Translator Disclaimer
1 January 2020 Tropical ecosystems vulnerability to climate change in southern Ecuador
Author Affiliations +
Abstract

Tropical ecosystems are among the most vulnerable to climate change. Understanding climate impacts on these ecosystems is a primary challenge for policy makers, ecologists, and conservationists today. We analyzed the vulnerability of ecosystems in a very heterogeneous tropical region in southern Ecuador, selected because of its exceptional biodiversity and its ecosystem services provided to people of southern Ecuador and northern Peru. The vulnerability assessment focused on three components: exposure, sensitivity, and adaptive capacity. For the first two components, we identified stressors or drivers of change that negatively influence ecosystems. For the third component, we identified existing and potential buffers that reduce impacts. This process was developed in workshops and by expert elicitation. Representative Concentration Pathway (RCP) scenarios were used, considering RCP 2.6 and RCP 8.5 for a time horizon to 2050. Under the RCP 2.6 scenario, the components of overall vulnerability in the southern region of Ecuador showed very low to moderate vulnerability for most areas, particularly in semi-deciduous forest ecosystems, Amazon semi-deciduous forest, Amazon rainforest, and mangrove forests. These areas had high vulnerability under the RCP 8.5 scenario. A variety of conservation strategies (e.g., protected areas) were shown to increase the adaptive capacity of ecosystems and reduce their vulnerability. We therefore recommend improving these conservation initiatives in ecosystems like dry forests, where the greatest vulnerability is evident.

Introduction

Climate change entails many challenges for future decades, especially potential impacts on people, crop production (IPCC, 2014; Thornton, Ericksen, Herrero, & Challinor, 2014; Wheeler & Von-Braun, 2013), and species conservation (Dawson, Jackson, House, Prentice, & Mace, 2011; McCarty, 2001). All climate models project a rise in temperature in South America, while the precipitation projections disagree and show either an increase or a decrease in coming decades (IPCC, 2016). For the Southern Region of Ecuador (SRE), most projections show an increase in precipitation and all show increases in temperature, suggesting increased climatic variation that would impact ecosystems and their services (Colwell, Brehm, Cardelús, Gilman, & John, 2008; Foster, 2001; Furniss et al., 2013; Glick, Stein, & Edelson, 2011; Pearson, 2006; Thornton et al., 2014; Thuiller et al., 2008). This problem could be exacerbated by population growth (Jiang & Hardee, 2011; Nagendra, Sudhira, Katti, & Schewenius, 2013) and increasing pressure on natural resources by land-use change, deforestation, fragmentation (Lewis, Malhi, & Phillips, 2004), and other stressors that increase vulnerability.

Climate change will impact both human and natural systems (IPCC, 2001; Thornton et al., 2014). Ecosystems might suffer effects such as migration and extinction of species, changes in biodiversity, species composition and phenology, and reduced growth rates (Bellard, Bertelsmeier, Leadley, Thuiller, & Courchamp, 2012; Clark & Clark, 2010; Colwell et al., 2008; Feeley, Wright, Supardi, Rahman, & Davie, 2007; IPCC, 2001; Thomas et al., 2004; Williams, Jacksn, & Kutzbach, 2007). Ecosystems can have high levels of vulnerability due to habitat decline, vegetation loss (Glick et al., 2011), or changes in their altitudinal gradients (Cuesta, Bustamante, Becerra, Postigo, & Peralvo, 2012; Gómez-Mendoza, Galicia, & Aguilar-Santelises, 2008; Marquet et al., 2010). Ecosystems with a limited altitudinal range, such as páramo, can be highly vulnerable to incremental climate changes, causing páramo species to migrate towards upper elevations in order to find better environmental conditions (Young, Young, & Josse, 2011); however, these type of ecosystems can have less possibilities to migrate because their altitudinal restrictions.

SRE is located along an altitudinal gradient between the coast and the Amazon region (0 m a.s.l. – 3,800 m a.s.l.) of Ecuador (Barthlott et al., 2007; Brummitt & Lughadha, 2003). Throughout this gradient we can find complex ecosystems with dry and wet characteristics over short distances (Beck, Bendix, Kottke, Makeschin, & Mosandl, 2008), creating an important biodiversity hotspot (Brehm et al., 2008; Myers, Mittermeier, Mittermeier, Fonseca, & Kent, 2000; Richter, Diertl, Emck, Peters, & Beck, 2009), as a result of a high speciation rate, Andean depression (Huancabamba), topography conditions, microclimatic influences, and human intervention (Barthlott et al., 2007; Keating, 2008; Richter & Moreira-Muñoz, 2005; Richter et al., 2009). Throughout the SRE are 45% of the 91 described ecosystems of Ecuador (MAE, 2013), within which there are 7048 species of flora (Lozano, 2002), and a high percentage of endemic plants (29%) (Lozano, Delgado, & Aguirre, 2003). El Oro has 228 endemic plant species, Loja has 639, and Zamora Chinchipe has 568 (Lozano, 2002).

The varied ecosystems in the SRE provide many greatly valued goods and services to communities in the region (MAE, 2001), but despite the environmental and social importance of these ecosystems, currently there is little scientific information on their vulnerability to climate change or other environmental effects. Environmental impacts and climate vulnerability differ from place to place and between ecosystems, due to their differing structural, topographical, and environmental characteristics.

A vulnerability assessment of ecosystems in the SRE is needed in order to estimate impacts on ecological integrity and function, as well as on human livelihoods; additionally, these type of studies help to set priorities for conservation actions. This article is a baseline on the SRE ecosystems most vulnerable to climate change and those more able to adapt to adverse climatic conditions. Our results will help to design and implement strategies for climate change adaptation (Fussel & Klein, 2007) and mitigation, which will improve conservation programs not only in the SRE, but also in other tropical ecosystems.

Methods

Study area

The research was conducted in the Southern Region of Ecuador (SRE), which has an area of 27,535 km2 (11% of Ecuador) (IGM, 2010). SRE includes El Oro with 600,659 inhabitants and a population growth rate of 1.5 %, Loja with 448,966 inhabitants and a population growth rate of 1.1 %, and Zamora Chinchipe with 91,376 inhabitants and a population growth rate of 2.0 % (INEC, 2010) (Figure 1). The average annual temperature ranges from 3℃ to 26℃, and the annual precipitation is between 37 mm to 6,000 mm (Herbario-Loja, 2001; INAMHI, 2013; Richter & Moreira-Muñoz, 2005). Changes in ecosystems over the past three decades in Ecuador and in the SRE are mainly due to land-use changes, human settlements, mining, roads, and deforestation (Sierra, 2013; Wasserstrom & Southgate, 2013). These are the principal drivers of change and have often seriously degraded ecological systems (Sierra, 2013; Tarras-Wahlberg, Flachier, Lane, & Sangfors, 2001; Wasserstrom & Southgate, 2013), impacting biodiversity (Hautier et al., 2015) and ecosystem services.

Figure 1.

Ecosystems of the Southern Region of Ecuador.

10.1177_1940082916668007-fig1.tif

Assessing the Vulnerability to Climate Change

Our assessment is based on the definition of vulnerability (vulnerability = exposure + sensitivity–adaptive capacity) by the Intergovernmental Panel on Climate Change (Cinner et al., 2012; Eigenbrod, Gonzalez, Dash, & Steyl, 2015; Füssel, 2010; Fussel & Klein, 2007; IPCC, 2001, 2007; Liu, Wang, Peng, Braimoh, & Yin, 2013). It included four components: i) values; ii) exposure; iii) sensitivity; and iv) adaptive capacity. The values assessed were the tropical ecosystems of southern Ecuador, due to their importance in providing goods and services to local and regional communities. Ecosystems were identified from information generated by the ecosystem classification system by the Ministry of Environment of Ecuador (MAE, 2013a, 2013b). This information was conglomerated in eight ecosystems based on the similarity of vegetation cover and seasonality. We evaluated the following biological systems: i) páramo (129,579 ha) (High Andean ecosystem distributed along the mountains above 3,000 m a.s.l. between closed forest and snow (Hofstede, Pool, & Mena, 2003)); ii) deciduous forest (138,990 ha); iii) semi-deciduous forest (482,164 ha); iv) western montane forest (138,951 ha); v) eastern montane forest (290,029 ha); vi) Amazon rainforest (463,259 ha); vii) Amazon semi-deciduous forest (9,663 ha); and viii) mangroves (23,026 ha). For each ecosystem we analyzed the exposure, sensitivity, adaptive capacity, and vulnerability to climate change (Figure 1).

Vulnerability is influenced by stressors that increase susceptibility (Cinner et al., 2012; Eigenbrod et al., 2015; Furniss et al., 2013; IPCC, 2001, 2007). We selected anthropogenic, natural or intrinsic, and climatic stressors as drivers of change in ecosystems. Management or conservation actions implemented at the SRE were considered to be buffers that could increase the adaptive capacity of ecosystems and reduce the effects of stressors (Dawson et al., 2011; Furniss et al., 2013; Fussel & Klein, 2007). We identified the main stressors of exposure and sensitivity as well as buffers for adaptive capacity through expert elicitation and three workshops (one per province) with the participation of researchers (universities and research centers from Ecuador), government representatives (Ministry of Environment, Ministry of Agriculture, Ministry of Water; and delegates of local governments from El Oro, Loja, and Zamora Chinchipe), community organizations, non-governmental organizations, and international experts (United States Forest Services and United States Agency for International Development). During this process around 40 experts provided information on what stressors or buffers have the most effect on ecosystems of the SRE. We based our final selection of the stressors and buffers on their importance and the available information. The variables used for this assessment are shown in Appendix 1.

Spatial analysis

Stressors and buffer variables were calculated using ArcGIS software, and the methodological process for each variable was based on its characteristics and the available information. Stressors and buffers were normalized on a scale of 0%–100% using normalization equations for categorical variables (Table 1, Eq. 4) or continuous variables (Table 1, Eq. 5). For each component (exposure, sensitivity, and adaptive capacity), we classified the level of influence of each stressor or buffer on ecosystems, establishing five categories (very low, low, moderate, high, and very high) through the method of natural breaks (Brewer & Pickle, 2002). For categorical stressors we assigned a weight based on the degree of impact on ecosystems, determined by workshops and the analytic hierarchy process developed by Saaty (1990, 2008).

Table 1.

Assessing vulnerability to climate change of ecosystems: equations for the region south of Ecuador.

10.1177_1940082916668007-table1.tif

Climatic exposure was calculated from eight general circulation models (GCMs) (BCC-CSM1-1, CCSM4, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MRI-CGCM3, NorESM1-M) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), and two climate change scenarios to 2050 (RCP 2.6 and RCP 8.5) from WorldClim platform with a spatial resolution of 1 km2. To reduce uncertainty associated with each model, we generated assemblies of scenarios of the climatic variables (IPCC, 2016), using a combination of a set of individual climate models. To this, we added eight models and took an average (Kharin & Zwiers, 2002; Knutti, Furrer, Tebaldi, Cermak, & Meehl, 2010) to reduce uncertainty and have a better representation than with individual models (Armenta, Dorado, Rodriguez, & Ruiz, 2014; Knutti et al., 2010; Lambert, & Boer, 2001). Assemblies were calculated for each of the climatic variables. Within the exposure assessment, we analyzed absolute changes in climatic variables (annual average temperature, annual average maximum and minimum temperature, and annual precipitation) between now and 2050 (Table 1, Eq. 1).

The total sensitivity (Table 1, Eq. 2) resulted from the analysis of the environmental, socio-economic and intrinsic sensitivity. Environmental stressors (land use, road density, deforestation, fragmentation and mining) were related to human activities that have caused changes in land cover, ecosystem structure and function. Socio-economic stressors (population density, population growth, basic needs, and water consumption) comprised factors like cultural and social conflicts, and living conditions that influence land use and natural resources. Finally, intrinsic stressors (mass movement, water deficit probability, flooding probability, and forest fires probability) were those with the potential to appear naturally within the ecosystem. The source of data for each stressor is shown in Appendix 2.

The stressors for each type of sensitivity (environmental, socio-economic, and intrinsic) were normalized and combined using map algebra. For categorical variables, we used a hierarchical process (Saaty, 1990, 2008) to weigh the importance of the relative effect of each of the categories of the variables; these weighs were established by experts during the workshops. This information was used to calculate the total sensitivity to both human and intrinsic stressors.

Buffers such as protected areas, conservation tools, and population decrease were used to determine adaptive capacity (Table 1, Eq. 3). Buffers, as categorical variables, were normalized through each internal category. We assigned a weight depending on the degree of contribution to the adaptive capacity of ecosystems. A hierarchical method (Saaty, 1990, 2008) and expert elicitation process were used for assigning weights to the categories (Appendix 3).

Finally, we produced maps of exposure, sensitivity, adaptive capacity, and vulnerability, with a resolution of 30 m × 30 m.

Results

Exposure

Both scenarios (RCP 2.6 and RCP 8.5) projected an increase in annual precipitation, primarily in the deciduous ecosystems and the Amazon rainforest. The greatest increases in annual average temperature, annual average maximum temperature, and annual average minimum temperature were in the Amazon basin, particularly in the eastern montane forest, Amazon rainforest, and Amazon semi-deciduous forest. In the RCP 2.6 scenario, exposures from the inter-Andean ecosystems to the coastal region were very low to moderate, while those located in the Amazon basin had moderate exposure (Figure 2). The high emissions scenario (RCP 8.5) showed the eastern montane forest and Amazon semi-deciduous forest with high exposure (53% and 80% of its area respectively), and the Amazon rainforest (88% of its area) had very high exposure (Figure 3, Appendix 4).

Figure 2.

Exposure to climate change under a RCP 2.6 scenario. (a) Mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig2.tif

Figure 3.

Exposure to climate change under a RCP 8.5 scenario. (a) Mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig3.tif

Exposure analysis trends reflected moderate to very high levels of exposure for both scenarios in the Amazon basin (Zamora Chinchipe), mainly in the Amazon rainforest, eastern montane forest, and Amazon semi-deciduous forest (Figures 2 and 3). However, the biggest absolute changes in climatic variables (annual precipitation, annual average temperature, annual average maximum temperature, and annual average minimum temperature) were for RCP 8.5. This suggests that climatic variables will have the greatest impact on ecosystems located in the eastern part of Ecuador.

Sensitivity

Ecosystems such as mangroves, deciduous forest, western montane forest, the Amazon rainforest, páramo, and eastern montane forest had mainly moderate to very low levels of sensitivity in large proportions of the territory, although the first four also have between 5% and 13% surface with high or very high sensitivity. Ecosystems that have a greater area in high sensitivity were the semi-deciduous forest and Amazon semi-deciduous forest, with 25% and 41% of their areas respectively (Figure 4, Appendix 5). These areas will be most affected in the future due to environmental stressors (land use, deforestation, mining, and road density) imposed historically by human pressure and degradation.

Figure 4.

Sensitivity (a) Mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig4.tif

Adaptive capacity

SRE had an overall low adaptive capacity, ranging from very low (34% of the area) to low (29% of the area). Ecosystems that had very low adaptive capacity were the mangroves (79% of its area), the semi-deciduous forest (40% of its area), and western montane forest (36% of its area) (Figure 5, Appendix 6). The deciduous forest had a moderate adaptive capacity in 50% of its territory. Ecosystems located in the Amazon basin, such as the Amazon rainforest and the Amazon semi-deciduous forest, despite including major conservation areas (protected areas or conservation programs), had large areas with low adaptive capacity (41% and 84% respectively).

Figure 5.

Adaptive capacity (a) Mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig5.tif

Páramo and the western montane forest have larger areas with very high levels of adaptive capacity (36% and 33% respectively). This is primarily because those ecosystems are located within protected areas (eg: Podocarpus National Park) and other conservation programs (e.g., Biosphere Reserve Podocarpus–The Condor). These conservation measures are a buffer against impacts of climate change and anthropogenic activities, reducing ecosystem vulnerability.

Vulnerability to climate change

SRE under RCP 2.6 had moderate vulnerability, a trend reflected in most ecosystems. According to the assessment, 94% of the Amazon semi-deciduous forest, 74% of semi-deciduous forest, 70% of the mangrove, and 62% of the Amazon rain forest have moderate vulnerability (Figure 6, Appendix 7). However, the deciduous forest (57%), páramo (35%), and eastern montane forest (58%) had low vulnerability. For the RCP 8.5 scenario, all ecosystems show high levels of vulnerability, because the changes in climatic variables are more extreme. Ecosystems with the largest areas of high vulnerability are the Amazon semi-deciduous forest (98% of its territory), semi-deciduous forest (85%), mangroves (74%), and Amazon rain forest (69%) (Figure 7).

Figure 6.

Vulnerability to climate change. RCP 2.6 scenario. (a) Mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig6.tif

Figure 7.

Vulnerability to climate change. RCP 8.5 scenario. (a) mangroves; (b) deciduous forest; (c) semi-deciduous forest; (d) western montane forest; (e) páramo; (f) eastern montane forest; (g) Amazon rainforest; (h) Amazon semi-deciduous forest.

10.1177_1940082916668007-fig7.tif

Discussion

By 2050, the annual average temperature would be expected to increase 1.46℃ in the RCP 2.6 scenario; and 2.37℃ in the RCP 8.5 scenario (Table 2). These changes could cause variations of current temperatures at specific locations and produce climatic conditions typical at lower altitudes (Peters et al., 2013). The Amazon basin had a high level of exposure, and ecosystems located between the Andes and the Amazon region would have high impacts. High levels of exposure could cause: changes in population dynamics, structure, and species composition, as well as migration, extinction or adaptation (Colwell et al., 2008; Dawson et al., 2011; McCarty, 2001; Pearson, 2006; Thomas et al., 2004; Thuiller et al., 2008). Distribution changes of tropical species could occur throughout the altitudinal gradient. In this case, lowland species will be more able to adapt to new climatic conditions along this gradient, but it will be difficult for species to populate these lowland areas, which would lead to biotic attrition (Colwell et al., 2008). On the other hand, páramo species may face increased isolation due to restricted geographical ranges, which could cause major extinctions of species that cannot adapt quickly (Tarras-Wahlberg et al., 2001; Wasserstrom & Southgate, 2013). Furthermore, some studies have estimated a change in the area of Andean biomes in the future. Ecosystems could face geographic expansion or reduction due to changes in environmental conditions, causing the extinction or migration of species (Anderson et al., 2011; Cuesta et al., 2012; Larsen et al., 2011).

Table 2.

Absolute changes in climatic variables of exposure scenarios RCP 2.6 and 8.5.

10.1177_1940082916668007-table2.tif

Other studies in tropical rain forests suggest impacts to growth rates of trees, negatively correlated with increases in annual average temperature, annual average maximum temperature, and intensity of the dry season (Clark, Clark, & Oberbauerz, 2010; Clark, Piper, Keeling, & Clark, 2003; Feeley et al., 2007). These effects may be reflected in the ecosystems of the SRE as coming decades bring strong increases in temperatures, potentially beyond thermal optima for plant growth. This will result in stress and reduced net primary production and growth (Lambers, Chapin, & Pons, 2008; Schuur, 2003), causing impacts on ecosystem structure (Clark, Clark, & Oberbauerz, 2010).

The effects of increasing climatic exposure may deepen for sensitive ecosystems with anthropogenic or intrinsic stressors. Anthropogenic stressors such as land-use change, open roads, and deforestation usually cause significant impacts on natural systems (Fischlin & Midgley, 2007; Hautier et al., 2015; Laurance, Goosem, & Laurance, 2009; Liu et al., 2008; Wasserstrom & Southgate, 2013) and may alter the way that our ecosystems respond to climate change (Burkett, Wilcox, Stottlemyer, Barrow, & Fagre, 2005).

Within the SRE, semi-deciduous forest had higher levels of sensitivity because of deforestation, land use, mining, and roads, which are the main drivers of change. Although in our study the deciduous and semi-deciduous forest had some areas with high sensitivity, they had better conservation than those located in northern Ecuador or northern Peru (Aguirre & Kvist, 2014). In addition, the Amazon rainforest and the Amazon semi-deciduous forest also show sensitivity to stressors such as the opening of roads (Freitas, Hawbaker, & Metzger, 2010; Laurance et al., 2009; Liu et al., 2008), which facilitate access to resources, colonization, and inevitable land-use changes, increasing forest loss in the Amazon basin (Wasserstrom & Southgate, 2013). Intrinsic stressors such as mass movement, water deficit, and wildfire also affect our ecosystems. The study area has an irregular surface with moderate slopes in the valleys and steeper slopes as it approaches the Andean mountains (Bendix et al., 2013). The region’s topography, geography, vegetation, and high rainfall, especially in the páramo, have impacts mainly on the western flanks of the Andes (Lozano, Busmann, Kupers, & Lozano, 2008).

Páramo and eastern montane forest ecosystems are better able to adapt to climate changes, mainly because much of the area is within protected areas. Additionally, the health of forests in southern Ecuador is better than those in the central and western region (Mena & Hofstede, 2006). Although the páramo has good adaptive capacity, we must not forget that these environments can be geographically isolated, making some species highly vulnerable in the future (Buytaert et al., 2011). On the other hand, there are ecosystems that have very low adaptive capacity, especially those located from the valleys to the coastal region (semi-deciduous forest, deciduous forest and mangrove), with clear and significant gaps in conservation. In addition to habitat loss, climate change will greatly increase the vulnerability of ecosystems (Eigenbrod et al., 2015). In this regard, the strengthening or the creation of conservation corridors could facilitate the connection between the lowlands and the Andes mountains, reducing the impacts of climate change on species (Killeen & Solórzano, 2008; Larsen et al., 2011).

We found that human activities are important drivers of change and climate vulnerability, and that certain strategies to adapt to climate change should be maintained and implemented. Conservation programs can reduce the degradation of natural resources and ecosystem services, as well as improve community development in the SRE. Although we used the RCP 2.6 and RCP 8.5 scenarios as optimistic and pessimistic respectively, is essential to realize that even if we improve strategies to address climate change (in the case of RCP 2.6), ecosystems will experience residual impacts. We therefore must address all environmental impact mechanisms and take steps to increase their adaptive capacity.

Implications for conservation

Assessment of vulnerability to climate change is a valuable tool to predict which ecosystems could be most affected by climate change and where major impacts may occur. Our results could be useful for policy makers in developing adaptation strategies and natural resource management plans, in order to improve ecosystems and species conservation. From the eight ecosystems assessed, we identified four ecosystems that continuously appeared with high levels of exposure and sensitivity as well as low adaptive capacity and could be prioritized for the development of conservation strategies; those ecosystems are: i) mangroves, ii) semi-deciduous forests, iii) Amazon semi-deciduous forest and iv) Amazon rainforest. Consequently, we will analyze in more detail which will be the conservation implications of our results with respect to these four systems.

In the case of mangroves, land use is a big threat, mainly due to the establishment of commercial shrimp farms that historically have been affecting rural livelihoods (Beitl, 2012; Hamilton & Lovette, 2015). Moreover, the lack of conservation strategies to reduce climate change impacts and human influences on mangroves has attracted the development of unsustainable activities. There is an urgent need to strengthen existing laws and land-use plans that require conservation and rehabilitation programs but are not currently enforced or implemented. The regulation of shrimp farms is a good start, but cannot keep up with the destruction of mangroves. It is necessary to reduce human intervention and facilitate mangrove regeneration, but these actions must include productive alternatives for local people whose livelihoods are based on the mangrove.

In semi-deciduous forests, widespread throughout El Oro and Loja provinces, fragmentation and deforestation are the biggest problems. These forests need biological corridors connecting protected areas, biosphere reserves, Ramsar areas, agroforestry systems dominated by pastures or monocultures (like the central region of El Oro and the central and western part of Loja), and restoration projects in areas such as the western part of El Oro and south-western part of Loja, where there is great demand for water.

Amazon semi-deciduous forests comprise a small portion of the SRE in southern Zamora Chinchipe and extending toward the northern part of Perú. This ecosystem is highly affected by environmental stressors, primarily mining concessions, road construction, fragmentation, and deforestation. This ecosystem is outside important conservation areas, such as Podocarpus National Park or Yacuri National Park, where anthropogenic pressures have been reduced. Biological corridors between such conservation areas and the remnants of Amazon semi-deciduous forests could improve these forests’ adaptive capacity.

The Amazon rainforest concentrated in Zamora Chinchipe, suffers the same stressors as the Amazon semi-deciduous forest. Because of the rainforest’s potential for carbon storage and its role in climate change mitigation, conservation strategies should be focused on increasing forest cover through restoration projects that will contribute to carbon sequestration.

Acknowledgements

We thank Universidad Nacional de Loja, the United States Forest Service for the scientific advisor; and the United States Agency for International Development for the financial support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

1.

Aguirre, Z., Kvist, L., (2014) Compisición florística y estado de conservación de los bosques secos del sur-occidente del Ecuador. Lyonia 8: 41–67. Google Scholar

2.

Anderson, E., Marengo, J., Villalba, R., Halloy, S., Young, B., Cordero, D., Ruiz, D., (2011) Consequences of climate change for ecosystems and ecosystem services in the tropical Andes. In: Herzog, S., Martinez, R., Jorgensen, P., Tiessen, H., (eds) Climate change and biodiversity in tropical Andes MacArthur Foundation, Paris, France: Inter-American Institute for Global Change Research (IAI), Scientific Committee on Problems of the Environment (SCOPE), pp. 1–5. Google Scholar

3.

Armenta, G., Dorado, J., Rodriguez, A., Ruiz, J., (2014) Escenarios de Cambio Climático para Precipitación y Temperaturas en Colombia, Bogotá, Colombia: Instituto de Hidrología, Meteorología y Estudios Ambientales de Colombia IDEAM, pp. 274. Google Scholar

4.

Barthlott, W., Hostert, A., Kuper, W., Kreft, H., Mutke, J., Rafiqpoor, D., Henning, J., (2007) Geographic patterns of vascular plant diversity at continental to global scales. Erdkunde 61: 305–315. Google Scholar

5.

(2008) Gradients in a tropical mountain ecosystem of Ecuador. In: Beck, E., Bendix, J., Kottke, I., Makeschin, F., Mosandl, R., (eds) Springer Science & Business Media 198: 525. Google Scholar

6.

Beitl, C., (2012) Shifting policies, access, and the tragedy of enclosures in Ecuadorian mangrove fisheries: Towards a political ecology of the commons. Journal of Political Ecology 19: 94–113. Google Scholar

7.

Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., Courchamp, F., (2012) Impacts of climate change on the future of biodiversity. Ecology Letters 15: 365–377. Google Scholar

8.

Bendix, J., Dislich, C., Huth, A., Huwe, B., Ließ, M., Schroder, B., Wilcke, W., (2013) Natural landslides which impact current regulating services: Environmental preconditions and modeling. In: Bendix, J., Beck, E., Brauning, A., Makeschin, F., Mosandl, R., Scheu, S., Wilcke, S., (eds) Ecosystem services, biodiversity and environmental change in a tropical mountain ecosystem of south Ecuador ecological studies Vol. 221, Berlin, Germany: Springer, pp. 153–170. Google Scholar

9.

Brehm, G., Homeier, K., Fiedler, K., Kottke, I., Illig, J., Nöske, N., Breckle, S., (2008) Mountain rain forests in southern Ecuador as a hotspot of biodiversity–limited knowledge and diverging patterns. In: Beck, E., Bendix, J., Kottke, I., Makeschin, F., Mosandl, R., (eds) Gradients in a tropical mountain ecosystem of Ecuador, Berlin, Germany: Springer, pp. 15–23. Google Scholar

10.

Brewer, C., Pickle, L., (2002) Evaluation of methods for classifying epidemiological data on Choropleth maps in series. Annals of the Association of American Geographers 92: 662–681. Google Scholar

11.

Brummitt, N., Lughadha, E., (2003) Biodiversity: whereá hot and where's not. Conservation Biology 17: 1442–1448. Google Scholar

12.

Burkett, V., Wilcox, D., Stottlemyer, R., Barrow, W., Fagre, D., (2005) Nonlinear dynamics in ecosystem response to climatic change: Case studies and policy implications. Ecological Complexity 2: 357–394. Google Scholar

13.

Buytaert, W., Cuesta-Camacho, F., Tobón, C., (2011) Potential impacts of climate change on the environmental services of humid tropical alpine regions. Global Ecology and Biogeography 20: 19–33. Google Scholar

14.

Cinner, J., McClanahan, T., Graham, N., Daw, T., Maina, J, Stead, S, Bodin, O., (2012) Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change 22: 12–20. Google Scholar

15.

Clark, D., Clark, D., (2010) Assessing tropical forests’ climatic sensitivities with long-term data. Biotropica 43: 31–40. Google Scholar

16.

Clark, D., Clark, D., Oberbauerz, S., (2010) Annual wood production in a tropical rain forest in NE Costa Rica linked to climatic variation but not to increasing CO2. Global Change Biology 16: 747–759. Google Scholar

17.

Clark, D., Piper, S., Keeling, C., Clark, D., (2003) Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. PNAS 100: 5851–5857. Google Scholar

18.

Colwell, R., Brehm, G., Cardelús, C., Gilman, A., John, L., (2008) Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322: 258–261. Google Scholar

19.

Cuesta, F., Baez, S., Ramirez, J., Tovar, C., Devenish, C., Buytaert, W., Jarvis, A., (2012) Sintesis de los impactos y estado de conocimiento de los efectos del cambio climático en la biodiversidad de los andes tropicales. In: Cuesta, F., Becerra, M., Postigo, J., Peralvo, M., (eds) Panorama andino sobre cambio climático Vulnerabilidad y adaptación en los andes tropicales, Lima, Peru: CONDESAN SG-CAN, pp. 109–146. Google Scholar

20.

Cuesta, F., Bustamante, M., Becerra, M., Postigo, J., Peralvo, J., (2012) Introducción: cambio climático y los andes tropicales. In: Cuesta, F., Bustamante, M., Becerra, M., Postigo, J., Peralvo, J., (eds) Panorama andino sobre cambio climático: Vulnerabilidad y adaptación en los Andes Tropicales, Lima, Peru: CONDESAN SG-CAN, pp. 13–19. Google Scholar

21.

Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C., Mace, G. M., (2011) Beyond predictions: Biodiversity conservation in a changing climate. Science 332: 53–58. Google Scholar

22.

Eigenbrod, F., Gonzalez, P., Dash, J., Steyl, I., (2015) Vulnerability of ecosystems to climate change moderated by habitat intactness. Global Change Biology 21: 275–286. Google Scholar

23.

Feeley, K., Wright, J., Supardi, N., Rahman, A., Davie, S., (2007) Decelerating growth in tropical forest trees. Ecology Letters 10: 461–469. Google Scholar

24.

Fischlin, A., & Midgley, G. (2007). Ecosystems, their properties, goods and services. Climate Change 2007: Impacts, adaptation and vulnerability. contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, England: Cambridge University Press. Google Scholar

25.

Foster, P., (2001) The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Science Reviews 55: 73–106. Google Scholar

26.

Freitas, S., Hawbaker, T., Metzger, J., (2010) Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest. Forest Ecology and Management 259: 410–417. Google Scholar

27.

Furniss, M., Roby, K., Cenderelli, D., Chatel, J., Clifton, C., Clingenpeel, A., Weinhold, M., (2013) Assessing the vulnerability of watersheds to climate change results of national forest watershed vulnerability pilot assessments, Washington, DC: USDA Forest Service. Google Scholar

28.

Füssel, H., (2010) Review and quantitative analysis of indices of climate change exposure, adaptive capacity, sensitivity, and impacts. World Development Report 34. Washington, DC: World Bank. Google Scholar

29.

Fussel, H., Klein, R., (2007) Climate change vulnerability assessments: an evolution of conceptual thinking. Climatic Change 75: 301–329. Google Scholar

30.

Glick, P., Stein, B., Edelson, N., (2011) Scanning the conservation horizon: A guide to climate change vulnerability assessment, Reston, VA: National Wildlife Federation. Google Scholar

31.

Gómez-Mendoza, L., Galicia, L., Aguilar-Santelises, R., (2008) Sensibilidad de grupos funcionales al cambio climático en la Sierra Norte de Oaxaca, México. Investigaciones Geográficas, Boletín del Instituto de Geografía. UNAM 67: 76–100. Google Scholar

32.

Hamilton, S., Lovette, J., (2015) Ecuador’s mangrove forest carbon stocks: A spatiotemporal analysis of living carbon holdings and their depletion since the advent of commercial aquaculture. PLoS ONE 10: 3–14. Google Scholar

33.

Hautier, Y., Tilman, D., Isbell, F., Seabloom, E., Borer, E., Reich, P., Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348: 336–340. Google Scholar

34.

Herbario, L., (2001) Zonificación y determinación de los tipos de bosque seco en el suroccidente de la provincia de Loja, Informe Final. Google Scholar

35.

Hofstede, R., Pool, S., Mena, P., (2003) Los paramos del Mundo. Proyeto Atlas Mundial de Los Páramos, Quito, Ecuador: Global Peatland initiative/NC-IUCN/EcoCiencia. Google Scholar

36.

IGM (2010) Atlas Geográfico de la Republica del Ecuador, Quito, Ecuador: Instituto Geográfico Militar. Google Scholar

37.

INAMHI (2013) Anuario meteorológico 2011 № 51, Quito, Ecuador: Instituto Nacional de Meteorologia e Hidrologia. Google Scholar

38.

INEC (2010) Censo de población y vivienda, San José, Costa Rica: Instituto Nacional de Estadísticas y Censos. Google Scholar

39.

IPCC (2001) Impactos, adaptación y vulnerabilidad. Parte de la contribución del Grupo de trabajo II al Tercer Informe de Evaluación Grupo Intergubernamental de Expertos sobre el Cambio Climático, Ginebra, Suiza: Grupo Intergubernamental de Expertos sobre el Cambio Climático. Google Scholar

40.

IPCC (2007) Climate change 2007: The physical science basis, Cambridge, England: Cambridge University Press. Google Scholar

41.

IPCC (2014) Summary for policymakers. In: Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., White, L. L., (eds) Climate change: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change, New York, NY: Cambridge University Press, pp. 1–32. Google Scholar

42.

IPCC (2016) 2016. Climate change 2013: The physical science basis. In Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change, Bern, Switzerland, , 29 January. Google Scholar

43.

Jiang, L., Hardee, K., (2011) How do recent population trends matter to climate change? Population Research and Policy Review 30: 287–312. Google Scholar

44.

Keating, P., (2008) The floristic composition and biogeographical significance of a megadiverse paramo site in the southern Ecuadorian Andes. Journal of the Torrey Botanical Society 135: 554–570. Google Scholar

45.

Kharin, V., Zwiers, F., (2002) Climate predictions with multimodel ensembles. Journal of Climate 15: 793–799. Google Scholar

46.

Killeen, T., Solórzano, L., (2008) Conservation strategies to mitigate impacts from climate change in Amazonia. Philosophical Transactions of the Royal Society B: Biological Sciences 363: 1881–1888. Google Scholar

47.

Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., Meehl, G., (2010) Challenges in combining projections from multiple climate models. Journal of Climate 23: 2739–2758. Google Scholar

48.

Lambert, S., Boer, G., (2001) CMIP1 evaluation and intercomparison of coupled climate models. Climate Dynamics 17: 83–106. Google Scholar

49.

Lambers, H., Chapin, S., Pons, T., (2008) Plant physiological ecology, 2nd ed. New York, NY: Springer. Google Scholar

50.

Larsen, T., Brehm, G., Navarrete, H., Franco, P., Gomez, H., Mena, J., Canhos, V., (2011) Range shifts and extinctions driven by climate change in the Tropical Andes: Synthesis and directions. Climate change and biodiversity in tropical Andes MacArthur Foundation, Paris, France: Inter-American Institute for Global Change Research (IAI), Scientific Committee on Problems of the Environment (SCOPE), pp. 47–67. Google Scholar

51.

Laurance, W., Goosem, M., Laurance, S., (2009) Impacts of roads and linear clearings on tropical forests. Trends in Ecology & Evolution 24: 1–10. Google Scholar

52.

Lewis, S. L., Malhi, Y., Phillips, O., (2004) Fingerprinting the impacts of global change on tropical forests. Perspectives in Plant Ecology, Evolution and Systematics 359: 467–462. Google Scholar

53.

Liu, X., Wang, Y., Peng, J., Braimoh, A., Yin, H., (2013) Assessing vulnerability to drought based on exposure, sensitivity and adaptive capacity: A case study in middle Inner Mongolia of China. Chinese Geographical Science 23: 13–25. Google Scholar

54.

Liu, S., Cui, B., Dong, S., Yang, Z., Yang, M., Holt, K., (2008) Evaluating the influence of road networks on landscape and regional ecological risk—A case study in Lancang River Valley of Southwest China. Ecological Engineering 34: 91–99. Google Scholar

55.

Lozano, P., (2002) Los tipos de bosque del sur del Ecuador. In: Aguirre, Z., Madsen, M., Cotton, E., Balslev, H., (eds) Botánica Austroecuatoriana: Estudios sobre los recursos vegetales en las provincias de El Oro, Loja, Ecuador: Loja y Zamora-Chinchipe, pp. 29–49. Google Scholar

56.

Lozano, P., Busmann, R., Kupers, M., Lozano, D., (2008) Deslizamientos naturales y comunidades pionera de ecosistemas montañosos al occidente del Parque Nacional Podocarpus (Ecuador). Botánica 30: 1–19. Google Scholar

57.

Lozano, P., Delgado, T., Aguirre, Z., (2003) Estado actual de la flora endémica exclusiva y su distribución en el Occidente del Parque Nacional Podocarpus, Loja, Ecuador: Publicaciones de la Fundación Ecuatoriana para la Investigación y Desarrollo de la Botánica. Google Scholar

58.

McCarty, J., (2001) Ecological consequences of recent climate change. Conservation Biology 15: 320–331. Google Scholar

59.

MAE (2001) La biodiversidad del Ecuador. Informe 2000, Quito, Ecuador: Ministerio del Ambiente del Ecuador. EcoCiencia. Unión Mundial para la Naturaleza. Google Scholar

60.

MAE (2013a) Mapa de Ecosistemas del Ecuador Continental, Quito, Ecuador: MInisterio del Ambiente del Ecuador. Google Scholar

61.

MAE (2013b) Metodología para la Representación Cartográfica de los Ecosistemas del Ecuador Continental, Quito, Ecuador: Subsecretaría de Patrimonio Natural. Ministerio del Ambiente del Ecuador, pp. 121. Google Scholar

62.

Marquet, P., Abades, S., Armesto, J., Barria, I., Arroyo, M., Cavieres, L., Vicuña, S., (2010) Estudio de vulnerabilidad de la biodiversidad terrestre en la eco-región mediterránea, a nivel de ecosistemas y especies, y medidas de adaptación frente a escenarios de cambio climático. (p.153), Santiago, Chile: Institute of Ecology and Biodiversity. Google Scholar

63.

Mena, P., Hofstede, R., (2006) Los páramos ecuatorianos. In: Moraes, M., Øllgaard, B., Kvist, L., Borchsenius, F., Balslev, H., (eds) Botánica Económica de los Andes Centrales, La Paz, Bolivia: Mayor de San Andrés, pp. 91–109. Google Scholar

64.

Myers, N., Mittermeier, R., Mittermeier, C., Fonseca, G., Kent, J., (2000) Biodiversity hotspots for conservation priorities. Nature 403: 853–858. Google Scholar

65.

Nagendra, H., Sudhira, H., Katti, M., Schewenius, M., (2013) Sub-regional assessment of India: Effects of urbanization on land use, biodiversity and ecosystem services. Urbanization, biodiversity and ecosystem services: Challenges and opportunities, Dordrecht, Netherlands: Springer, pp. 65–74. Google Scholar

66.

Pearson, R., (2006) Climate change and the migration capacity of species. Trends Ecology and Evolution 21: 111–113. Google Scholar

67.

Peters, T., Drobnik, T., Meyer, H., Rankl, M., Richter, M., Rollenbeck, R., Bendix, J., (2013) Environmental changes affecting the Andes of Ecuador. In: Bendix, J., Beck, E., Brauning, A., Makeschin, F., Mosandl, R., Scheu, S., Wilcke, S., (eds) Ecosystem services, biodiversity and environmental change in a tropical mountain ecosystem of South Ecuador, Berlin, Germany: Springer, pp. 19–30. Google Scholar

68.

Richter, M., Moreira-Muñoz, A., (2005) Heterogeneidad climática y diversidad de la vegetación en el sur de Ecuador: un método de fitoindicación. Revista Peruana de Biología 12: 217–238. Google Scholar

69.

Richter, M., Diertl, K., Emck, P., Peters, T., Beck, E., (2009) Reasons for an outstanding plant diversity in the tropical Andes of Southern Ecuador. Landscape Online 12: 1–35. Google Scholar

70.

Saaty, T., (1990) How to make a decision: The analytic hierarchy process. European Journal of Operational Research 48: 9–26. Google Scholar

71.

Saaty, T., (2008) Decision making with the analytic hierarchy process. International Journal of Services Sciences 1: 83–98. Google Scholar

72.

Schuur, E., (2003) Productivity and global climate revisited: the sensitivity of tropical forest growth to precipitation. Ecology 84: 1165–1170. Google Scholar

73.

Sierra, R., (2013) Patrones y factores de deforestación en el Ecuador continental, 1990–2010. Y un acercamiento a los próximos 10 años, Quito, Ecuador: Conservación Internacional Ecuador y Forest Trends. Google Scholar

74.

Tarras-Wahlberg, N., Flachier, A., Lane, S., Sangfors, O., (2001) Environmental impacts and metal exposure of aquatic ecosystems in rivers contaminated by small scale gold mining: The Puyango River basin, southern Ecuador. The Science of the Total Environment 278: 239–261. Google Scholar

75.

Thomas, C., Cameron, A., Green, R., Bakkenes, M., Beaumont, L., Collingham, Y., Williams, S., (2004) Extinction risk from climate change. Nature 427: 145–148. Google Scholar

76.

Thornton, P., Ericksen, P., Herrero, M., Challinor, A., (2014) Climate variability and vulnerability to climate change: a review. Global Change Biology 20: 3313–3328. Google Scholar

77.

Thuiller, W., Albert, C., Araujo, M., Berry, P., Cabeza, M., Guisan, A., Zimmermann, N., (2008) Predicting global change impacts on plant species’ distributions: Future challenges. Perspectives in Plant Ecology, Evolution and Systematic 9: 137–152. Google Scholar

78.

Wasserstrom, R., Southgate, D., (2013) Deforestation, agrarian reform and oil development in ecuador, 1964-1994. Natural Resources 04: 31–44. Google Scholar

79.

Wheeler, T., Von-Braun, J., (2013) Climate change impacts on global food security. Science 341: 508–513. Google Scholar

80.

Williams, J., Jacksn, W., Kutzbach, J., (2007) Projected distributions of novel and disappearing climates. PNAS 104: 5738–5742. Google Scholar

81.

Young, B., Young, K., & Josse, C. (2011). Vulnerability of Tropical Andean ecosystems to climate change. Climate change and biodiversity in tropical Andes MacArthur Foundation (pp. 170–181). Paris, France: Inter-American Institute for Global Change Research (IAI), Scientific Committee on Problems of the Environment (SCOPE). Google Scholar

Appendices

Appendix 1.

Stressor and buffers variables used for the vulnerability assessment

10.1177_1940082916668007-img8.tif

Appendix 2.

Source of data of each stressor and buffers.

10.1177_1940082916668007-table3.tif

Appendix 3.

Weights for adaptive capacity buffers.

10.1177_1940082916668007-table4.tif

Appendix 4.

Exposure results.

10.1177_1940082916668007-table5.tif

Appendix 5.

Sensitivity results.

10.1177_1940082916668007-table6.tif

Appendix 6.

Adaptive Capacity results.

10.1177_1940082916668007-table7.tif

Appendix 7.

Vulnerability to climate change results.

10.1177_1940082916668007-table8.tif
© The Author(s) 2016 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Received: 6 January 2016; Accepted: 18 July 2016; Published: 1 January 2020
JOURNAL ARTICLE
PAGES


SHARE
ARTICLE IMPACT
Back to Top