plots showing signs of fire in comparison to plots showing the combined presence of fire and logging. However, when assessing the difference between the mean values of structural attributes reported in those studies with our findings, the results should be viewed with caution because the elapsed time since the disturbance may have been different. For instance, the post-fire plots measured in this study may have been immediately abandoned after the occurrence of the last catastrophic fire that occurred 70 years ago, having a longer recovery time since disturbance.

The introduction of livestock by European colonists has had a detrimental effect on stand structure by reducing stand attributes such as understory density, basal area, and litter depth [20,43,81,90]. Our results correspond with this statement as they show that the presence of livestock—solely or in combination with other disturbances—is the most important factor decreasing stand structural complexity in South American temperate forests. Tasker and Bradstock [110] reached the same conclusion when studying the influence of livestock grazing on forest understory in New South Wales, Australia. Similarly, livestock had a stronger negative effect on forest regeneration in comparison to logging in the composition and structure of southern temperate forests [111].

Grazing activity by livestock not only affects understory density but other structural attributes. For example, low values for stand basal area, and for size and abundance of woody plants have been reported for forests with a high density of livestock [93,112]. Also, regeneration of trees (e.g. A. araucana) is significantly affected by the presence of livestock [20]. Livestock also affects understory structure by grazing and trampling the herbaceous layer [113]. For example, Rummell [114] showed that grazing by livestock reduced understory vegetation by 45–61% in ponderosa pine forests. In Argentina, understory density—mainly of bamboo—was significantly reduced in highly grazed stands in comparison to less disturbed stands [112].

We did not find information on the effects that livestock has on CWD volume or snag density in South American temperate forests. However, our results suggest that the reduction of understory density and basal area by livestock is indirectly affecting other structural attributes such as the volume of CWD, snag density, and litter depth. For instance, the lowest mean values of litter depth reported in this study were measured in plots that were subject to the presence of livestock. This result may be associated with the fact that livestock grazing of plant biomass located above ground minimizes the quantity of biomass for litter conversion [113]. Similarly, Hayes and Holl [115] reported that litter depth was significantly lower in grazed sites in comparison to ungrazed sites when investigating the impact of cattle grazing on a coastal prairie plant community in California. On the other hand, the low mean values of volume of CWD and stand basal area in plots that were subject to livestock may be due to the practice of removing bamboo understory as well as other woody debris for aesthetic and logging activity reasons [48].

Recommendations for management

In South American temperate forests, rapid degradation at the forest stand level and the lack of protection beyond protected areas are calling for novel ways of managing forests to prevent the loss of biodiversity. Our results suggest that temperate forest biodiversity will be benefited if management initiatives, promoting the retention of structural attributes and the reduction of anthropogenic disturbances, are implemented.

We show that there is no single ideal stand structure or habitat type that maximizes structural complexity. However, our results indicate that forest management schemes should aim to retain structural attributes including understory density (7.2 ± 2.5 # contacts), volume of CWD (22.4 ± 25.8 m3/ha), snag density (94.4 ± 71.0 stems/ha), stand basal area (61.2 ± 31.4 m2/ha), and litter depth (7.5 ± 2.7 cm). These desired values of structural attributes might be reached by retaining large trees (DBH > 53 cm) and retaining a dense bamboo understory. The application of the index of stand structural complexity in order to guide conservation management will likely benefit temperate forest biodiversity [45,48,85].

Supporting Information

S1 File. Vegetation plot data containing plot numbers, habitat type, presence of fire, logging, and livestock as well as the following stand structural attributes: understory density (# contacts), volume of coarse woody debris (m3/ha), stand basal area (m2/ha), litter depth (cm), snag density (stems/ha), and the values for the index of stand structural complexity (%) for each plot.

https://doi.org/10.1371/journal.pone.0169450.s001

(XLSX)

Acknowledgments

We thank the Chilean Forest Service (CONAF), M. Venegas and R. Sanhueza (Guías-Cañe), Kawelluco Private Sanctuary, and many other landowners who allowed us to work in their lands. We are especially grateful to Tomás Altamirano for all his help and support during the project. Dr. Jan Axmacher and Franca Wolf provided valuable comments on an earlier version of this manuscript.

Author Contributions

  1. Conceptualization: JTI.
  2. Data curation: JTI JC.
  3. Formal analysis: JC.
  4. Funding acquisition: JTI.
  5. Investigation: JC JTI.
  6. Methodology: JTI JC.
  7. Project administration: JTI.
  8. Resources: JTI.
  9. Software: JC.
  10. Supervision: JTI.
  11. Validation: JC JTI.
  12. Visualization: JC.
  13. Writing – original draft: JC JTI.
  14. Writing – review & editing: JTI JC.

References

  1. 1. Messier C, Puettmann KJ. Forests as complex adaptive systems: implications for forest management and modelling. L’Italia For e Mont. 2011;66: 249–258.
  2. 2. McElhinny C, Gibbons P, Brack C, Bauhus J. Forest and woodland stand structural complexity: Its definition and measurement. For Ecol Manage. 2005;218: 1–24.
  3. 3. Beckschäfer P, Mundhenk P, Kleinn C, Ji Y, Yu DW, Harrison RD. Enhanced Structural Complexity Index: An Improved Index for Describing Forest Structural Complexity. Open J For. 2013;03: 23–29.
  4. 4. Hansen AJ, Spies TA, Swanson FJ, Ohmann JL. Conserving Biodiversity in Managed Forests. Bioscience. 1991;41: 382–392.
  5. 5. Ibarra JT, Martin K. Beyond species richness: an empirical test of top predators as surrogates for functional diversity and endemism. Ecosphere. 2015;6: 142. http://dx.doi.org/10.1890/ES15-00207.1
  6. 6. Persiani A, Lombardi F, Lunghini D, Granito V, Tognetti R, Maggi O, et al. Stand structure and deadwood amount influences saproxylic fungal biodiversity in Mediterranean mountain unmanaged forests. iForest—Biogeosciences For. 2016;9: 115–124.
  7. 7. Zenner EK. Does old-growth condition imply high live-tree structural complexity? For Ecol Manage. 2004;195: 243–258.
  8. 8. Franklin J. Structural and Functional Diversity in Temperate Forests. In: Wilson E, Peter F, editors. Biodiversity. Washington, D.C.: National Academy Press; 1988. pp. 166–176.
    • 9. Franklin J, Spies TA, Pelt R Van, Carey AB, Thornburgh DA, Berg DR, et al. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manage. 2002;155: 399–423.
    • 10. Sabatini FM, Burrascano S, Lombardi F, Chirici G, Blasi C. An index of structural complexity for Apennine beech forests. iForest. 2015;8: 314–323.
    • 11. Tews J, Brose U, Grimm V, Tielbörger K, Wichmann MC, Schwager M, et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr. 2004;31: 79–92.
    • 12. McElhinny C, Gibbons P, Barack C, Bauhus J. Fauna-habitat relationships: a basis for indentifying key stand structural attriutes in temperate Australian eucalypt forests and woodlands. Pacific Conserv Biol. 2006;12: 89–110.
    • 13. Braunisch V, Coppes J, Arlettaz R, Suchant R, Zellweger F, Bollmann K. Temperate Mountain Forest Biodiversity under Climate Change: Compensating Negative Effects by Increasing Structural Complexity. Chen J, editor. PLoS One. 2014;9: e97718. pmid:24823495
    • 14. Newton PF. Complex stand structures and associated dynamics: measurement indices and modelling approaches. Forestry. 2008;81: 573–574.
    • 15. Franklin J, Van Pelt R. Spatial Aspects of Structural Complexity in Old-Growth Forests. J For. 2004;102: 22–28.
    • 16. Mishra BP, Tripathi OP, Tripathi RS, Pandey HN. Effects of anthropogenic disturbance on plant diversity and community structure of a sacred grove in Meghalaya, northeast India. Biodivers Conserv. 2004;13: 421–436.
    • 17. Liira J, Sepp T, Parrest O. The forest structure and ecosystem quality in conditions of anthropogenic disturbance along productivity gradient. For Ecol Manage. 2007;250: 34–46.
    • 18. Ramírez-Marcial N, González-Espinosa M, Williams-Linera G. Anthropogenic disturbance and tree diversity in Montane Rain Forests in Chiapas, Mexico. For Ecol Manage. 2001;154: 311–326.
    • 19. Harmon M, Franklin J, Swanson F, Sollins P, Gregory S, Lattin J, et al. Ecology of Coarse Woody Debris in Temperate Ecosystems. Advances in Ecological Research. 2004. pp. 59–234.
    • 20. Zamorano-Elgueta C, Cayuela L, González-Espinosa M, Lara A, Parra-Vázquez MR. Impacts of cattle on the South American temperate forests: Challenges for the conservation of the endangered monkey puzzle tree (Araucaria araucana) in Chile. Biol Conserv. 2012;152: 110–118.
    • 21. Malcolm JR, Ray JC. Influence of Timber Extraction Routes on Central African Small-Mammal Communities, Forest Structure, and Tree Diversity. Conserv Biol. 2000;14: 1623–1638.
    • 22. Owens AK, Moseley KR, McCay TS, Castleberry SB, Kilgo JC, Ford WM. Amphibian and reptile community response to coarse woody debris manipulations in upland loblolly pine (Pinus taeda) forests. For Ecol Manage. 2008;256: 2078–2083.
    • 23. Ibarra JT, Gálvez N, Gimona A, Altamirano TA, Rojas I, Hester A, et al. Rufous-legged Owl (Strix rufipes) and Austral Pygmy Owl (Glaucidium nanum) stand use in a gradient of disrupted and old growth Andean temperate forests, Chile. Stud Neotrop Fauna Environ. 2012;47: 33–40.
    • 24. Ibarra JT, Martin K. Biotic homogenization: Loss of avian functional richness and habitat specialists in disturbed Andean temperate forests. Biol Conserv. Elsevier B.V.; 2015;192: 418–427.
    • 25. Acosta-Jamett G, Simonetti JA. Habitat use by Oncifelis guigna and Pseudalopex culpaeus in a fragmented forest landscape in central Chile. Biodivers Conserv. 2004;13: 1135–1151.
    • 26. Meier D, Merino ML. Distribution and habitat features of southern pudu (Pudu puda Molina, 1782) in Argentina. Mamm Biol. 2007;72: 204–212.
    • 27. Celis-Diez JL, Hetz J, Marín-Vial PA, Fuster G, Necochea P, Vásquez RA, et al. Population abundance, natural history, and habitat use by the arboreal marsupial Dromiciops gliroides in rural Chiloé Island, Chile. J Mammal. 2012;93: 134–148.
    • 28. Lohr SM, Gauthreaux SA, Kilgo JC. Importance of Coarse Woody Debris to Avian Communities in Loblolly Pine Forests. Conserv Biol. 2002;16: 767–777.
    • 29. Dupuis LA, Smith JNM, Bunnell F. Relation of Terrestrial-Breeding Amphibian Abundance to Tree-Stand Age. Conserv Biol. 1995;9: 645–653.
    • 30. Siitonen J. Forest management, coarse woody debris and saproxylic organism: Fennoscandian boreal forests as an example. Ecol Bull. 2001;49: 11–41.
    • 31. Grove SJ. Tree basal area and dead wood as surrogate indicators of saproxylic insect faunal integrity: a case study from the Australian lowland tropics. Ecol Indic. 2002;1: 171–188.
    • 32. Gibbons P, Lindenmayer DB, Barry SC, Tanton MT. Hollow selection by vertebrate fauna in forests of southeastern Australia and implications for forest management. Biol Conserv. 2002;103: 1–12.
    • 33. Ross B, Fredericksen T, Ross E, Hoffman W, Morrison M, Beyea J, et al. Relative Abundance and Species Richness of Herpetofauna in Forest Stands in Pennsylvania. For Sci. 2000;46: 139–146.
    • 34. Fauth JE, Crother BI, Slowinski JB. Elevational patterns of species richness, evenness, and abundance of the Costa Rican leaf-litter herpetofauna. Biotropica. 1989;21: 178–185.
    • 35. Uetz GW. The influence of variation in litter habitats on spider communities. Oecologia. 1979;40: 29–42.
    • 36. Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GA, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403: 853–8. pmid:10706275
    • 37. Neira E, Verscheure H, Revenga C. Chile’s Frontier Forests: Conserving a Global Treasure. Chile: Library of Congress; 2002.
      • 38. Echeverría C, Coomes D, Salas J, Rey-Benayas JM, Lara A, Newton A. Rapid deforestation and fragmentation of Chilean Temperate Forests. Biol Conserv. 2006;130: 481–494.
      • 39. Lara A. La conservación del bosque nativo en Chile: problemas y desafíos. In: Armesto J, Villagrán C, Arroyo M, editors. Ecología de Los Bosques Nativos de Chile. Santiago: Editorial Universitaria; 1996. pp. 335–362.
        • 40. Armesto J, Smith-Ramírez C, León P, Arroyo M. Biodiversidad y conservación del bosque templado en Chile. Ambient y Desarro. 1992; 19–24.
        • 41. UNESCO. World Network of Biosphere Reserves (WNBR). In: Ecological Sciences for Sustainable Development [Internet]. 2016 [cited 22 Jun 2015]. http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/world-network-wnbr/wnbr/
          • 42. Gajardo R. La vegetación natural de Chile: clasificación y distribución geográfica. Santiago, Chile: Editorial Universitaria; 1993.
            • 43. Veblen T, Kitzberger T, Lara A. Disturbance and forest dynamics along a transect from Andean rain forest to Patagonian shrubland. J Veg Sci. 1992;3: 507–520.
            • 44. McElhinny C, Gibbons P, Brack C. An objective and quantitative methodology for constructing an index of stand structural complexity. For Ecol Manage. 2006;235: 54–71.
            • 45. Díaz I, Armesto J, Reid S, Sieving K, Willson M. Linking forest structure and composition: avian diversity in successional forests of Chiloé Island, Chile. Biol Conserv. 2005;123: 91–101.
            • 46. Eyre TJ, Butler DW, Kelly AL, Wang J. Effects of forest management on structural features important for biodiversity in mixed-age hardwood forests in Australia’s subtropics. For Ecol Manage. 2010;259: 534–546.
            • 47. Meserve PL, Lang BK, Murua R, Muñoz-Pedreros A, González LA. Characteristics of a terrestrial small mammal assemblage in a temperate rainforest in Chile. Rev Chil Hist Nat. 1991;64: 157–169.
            • 48. Reid S, Díaz I, Armesto J, Willson M. Importance of native bamboo for understory birds in Chilean temperate forests. Auk. 2004;121: 515–525.
            • 49. Ibarra JT, Altamirano TA, Martin K, Vargas FH, Bonacic C. Tree–cavity Nesting of Austral Pygmy–Owls (Glaucidium nana) in Andean Temperate Forests of Southern Chile. J Raptor Res. 2014;48: 82–85.
            • 50. Thiollay J. Influence of selective logging on bird species diversity in a Guianan rain forest. Biol Conserv. 1993;64: 245–246.
            • 51. Aleixo A. Effects of Selective Logging on a Bird Community in the Brazilian Atlantic Forest. Condor. 1999;101: 537–548.
            • 52. Whitfield SM, Bell KE, Philippi T, Sasa M, Bolanos F, Chaves G, et al. Amphibian and reptile declines over 35 years at La Selva, Costa Rica. Proc Natl Acad Sci. 2007;104: 8352–8356. pmid:17449638
            • 53. Fowler HG, Silva CA, Venticinque E. Size, taxonomic and biomass distributions of flying insects in Central Amazonia: forest edge vs. understory. Rev Biol Trop. 1993;41: 755–760.
            • 54. Kelt DA, Martinez DR. Notes on Distribution and Ecology of Two Marsupials Endemic to the Valdivian Forests of Southern South America. J Mammal. 1989;70: 220–224.
            • 55. Kelt DA, Meserve PL, Lang BK. Quantitative Habitat Associations of Small Mammals in a Temperate Rainforest in Southern Chile: Empirical Patterns and the Importance of Ecological Scale. J Mammal. 1994;75: 890–904.
            • 56. Soto-Azat C, Valenzuela-Sánchez A, Collen B, Rowcliffe JM, Veloso A, Cunningham AA. The Population Decline and Extinction of Darwin’s Frogs. Gratwicke B, editor. PLoS One. 2013;8: e66957. pmid:23776705
            • 57. Bowman JC, Sleep D, Forbes GJ, Edwards M. The association of small mammals with coarse woody debris at log and stand scales. For Ecol Manage. 2000;129: 119–124.
            • 58. Davis JC, Castleberry SB, Kilgo JC. Influence of coarse woody debris on herpetofaunal communities in upland pine stands of the southeastern Coastal Plain. For Ecol Manage. Elsevier B.V.; 2010;259: 1111–1117.
            • 59. Tomasevic JA, Estades CF. Stand attributes and the abundance of secondary cavity-nesting birds in southern beech (Nothofagus) forests in South-Central Chile. Ornitol Neotrop. 2006;17: 1–14.
            • 60. Ojeda V, Chazarreta L. Home range and habitat use by Magellanic Woodpeckers in an old-growth forest of Patagonia. Can J For Res. 2014;44: 1265–1273.
            • 61. Díaz S, Kitzberger T. Nest habitat selection by the Austral parakeet in north-western Patagonia. Austral Ecol. 2013;38: 268–278.
            • 62. Smith AP, Wellham GS, Green SW. Seasonal foraging activity and microhabitat selection by echidnas (Tachyglossus aculeatus) on the New England Tablelands. Aust J Ecol. 1989;14: 457–466.
            • 63. Nappi A, Drapeau P, Saint-Germain M, Angers VA. Effect of fire severity on long-term occupancy of burned boreal conifer forests by saproxylic insects and wood-foraging birds. Int J Wildl Fire. 2010;19: 500.
            • 64. Bennett AF, Lumsden LF, Nicholls AO. Tree hollows as a resource for wildlife in remnant woodlands: Spatial and temporal patterns across the northern plains of Victoria, Australia. Pacific Conserv Biol. 1994;1: 222–235.
            • 65. Raphael M, White M. Use of Snags by Cavity-Nesting Birds in the Sierra Nevada. Wildl Monogr. 1984; 3–66.
              • 66. Dunstone N, Durbin L, Wyllie I, Freer R, Jamett G a, Mazzolli M, et al. Spatial organization, ranging behaviour and habitat use of the kodkod (Oncifelis guigna) in southern Chile. J Zool. 2002;257: 1–11.
              • 67. Carneiro APB, Jiménez JE, Vergara PM, White TH. Nest-site selection by Slender-billed Parakeets in a Chilean agricultural-forest mosaic. J F Ornithol. 2013;84: 13–22.
              • 68. Johns AD. Effects of “Selective” Timber Extraction on Rain Forest Structure and Composition and Some Consequences for Frugivores and Folivores. Biotropica. 1988;20: 31.
              • 69. Crump ML. Natural History of Darwin’s frog, Rhinoderma darwinii. Herpetol Nat Hist. 2002;9: 21–30.
              • 70. Willson MF, Sabag C, Figueroa J, Armesto JJ, Caviedes M. Seed dispersal by lizards in Chilean rainforest. Rev Chil Hist Nat. 1996;69: 339–342.
              • 71. Kuschel G. Terrestrial Zoology in Southern Chile. Proc R Soc B Biol Sci. 1960;152: 540–550.
              • 72. Reid S, Cornelius C, Barbosa O, Meynard C, Silva-García C, Marquet PA. Conservation of temperate forest birds in Chile: implications from the study of an isolated forest relict. Biodivers Conserv. 2002;11: 1975–1990.
              • 73. Barbosa O, Marquet PA. Effects of forest fragmentation on the beetle assemblage at the relict forest of Fray Jorge, Chile. Oecologia. 2002;132: 296–306.
              • 74. Laurance SGW. Responses of understory rain forest birds to road edges in central Amazonia. Ecol Appl. 2004;14: 1344–1357.
              • 75. Yahner RH. Microhabitat Use by Small Mammals in Even-Aged Forest Stands. Am Midl Nat. 1986;115: 174.
              • 76. Ibarra JT, Martin K, Altamirano TA, Vargas FH, Bonacic C. Factors associated with the detectability of owls in South American temperate forests: Implications for nocturnal raptor monitoring. J Wildl Manage. 2014;78: 1078–1086.
              • 77. Affleck DLR, Gregoire TG, Valentine HT. Design unbiased estimation in line intersect sampling using segmented transects. Environ Ecol Stat. 2005;12: 139–154.
              • 78. Díaz IA, Armesto JJ, Willson MF. Mating success of the endemic Des Murs’ Wiretail (Sylviorthorhynchus desmursii, Furnariidae) in fragmented Chilean rainforests. Austral Ecol. 2006;31: 13–21.
              • 79. Franklin J, Spies TA, Pelt R Van, Carey AB, Thornburgh DA, Berg DR, et al. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For Ecol Manage. 2002;155: 399–423.
              • 80. Veblen T, Ashton DH. Catastrophic influences on the vegetation of the Valdivian Andes, Chile. Vegetatio. 1978;36: 149–167.
              • 81. González ME, Veblen TT, Sibold JS. Fire history of Araucaria-Nothofagus forests in Villarrica National Park, Chile. J Biogeogr. 2005;32: 1187–1202.
              • 82. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2014. http://www.r-project.org/
                • 83. Franklin JF, Spies TA. Composition, Function, and Structure of Old-Growth Douglas-Fir Forests. In: Ruggiero L, Aubry K, Carey A, Huff M, editors. Wildlife and Vegetation of Unmanaged Douglas-fir Forests. Portland, Oregon; 1991. pp. 71–80.
                  • 84. Armesto J, Smith-Ramirez C, Rozzi R. Conservation strategies for biodiversity and indigenous people in Chilean forest ecosystems. J R Soc New Zeal. 2001;31: 865–877.
                  • 85. Ibarra JT, Martin K, Drever M, Vergara G. Occurrence patterns and niche relationships of sympatric owls in South American temperate forests: a multi-scale approach. For Ecol Manage. 2014;1: 1–35.
                  • 86. Pasher J, King DJ. Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data. Can J For Res. 2011;41: 44–58.
                  • 87. Christie DA, Armesto JJ. Regeneration microsites and tree species coexistence in temperate rain forests of Chiloe Island, Chile. J Ecol. 2003;91: 776–784.
                  • 88. Spies TA, Franklin JF. The Structure of Natural Young, Mature, and Old-Growth Douglas-Fir Forests in Oregon and Washington. In: Ruggiero L, Aubry K, Carey A, Huff M, editors. Wildlife and Vegetation of Unmanaged Douglas-fir Forests. Portland, Oregon; 1991. pp. 91–109.
                    • 89. Carmona MR, Armesto JJ, Aravena JC, Pérez CA. Coarse woody debris biomass in successional and primary temperate forests in Chiloé Island, Chile. For Ecol Manage. 2002;164: 265–275.
                    • 90. Veblen T, Donoso C, Kitzberger T, Rebertus A. Ecology of Southern Chilean and Argentinean Nothofagus Forests. In: Veblen T, Hill R, Read J, editors. The Ecology and Biogeography ofNothofagus Forests. New Haven, USA: Yale University Press; 1996. pp. 293–353.
                      • 91. Wiebe KL. Responses of cavity-nesting birds to fire: testing a general model with data from the Northern Flicker. Ecology. 2014;95: 2537–2547.
                      • 92. Armesto J, Figueroa J. Stand Structure and Dynamics in the Temperate Rain Forests of Chiloé Archipelago, Chile. J Biogeogr. 1987;14: 367.
                      • 93. Echeverría C, Newton AC, Lara A, Benayas JMR, Coomes DA. Impacts of forest fragmentation on species composition and forest structure in the temperate landscape of southern Chile. Glob Ecol Biogeogr. 2007;16: 426–439.
                      • 94. Armesto J, Fuentes ER. Tree species regeneration in a mid-elevation, temperate rain forest in Isla de Chiloé, Chile. Vegetatio. 1988;74: 151–159.
                      • 95. Veblen T, Ashton DH, Schlegel FM, Veblen AT. Distribution and Dominance of Species in the Understorey of a Mixed Evergreen-Deciduous Nothofagus Forest in South-Central Chile. J Ecol. 1977;65: 815.
                      • 96. Veblen T, Schlegel FM, BE R.. Structure and Dynamics of Old-Growth Nothofagus Forests in the Valdivian Andes, Chile. J Ecol. 1980;68: 1.
                      • 97. Willson MF, Armesto JJ. The natural history of Chiloé: on Darwin’s trail. Rev Chil Hist Nat. 1996;69: 149–161.
                      • 98. Aravena JC, Carmona MR, Pérez CA, Armesto JJ. Changes in tree species richness, stand structure and soil properties in a successional chronosequence in northern Chiloé Island, Chile. Rev Chil Hist Nat. 2002;75: 339–360.
                      • 99. Aide TM, Zimmerman JK, Pascarella JB, Rivera L, Marcano-Vega H. Forest Regeneration in a Chronosequence of Tropical Abandoned Pastures: Implications for Restoration Ecology. Restor Ecol. 2000;8: 328–338.
                      • 100. Peck JE, Zenner EK, Brang P, Zingg A. Tree size distribution and abundance explain structural complexity differentially within stands of even-aged and uneven-aged structure types. Eur J For Res. 2014;133: 335–346.
                      • 101. O’Hara KL. Multiaged forest stands for protection forests: concepts and applications. For Snow Landsc Res. 2006;80: 45–55.
                      • 102. Veblen T. Growth Patterns of Chusquea Bamboos in the Understory of Chilean Nothofagus forests and Their Influences in Forest Dynamics. Bull Torrey Bot Club. 1982;109: 474–487.
                      • 103. Smith-Ramírez C, Maturana V, Gaxiola A, Carmona M. Salvage Logging by Indigenous People in a Chilean Conifer Forest. For Sci. 2014;60: 1–7. http://dx.doi.org/10.5849/forsci.12-079
                      • 104. Jaña-Prado R, Celis-Diez JL, Gutiérrez ÁG, Cornelius C, Armesto JJ. Diversidad en bosques fragmentados de Chiloé: ¿Son todos los fragmentos iguales? In: Grez A, Simonetti J, Bustamante R, editors. Biodiversidad en ambientes fragmentados de Chile: patrones y procesos diferentes escalas. Santiago, Chile: Editorial Universitaria; 2007. pp. 159–189.
                        • 105. Youngblood A. Thinning and Burning in Dry Coniferous Forests of the Western United States: Effectiveness in Altering Diameter Distributions. For Sci. 2010;56: 46–59.
                        • 106. Hitimana J, Kiyiapi JL, Njunge JT. Forest structure characteristics in disturbed and undisturbed sites of Mt. Elgon Moist Lower Montane Forest, western Kenya. For Ecol Manage. 2004;194: 269–291.
                        • 107. Aravena C, Hutchinson WG, Longo A. Environmental pricing of externalities from different sources of electricity generation in Chile. Energy Econ. Elsevier B.V.; 2012;34: 1214–1225.
                        • 108. Gutiérrez A, Armesto J, Aravena J. Disturbance and regeneration dynamics of an old-growth North Patagonian rain forest in Chiloé Island, Chile. J Ecol. 2004;92: 598–608.
                        • 109. Seaman BJ, Albornoz FE, Armesto JJ, Gaxiola A. Phosphorus conservation during post-fire regeneration in a Chilean temperate rainforest. Austral Ecol. 2015;40: 709–717.
                        • 110. Tasker EM, Bradstock RA. Influence of cattle grazing practices on forest understorey structure in north-eastern New South Wales. Austral Ecol. 2006;31: 490–502.
                        • 111. Zamorano-Elgueta C, Cayuela L, Rey-Benayas JM, Donoso PJ, Geneletti D, Hobbs RJ. The differential influences of human-induced disturbances on tree regeneration community: a landscape approach. Ecosphere. 2014;5: art90.
                        • 112. Veblen T, Mermoz M, Martin C, Kitzberger T. Ecological impacts of introduced animals in Nahuel Huapi National Park, Argentina. Biol Conserv. 1993;64: 180.
                        • 113. Belsky AJ, Blumenthal DM. Effects of Livestock Grazing on Stand Dynamics and Soils in Upland Forests of the Interior West. Efectos del Pastoreo sobre la Dinamica de Arboles y Suelos en Bosques en el Altiplano del Occidente Interior. Conserv Biol. 1997;11: 315–327.
                        • 114. Rummell RS. Some Effects of Livestock Grazing on Ponderosa Pine Forest and Range in Central Washington. Ecology. 1951;32: 594–607.
                        • 115. Hayes GF, Holl KD. Cattle Grazing Impacts on Annual Forbs and Vegetation Composition of Mesic Grasslands in California. Conserv Biol. 2003;17: 1694–1702.