Projected Distribution Shifts and Protected Area Coverage of Range

Global Ecology and Conservation 4 (2015) 459–469

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Global Ecology and Conservation

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Original research article Projected distribution shifts and protected area coverage of range-restricted Andean birds under climate change Verónica del Rosario Avalos ∗, Jaime Hernández Laboratory of Geomatic and Landscape Ecology, Faculty of Forestry and Conservation, University of Chile, Santa Rosa 11315, La Pintana, Santiago de Chile, Chile article info a b s t r a c t

Article history: In this study we projected the effect of anthropogenic climate change in endemic and Received 31 October 2014 restricted-range Andean bird species that spread out from the center of Bolivia to south- Received in revised form 23 July 2015 eastern Peru. We also analyzed the representation of these species in protected areas. The Accepted 20 August 2015 ensemble forecasts from niche-based models indicated that 91–100% of species may reduce their range size under full and no dispersal scenarios, including five species that are currently threatened. The large range reduction (average 63%) suggests these moun- Keywords: tain species may be threatened by climate change. The strong effects due to range species Conservation Gap analysis losses are predicted in the humid mountain forests of Bolivia. The representation of bird Protected areas species also decreased in protected areas. Partial gap species (94–86%) are expected to Range size increase over the present (62%). This suggests climate change and other non-climate Threatened species stressors should be incorporated in conservations plans for the long-term persistence of these species. This study anticipates the magnitude of shifts in the distribution of endemic birds, and represents in the study area the first exploration of the representation of range- restricted Andean birds in protected areas under climate change. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Anthropogenic climate change is recognized as a serious threat to biodiversity (IPCC, 2007). Changes in the geographic distribution of species, decreases in species population sizes, changes in ecological interactions (Williams et al., 2007; Auer and Martin, 2013) and the extinction of various species are some of the predicted impacts of climate change (Thomas et al., 2004; Colwell et al., 2008). Modeling studies in particular of global studies predicted that climate change may be an important extinction driver in the tropics. In Latin America, the regions predicted to have the greatest changes in fauna include the tundra, Central America and the Andes (Lawler et al., 2009). In the Andes specifically a high extinction rates of plants is predicted (Malcolm et al., 2006) as well as a large number of vulnerable birds and amphibians (Foden et al., 2013). These assessments in most cases are attempts to lead management strategies that can help species survive in the future (Pacifici et al., 2015). Tropical species and those that inhabit mountain regions are predicted to make altitudinal rather than latitudinal shifts (Colwell et al., 2008; La Sorte and Jetz, 2010; Buermann et al., 2011), which is consistent with recent evidence of species surveys (Peh, 2007; Feeley et al., 2011; Harris et al., 2012; Freeman and Class Freeman, 2014). Highland species may be very vulnerable to extinction induced by climate change (Şekercioğlu et al., 2008; Harris et al., 2014). The warming temperatures may force the mountain species to shift upslope, reducing their geographic ranges almost entirely, decreasing

∗ Correspondence to: Colección Boliviana de Fauna, Instituto de Ecología, Campus Universitario: Calle 27 Cota-Cota, La Paz, Bolivia. Tel.: +591 77247228. E-mail addresses: [email protected] (V. del R. Avalos), [email protected] (J. Hernández). http://dx.doi.org/10.1016/j.gecco.2015.08.004 2351-9894/© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 460 V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 their population, and driving them to extinction (Shoo et al., 2005; Harris et al., 2014). Restricted-range and endemic species are also projected to be vulnerable to climate change (Malcolm et al., 2006; IPCC, 2007; Williams et al., 2007; Ohlemüller et al., 2008; Şekercioğlu et al., 2008; Harris et al., 2014). In the northern Andes, about 43% of bird species may reduce their geographic range and some species may become extinct due to climate change (Velásquez-Tibatá et al., 2012). In Costa Rica, the populations of nearly half of 77 forest bird species may decline, and seven of the eight species projected to become locally extinct are endemic to Costa Rica and Panamá (Gasner et al., 2010). The populations of endemic high-elevation birds in Indonesia are also projected to decline (Harris et al., 2014). Life history traits and dispersal ability among others factors could have a great influence determining current and future species ranges as well as their vulnerability to climate change (Bellard et al., 2012; Rehm, 2014; Pacifici et al., 2015). However, considering changes in land use are increasing in the tropics, the urban and crop areas and other management areas may negatively affect or restrict the range shifts (Jetz et al., 2007; Feeley and Silman, 2010; Şekercioğlu et al., 2012). Various endemic and restricted-range bird species inhabit the Bolivian and Peruvian Andes (Stattersfield et al., 1998) and most are little known. For example, in the humid mountain forests known locally as ‘‘Yungas’’ are 35 endemic birds, six of which are globally threatened (Stattersfield et al., 1998). The highest percentage of endemic birds is found at the highest elevations (Ibisch and Mérida, 2003). This region was historically isolated from the lowlands by the uplift of the Andes, creating a complex structure of high mountains and inter-Andean valleys (Ibisch and Mérida, 2003). The species of the Andes may be relict populations that survived global change periods in places where the climate was moderate or stable (Fjeldså et al., 1999). Although these species are of interest for conservation, currently most are threatened by habitat loss, and the occurrences of most species in protected areas in Bolivia and Peru is limited (Hennessey et al., 2003). In fact, critical areas of bird endemism may be only partially covered in these protected areas (Young et al., 2009). These protected areas are also at risk, facing threats that include but are not limited to deforestation (Killeen et al., 2007), roads, mining activities, fires and hydrocarbon projects (Ibisch et al., 2007; SERNANP, 2007). Moreover, climate change adds an additional relevant threat in protected areas and their management. For example, some studies have reported species may move outside of the protected areas (Araújo et al., 2004; Hannah et al., 2005) so their effectiveness in securing species under rapid climate change is uncertain (Araújo et al., 2004). Conserving species in Bolivia and Peru is currently a challenging task, but under climate change may require new management plans and adaptive strategies that should be included in each country’s plans. It is considered that more detailed studies of these species under climate change are needed for a better understanding of the status of the birds and their long-term conservation. In this study we projected the changes in geographic distribution in restricted-range birds of the Andes found in Bolivia and southeastern Peru by using two climate change scenarios and two dispersal scenarios for 2080. We also identified gap species in the current protected areas and propose recommendations for their conservation.

2. Methods

2.1. Study area

The study area is located on the eastern slope of the Andes; it extends from Cusco in Peru to the center of Bolivia (between 73° 5′W and 19° 7′S). The Yungas ecoregion is located in this sector of the Andes between 500 and 4000 m elevation (Olson et al., 2001; Josse et al., 2009). The most important vegetation types for bird species are: (1) the Yungas, humid cloud forests (1000–4200 m) with annual precipitation between 1500 and 6000 mm and temperature of 7–24 °C(Ibisch and Mérida, 2003); (2) the sub-Andean Amazonian forests (500–1000 m), a transition area between Andean and Amazonian species with annual precipitation between 1500–7000 mm and temperature of 24–26 °C(Ibisch and Mérida, 2003); and (3) the inter-Andean dry forests and transition Yungas to Puna (1700–4000 m), located on the east flanks of Yungas (Ibisch and Mérida, 2003; Hennessey et al., 2003). These areas in Bolivia are in La Paz, Cochabamba and part of the Departments of Santa Cruz and Beni. In Peru these areas are in Puno, Cusco and part of the Madre de Dios department.

2.2. Study species

We studied 21 species of endemic birds distributed from the center of Bolivia to southeastern Peru. These birds have geographic ranges of less than 50 000 km2 (Stattersfield et al., 1998) and most have more than 80% of their range in Bolivia (see maps of Ridgely and Tudor, 2009 and Swenson et al., 2012). These species are also found in endemic bird areas (EBA) suggested for conservation (Stattersfield et al., 1998). In the Yungas forests (Bolivian and Peruvian upper Yungas, EBA 055) are the high-elevation birds (range extent below 4000 m elevation, see Appendix A): Black-winged Parrot Hapalopsittaca melanotis melanotis, Hooded Mountain-Toucan Andigena cucullata, Light-crowned Spinetail Cranioleuca albiceps, Diademed Tapaculo Scytalopus schulenbergi, Orange- browed Hemispingus Hemispingus calophrys, Yungas Tody-Tyrant Hemitriccus spodiops, and Scimitar-winged Piha Lipaugus uropygialis (ranked as Vulnerable) (IUCN, 2014). The endemic birds of Bolivia include the Black-throated Thistletail Asthenes harterti, Black-hooded Sunbeam Aglaeactis pamela, Rufous-faced Antpitta Grallaria erythrotis and Bolivian Brushfinch Atlapetes rufinucha. In the sub-Andean Amazonian forests (Bolivian and Peruvian lower Yungas, EBA 054) are the middle-elevation birds (referred to here as species whose range extent is below 2500 m elevation): Bolivian Recurvebill Syndactyla striata, V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 461

Ashy Antwren Myrmotherula grisea, Upland Antshrike Thamnophilus aroyae and Hazel-fronted Pygmy-Tyrant Pseudotriccus simplex. The endemics Yellow-rumped Antwren Euchrepomis sharpei and Horned Curassow Pauxi unicornis are ranked as Endangered. The endemic Bolivian Swallow tailed-Cotinga Phibalura flavirostris boliviana (ranked as Endangered) and Green- capped Tanager Tangara meyerdeschauenseei (ranked as Near Threatened) would likely be in this EBA. In the inter-Andean dry forests (High Andes of Bolivia and Argentina, EBA 056) are the endemic Berlepsch’s Canastero Asthenes berlepschi (ranked as Near Threatened) and likely be the endemic Bolivian Spinetail Cranioleuca henricae (ranked as Endangered).

2.3. Data sources

The occurrence data for bird species included all observations of species recorded in Bolivia and Peru. These belong to the following databases: Colección Boliviana de Fauna, the online pages Gbif, Ebird, Xeno-canto, publications and unpublished reports (see Appendix B and ‘‘Acknowledgments’’). The geographic locations of each species were verified and corrected according to their habitat and elevation in the bibliographic review with the aid of Google Earth. We corrected the records in the vicinity of 1 km or less and excluded those with geographic errors (i.e. those occurring outside the current distribution). To perform the distribution modeling for a given species we included only those records with a high confidence of the taxonomic identification according to data source, record type, habitat and those that were at least 1 km distant from other records (Records are available in Appendix A). The environmental variables used for the modeling prediction were downloaded from different sources. The current bioclimatic data belong to Worldclim (http://www.worldclim.org), which is a set of 19 climatic variables that have a resolution of 1 km2. The future climate variables for the 2080 slice years (2080–2099) were statistically downloaded from the CIAT-GCM (http://www.ccafs-climate.org/), which also have 1 km2 of resolution. We used two climate models (Ukmo- HadCM3 and Csiro-Mk2) and two climate change scenarios (A2 and A1b) from the fourth evaluation report of the IPCC (Ar4) for the modeling. The two scenarios assume regionally oriented economic growth, with population and economic growth being higher in A2 than A1b. The topographic data of slope and altitude for the digital elevation model were from the Shuttle Radar Topography Mission (http://www2.jpl.nasa.gov/srtm/). In the platform R version 3.02 (R Development Core Team, 2012), we selected only nine climatic variables that were not collinear with each other (pairwise Pearson r < 0.8): mean annual temperature, mean diurnal range in temperature, isothermality (P2/P7), temperature seasonality (standard deviation*100), mean annual precipitation, precipitation of wettest month, precipitation seasonality (coefficient of variation), precipitation of wettest quarter and precipitation of driest quarter.

2.4. Distribution modeling

Different modeling techniques calibrated on the same species can produce different prediction values (Thuiller et al., 2009 and Araújo et al., 2005). BIOMOD allows the calculation of an ensemble prediction of all algorithms, reducing the uncertainties arising from using only a single algorithm (Thuiller et al., 2009). The modeling of geographic distribution or niche modeling of each species in the current and future scenarios was performed with the ensemble BIOMOD2 v.3.1-23 (Thuiller et al., 2013) implemented in R version 3.02 (R Development Core Team, 2012). BIOMOD2 includes the projections from 10 statistical models: general linear models (GLM), general additive models (GAM), classification tree analysis (CTA), artificial neural networks (ANN), BIOCLIM (SRE), flexible discriminant analysis (FDA), multivariate adaptive regression splines (MARS), random forest (RF), general boosted method (GBM) and the maximum entropy algorithm (MAXENT). The majority of modeling techniques require data about presences and absences, thus we used 1000 virtual absences (pseudo- absences) to model distribution throughout the study area. Following recommendations (Wisz and Guisan, 2009), this was done randomly in BIOMOD. A random sample of 70% of the occurrence data was used to calibrate the model and 30% was used for the evaluation of the model. The models were evaluated by calculating the area under the curve (AUC) of the receiver operating characteristics curve (Fielding and Bell, 1997) and the true skill statistics (TSS) (Allouche et al., 2006). Values of AUC of 0.5–0.7 usually have a low confidence, values of 0.7–0.9 have useful application in the model and values >0.9 have high confidence. TSS varies from −1 to +1 where +1 is a perfect prediction and values equal to or less than zero are a prediction no better than random (Allouche et al., 2006). For the final ensemble of the modeling for each species we included models using two criteria: those models with TSS > 0.7 and those whose distributions maps were consistent with known information. This approach was used to increase the performance projections under the assumption that it is better to use the best models than more models (Araújo et al., 2005). In order to transform the results of species distribution modeling from habitat suitability to presence/absence distribution, we transformed the projected probabilities into binary predictions using the TSS for optimizing thresholds for splitting. However, the selection of appropriate thresholds for models generated with small sample sizes is difficult (Hernandez et al., 2006). In most cases (70%) we followed a procedure by Young et al.(2009) to determine the most current realistic range prediction. We identified the threshold that produced the most reasonable map for the species according to known information and our present understanding of its current distribution (Young et al., 2009). We explored adequate potential sites and colonization areas under two types of potential dispersal scenarios for each species. In the full dispersal scenario (migration), the species was able to disperse totally without environmental 462 V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 impediments and change its geographic range according to the projected maps for 2080. In the no dispersal scenario, the species was considered unable to disperse in the landscape and its distribution was limited to the area of overlap between the present and future distributions (Araújo et al., 2004; Thomas et al., 2004). We used differences between the current and projected ranges to estimate the proportional change in range size for each species under each scenario. Overall we obtained eight future potential projections for each of the 21 species modeled; 168 present and future projections in total. To present the patterns of distribution maps in each climate model under full dispersal we combined the results of the 21 species projections. We also presented the distribution maps for species under the full dispersal scenario. All spatial analyses were performed with ArcGis 10.0.

2.5. Gap analysis

To study species represented in protected areas, a gap analysis was performed using the maps of legally established national, departmental and municipal protected areas in Peru and Bolivia. These maps were downloaded from the World Database on Protected Areas (http://protectedplanet.net/), which contains information from the National Service of Protected Areas of Bolivia (SERNAP) and Peru (SERNANP). We analyzed 43 protected areas in Bolivia (15 national, 11 departmental, 14 municipal and 3 private) and 19 in Peru (16 national and 3 Ramsar sites). These protected areas contain different categories of conservation and management: national parks, integrated management natural areas, wildlife reserves, national reserves, national and historical sanctuaries. We calculated the proportions of species represented in protected areas under current conditions and under the four future scenarios based on the full dispersal and no dispersal scenarios. We performed the gap analysis in protected areas following the method of Rodrigues et al.(2004) and Catullo et al.(2008). The conservation target of this method is 100% protection of the potential presence area for those species with range areas less than 1000 km2 and 10% for those species with a wide distribution greater than 250,000 km2. The conservation target for species with a potential area greater than 1000 km2 and less than 250,000 km2 was obtained by interpolation between the two extremes, using a linear regression of the potential area transformed logarithmically (Rodrigues et al., 2004). A species was identified as a total gap species if it did not meet the protection target in any protected area, whereas a partial gap species was established if only a portion of the potential area presence met the protection target, and a species was considered covered if met the protection target (Catullo et al., 2008).

3. Results

3.1. Modeling performance

The distribution methods showed variability in the ability to predict species distribution. The methods used in this study for the ensemble projection were composed mainly of four models: GBM, RF, MAXENT and FDA (70% of cases), and secondarily SRE, GLM and MARS (29%). ANN (1%) was hardly selected and in no case were CTA and GAM selected, either because their performance was not adequate according to the evaluation statistics or they did not match the current distribution. Individual species models selected had high values of AUC, 0.98 ± 0.03 (mean ± standard deviation) and TSS 0.94 ± 0.05. Overall, the performance of the ensemble model was high, with an average TSS of 0.97 ± 0.02, sensitivity of 99.85 ± 0.49 and specificity of 97.14 ± 1.9. The most important climate variables in the predictions across all models given by BIOMOD (Thuiller et al., 2009) were generally on average: mean annual temperature (0.32), precipitation of driest quarter (0.30) and mean diurnal range in temperature (0.23).

3.2. Extension of the range

All climatic scenarios predicted that the species may shift their patterns of potential geographic distribution in 2080, most by decreasing their range size (Table 1). In the best cases, the estimates indicated a decrease in range size for 91% of species and an increase for the 9% under the full dispersal scenario. In the worst cases, the estimates indicated a decrease in range size for 100% of the study species under the no dispersal scenario. The species that Ukmo-HadCM3 models predicted to increase in range size were A. harterti and P. simplex. The percentage of range size reductions of species under the full dispersal scenario was on average 75% for the Csiro-A2 model, 74% for the Csiro-A1b model, 42% for the Ukmo-A2 model and 32% for the Ukmo-A1b model. The range reduction under the no dispersal scenario was 85% for the Csiro-A2 model, 83% for the Csiro-A1b model, 61% for the Ukmo-A2 model and 52% for the Ukmo-A1b model. Using the average of all future scenarios, species may reduce their range size compared to the current distribution by 56% under the full dispersal scenario, 70% under the no dispersal scenario and 63% under the two dispersal scenarios. The majority of high-elevation birds (67%) may suffer higher reductions in range size (>60% of range loss) under the full dispersal scenario, whereas a minority of middle-elevation birds (22%) may suffer high percentage of reduction in range size. V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 463

Table 1 Species range shift percentages under climate models Csiro-Mk2 and Ukmo-HadCM3, scenarios A2 and A1b, and dispersal scenarios (D = full dispersal, ND = No dispersal). Negative values indicate gains in the range sizes. Species IUCN Current range Csiro-Mk2 Ukmo-HadCM3 Csiro-Mk2 Ukmo-HadCM3 km2 A2-D A1b-D A2-D A1b-D A2-ND A1b-ND A2-ND A1b-ND %%%%%%%%

Aglaeactis pamelaa LC 11 407.3 80.1 52.8 80.5 67.8 95.0 95.6 98.1 81.9 Andigena cucullata LC 36 691.4 83.3 88.5 54.8 48.2 86.1 88.8 61.4 58.3 Asthenes harterti LC 24 317.6 75.1 77.3 −31.5 −6.0 84.1 84.3 33.5 38.2 Asthenes berlepschia NT 1724.8 94.2 93.5 91.6 91.6 94.6 94.1 93.0 93.0 Atlapetes rufinuchaa LC 27 087.3 72.8 71.9 38.4 11.8 82.5 81.1 55.2 30.7 Cranioleuca albiceps LC 20 592.5 83.7 83.9 47.5 43.8 89.3 88.6 64.1 61.1 Cranioleuca henricaea EN 1566.1 65.3 83.0 98.7 94.1 99.4 98.9 99.0 97.3 Grallaria erythrotisa LC 22 754.9 82.2 82.4 29.8 18.5 86.7 85.7 44.5 38.4 Hapalopsittaca m. melanotis LC 20 993.4 88.0 89.3 73.6 53.7 90.0 90.5 78.7 62.3 Hemispingus calophrys LC 11 587.3 75.9 92.1 −7.9 5.5 85.5 99.9 28.5 41.8 Hemitriccus spodiops LC 47 222.6 44.8 57.9 6.9 4.5 62.3 65.9 20.6 18.2 Lipaugus uropygialis VU 14 793.8 85.7 91.3 38.7 32.9 92.7 93.2 70.7 56.8 Myrmotherula grisea LC 49 418.4 76.8 70.7 53.9 32.4 75.7 66.2 43.2 28.7 Pauxi unicornisa EN 13 877.6 84.6 81.5 73.3 55.1 84.7 79.8 77.2 70.7 Phibalura f. bolivianaa EN 7001.5 91.3 85.5 39.9 2.9 94.9 85.5 42.5 29.8 Pseudotriccus simplex LC 40 589.6 55.1 55.7 −10.2 −0.7 77.4 77.1 35.8 41.8 Scytalopus schulenbergi LC 17 613.8 91.1 69.5 64.6 20.3 98.4 94.9 78.2 54.0 Syndactyla striata LC 36 595.0 51.3 55.0 15.4 −5.8 77.8 77.8 54.1 29.9 Tangara meyerdeschauenseei NT 12 315.8 67.5 48.4 84.1 56.5 70.6 51.3 84.3 68.7 Euchrepomis sharpei EN 26 384.5 56.6 45.1 −10.3 20.3 73.5 66.4 63.4 57.7 Thamnophilus aroyae LC 38 523.6 80.5 77.0 54.3 31.3 81.2 78.2 56.3 37.0 a Bolivian endemic birds.

3.3. Distribution patterns

The plotted distribution patterns of all species combined in each future scenario compared to current conditions showed potential changes in different zones of southeastern Peru and Bolivia (Fig. 1). The A2 scenario in both climate models predicted strong effects of climate change on species by decreasing the probability of their ranges in the central area of the Bolivian Yungas (Fig. 1). The locations expected to suffer high species losses are in the Coroico (Nor and Sud Yungas Provinces) and in eastern Apolobamba (Bautista Saavedra Province) of the La Paz Department. An increase in the number of species around the northeast of Quilabamba (northern Cusco) is also expected. The A1b scenario showed variations in the output of climate model predictions related to the probability of future species ranges. The Csiro-Mk2 scenario maintains the predictions that few species would inhabit the central area of Bolivian Yungas. The Ukmo-Hadcm3 scenario is less extreme and did not show high species losses in the Bolivian Yungas. An increase in the ranges of some species around the Quilabamba location was predicted, and in a small zone around the mountains of Cocapata and Inquisivi in Bolivia.

3.4. Representation of species in protected areas and gap analysis

The probability of occurrence of species ranges in protected areas indicated that on average 34% of their geographic ranges under current conditions are represented in the protected areas of Bolivia and Peru (31% in Bolivia and 3% in Peru). In the future, the percentage of geographic ranges of species is predicted to decrease by an average of 14%. 11% of this range is expected to be located in Bolivia’s protected areas and 3% in Peru. These changes indicate a decrease in species representation in Bolivia’s protected areas more than in Peru’s. The gap analysis for current conditions (based on the average protection target of 50% of the total area) indicated that 8 species (38%) are covered species whereas 13 species (62%) are partial gap species. In addition, 8 partial gap species (38%) met less than 50% of their respective targets. In full dispersal future scenarios the analysis showed negative changes compared to the current conditions (Fig. 2). The average of the results of a future gap analysis of climate scenarios indicated two are covered species (6%) while 19 species (94%) are partial gap species based on the average protection target of 64% and 82% (min. and max.). In addition, 16 species (78%) of the partial gap species met less than 50% of their respective target. The species that may increase the probability of being total gap species in the future (meeting less than 10% of the target in average) of the current 13 partial gap species are A. pamela, C. albiceps, H. m. melanotis, S. schulenbergi, A. cucullata, L. uropygialis. A. berlepschi and C. henricae met this target under the future forecasts, except in scenarios Csiro-A2 and Ukmo- A2 respectively, where they become gap species. The eight covered species in current conditions that may change to partial gap species are M. grisea, P. unicornis, P. f. boliviana, T. meyerdeschauenseei, E. sharpei and T. aroyae. The no dispersal scenarios (Fig. 2) compared to the current conditions showed similar negative effects. The average of future results indicated primarily an increase in gap species (12%), a decrease in covered species (2%) and an increase in partial gap species (86%). It is added to the previous list (species that met less than 10% of the target): H. calophrys (gap 464 V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469

Fig. 1. Comparison of the richness patterns of 21 restricted-range bird species from the Andes of Bolivia and southeastern Peru from current conditions to climate change scenarios (under full dispersal conditions). High values imply increases in species richness (purple), low values imply decreases (gray). The bottom left figure shows the study area and the main protected areas (gray color): 1. Manu Park, 2. Megantoni Reserve, 3. Bahuaja Sonene Park, 4. Apolobamba Park, 5. Madidi Park, 6. Cotapata Park, 7. Isiboro Sécure Park, 8. Tunari Park, 9. Carrasco Park, 10. Amboró Park. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. Representation of species in protected areas under current and future climate conditions (C = Csiro-Mk2, U = Ukmo-HadCM3) for the full and no dispersal scenarios (D = full dispersal, ND = no dispersal). Different block patterns represent amounts of targets met in the gap analysis (expressed in percentages). species in Csiro-A1b) and M. grisea. S. striata may change from covered species to partial gap species in future conditions. C. henricae became a gap species in every scenario except in Ukmo-A1b, and A. berlepschi became a gap species in every scenario. The changes in the geographic distribution of the species contrasting the protected areas under full dispersal scenario are available in Appendix C. The protected areas that may maintain in future part of the species ranges are Carrasco, Madidi, Amboró, Bahuaja Sonene, Megantoni and Manu. The National Parks Cotapata and Tunari may lose almost all species predicted to be present under current conditions. Likewise, the municipal and small protected areas (<38.000 ha) such as Parabaná, Serranía del Tigre and Tequeje may lose the few species (<4) predicted under current conditions. V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 465

Fig. 3. Distribution of six threatened restricted-range birds projected for 2080 (median of four climate change scenarios) (blue color) under full dispersal conditions. The protected areas are shown in gray. The bottom left small figures (inside the projections of each species) represent the projected current distributions (yellow). PE = Peru and BO = Bolivia. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

In the case of threatened species (Fig. 3, Appendix C), these may have local shifts but a few protected areas may cover much of the species ranges under future conditions. Therefore, parks such as Carrasco and Madidi may cover part of four or five threatened species ranges. The protected areas such as Cotapata, Tunari, Pilón Lajas and Rio Grande Valles Cruceños may not represent the few threatened species predicted in the current conditions. A. berlepschi and C. henricae may not be covered by the protected areas.

4. Discussion

Based on the niche modeling, climate models and scenarios, we predicted the potential effects of climate change on the distribution of the restricted range species. Even if niche modeling and climate models have various sources of uncertainties (Wiens et al., 2009), the use of the BIOMOD2 ensemble method (including our best performing models), the four climate scenarios and two dispersal scenarios may have reduced these uncertainties (Araújo et al., 2004, 2005). It is expected that with the inclusion of more biological information on the species, new records, real dispersal scenarios as well as land use and vegetation change projections, the prediction of the effect of climate change could be improved. However, this detailed study may represent a feasible feature of the situation and conservation of these restricted range birds under climate change. Considering the outputs of the climate scenarios used in this study for 2080, Csiro-Mk2 showed few differences in projecting the species distribution between the A2 and A1b scenarios, whereas Ukmo-HadCM3 showed greater effect for the A1b than the A2 scenario. Between climate models, the Ukmo-HadCM3 showed less effect on species due to climate change than Csiro-Mk2, whose projections were extreme shifts in species distribution. Climate models may vary among variables and regions and this variation could be attributed to various reasons such as they incorporate distinct algorithms to portray the dynamics of atmospheric circulation (Wiens et al., 2009; Goberville et al., 2015). Despite these differences, all climate model scenarios based on rapid economic growth were consistent in predicting mostly negative changes in the species distribution. Temperature may be the most important climate variable limiting the distribution of species.

4.1. Changes in geographic distribution and conservation

The results indicated that the great majority of species studied (91%) may suffer a reduction in their geographic range, persisting in areas where portions of the current ranges are retained more than colonizing new areas. Some studies have found these tendencies to geographic reduction in tropical mountain species of South America, such as the threatened birds of the northern Andes in Colombia (Velásquez-Tibatá et al., 2012) and birds of the Atlantic forest in Brazil (Vieira de Souza et al., 2011). Moreover, the geographic range reduction (63%) is not far from the projections for range-restricted birds of the northern Andes in Colombia for 2050 (33–43%) (Velásquez-Tibatá et al., 2012). An increase in temperature can cause mountain tropical species to shift their distribution upslope. In the search for colder climates they may reduce their range 466 V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 size (Buermann et al., 2011; La Sorte and Jetz, 2010) and suffer population declines (Shoo et al., 2005; Harris et al., 2014). Indeed, in the last 42 years the mountain plant species in Peru have been migrating in altitude (Lutz et al., 2013), and birds have been moving gradually (Forero-Medina et al., 2011a). Although this study did not predict the disappearance of climate envelopes for bird species (total reduction of geographic range), which increases the likelihood of extinction (Williams et al., 2007), high percentages of range losses were predicted. Novel and disappearing climates in particular have been projected to be concentrated in the tropics (Williams et al., 2007). However, other factors could affect species under climate change, including their physiological, ecological, dispersal, population and evolutionary characteristics (Thuiller et al., 2005; Foden et al., 2013). Therefore, species could likely reach the extinction vortex before completely losing their habitat (Bellard et al., 2012). If the projections are met, the species may be threatened by climate change, as was estimated for other Andean species (Malcolm et al., 2006). According to our results, most species with small range sizes may be very threatened to climate change due to range reductions. Among the most sensitive species to climate change may be the globally threatened. For example, C. henricae, L. uropygialis, P. unicornis and P. f. boliviana have small range sizes (<20 000 km2) that in future may be drastically reduced, even considering the full dispersal scenarios. These species have decreasing small populations (<5.000 individuals, IUCN, 2014) and the probability that viable populations could persist in the future is not secure. Other birds currently listed as ‘‘Least Concern’’ such as C. albiceps, A. rufinucha T. ayorae, G. erythrotis, H. calophrys and S. schulenbergi may become likely threatened because of the range reduction and the influence of dispersal limitation. These understory insectivores generally have limited dispersal capabilities (Moore et al., 2008; Powell et al., 2015) so greater reductions in range size (>70%) of no dispersal scenario may be much approximated. Highland birds such as A. cucullata and A. pamela may also prone to be threatened. Even though this could be debatable because the first species would likely have long dispersal distances as most toucans (Holbrook, 2011) and the latter has altitudinal movements (Hennessey et al., 2003), both dispersal scenarios indicate high range reductions. However, the no dispersal scenario for most species may be close to reality as they are less likely to keep up the high rates of migration required to cope with rapid climate change (Malhi, 2012; Şekercioğlu et al., 2012). The range expansion under Ukmo-HadCM3 models for some species (particularly in A. harterti) may be explained by the species responding differentially to changes of climatic conditions. However, this common highland species has narrow elevation ranges and likely a decreasing population (IUCN, 2014) so that the Csiro-Mk2 projections may be better approximated. In the case of A. berlepschi even if the results should be read with caution because small samples used in modeling in some cases underpredicted their distribution, every scenario predicted high range losses. Overall, the analysis of the species is not far from the general prediction by the IUCN for Andean species (Foden et al., 2013). These species may have low ecological plasticity, specialized niches and reduced niche tolerances, as well as low genetic variability and small population sizes as a consequence of the way they evolved (Graham et al., 2011) which compromise their response to climate change. This approximation of climate change effects was performed with the intention to identify the species most in need of conservation action. However, the availability of life-history information (e.g. population trends) for most species could refine these predictions by using mechanistic or other models (Pacifici et al., 2015). For example, most species do not have an estimate of population size (see, IUCN, 2014) so studies on life history of the study species are needed. It has been suggested that land use change represents the greatest threat to tropical forest species and may reduce the potential for migration or dispersal to cooler or wetter regions in light of climate change (Jetz et al., 2007; Malhi, 2012). The mountain forests of the Andes region studied here have suffered historic deforestation due to human settlement (Killeen et al., 2007), and in various places the forests are not well conserved, surrounded by second-growth ferns and shrubs (Navarro and Maldonado, 2002). Deforestation may cause middle-elevation bird populations to decline (Harris et al., 2014) but at higher altitudes in the Andes, anthropogenic activities are also considered a barrier to upslope migration, as was reported for Andean plant species (Feeley and Silman, 2010). Therefore, the dispersal ability of the bird populations that inhabit these forests could be impeded as they move to a different unsuitable habitat or human settlements (Marini et al., 2009; Harris et al., 2014). This implies that conservation across the altitudinal gradient of mountain regions is fundamental, because these are the habitat for endemic birds and probably many other species, and it is necessary to reduce the habitat loss and other threats to reduce the pressures that stress the species under climate change (Hodgson et al., 2009). Therefore, we contend that the potential damage by climate change can be alleviated by reducing other threats (Hodgson et al., 2009); for example, reducing the fires in areas adjacent to the forest at higher elevations to let the species move to higher altitudes (Killeen et al., 2005; Feeley and Silman, 2010), as well as reducing deforestation and promoting sustainable land management practices. However, we should consider that other ecological constraints (i.e. interspecific aggression, Jankowski et al., 2010) or physical (i.e., topography traps, Forero-Medina et al., 2011b) among others could limit the upslope expansion or isolate highland species. There are challenging options proposed, including translocating species or establishing captive populations that could be analyzed according to the resources available in long-term (Mawdsley et al., 2009).

4.2. Representation of the species in protected areas

The modifications predicted in the geographic range of species would certainly influence the degree of species representation in the network of protected areas in the study area. The results indicated that under the current conditions, 38% are covered species and 62% are partial gap species. Comparison with previous studies (i.e. Young et al., 2009; Swenson V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469 467 et al., 2012) is not easy given their different objectives, but it is agreed that endemic birds in the tropical Andes are not totally protected and few bird species are well protected. This inadequacy of protected areas in representing bird species is mentioned because it is expected to get worse when the species may reduce their geographic ranges in the long term. The probability of suitable conditions for almost every bird species were projected to decrease in protected areas under two dispersal conditions, reducing to 10% of the geographic range of at least 10 species, and making them prone to be total gap species. Of those threatened species covered by protected areas in current conditions, four may not be more adequately represented in protected areas under future conditions (P. unicornis, P. f. boliviana, T. meyerdeschauenseei and E. sharpei). Some studies have found these trends for species in protected areas, predicting mainly total gap species in the future (i.e. Araújo et al., 2011) or those poorly represented in legally protected areas (Velásquez-Tibatá et al., 2012). In the study area we projected mainly an increase of partial gap species due to climate change. This study represents the first exploration to evaluate the representation of range-restricted birds in protected areas under climate change. Among the protected areas predicted to lose potentially endemic birds is Cotapata Park, as it is rich in species despite its small size (60,000 ha) (Ibisch et al., 2007). In contrast, the Megantoni Reserve and Manu Park in Peru may increase the number of some endemic bird species (i.e. H. spodiops). These protected areas do not currently present high probabilities of distributions of the birds studied but they are very rich in other taxa (CDC-UNALM and TNC, 2006). Carrasco Park is the protected area that may maintain and increase species in 2080. This park has the largest number of threatened species in Bolivia and a large number of vertebrate species (Ibisch et al., 2007). Carrasco Park may likely be important for other species that share similar biological traits to the study species. However, protected areas may still be important as habitat or stepping stones for species dispersal, so it is recommended that the protection of the management area of national parks be reinforced. Most of the protected areas in Bolivia are susceptible to deforestation because human settlements are situated adjacent to them, where the cultivation of illicit crops is widespread (Killeen et al., 2007). Similarly, in Peru the Camisea gas project in the Megantoni reserve has had heavy environmental impacts (SERNANP, 2007). Indeed, illegal deforestation, fires and illegal crop cultivation even in the protected areas affect biodiversity and maybe the species dispersal under climate change. Economic megaprojects such as oil or gas exploration should be seriously analyzed before their implementation in protected areas, taking into account the global effect of climate change. In the case of management of protected areas, species distributions shifts imply that climate change should not only be explicitly recognized as a threat but also as a call to action. Because the results found here may occur in other high or middle-elevation species, we suggest the management plans of each protected area should contain adaptive approach pro- grams under climate change. Although the conservation recommendations in response to climate change could be similar to those under current conditions, we suggest for the protected areas: (1) implement long-term species monitoring, given that in Bolivia at least this is not being done; (2) improve the management and recovery of deforested areas in protected areas and buffer areas to increase resilience. Carrasco and Amboró Nationals Parks for example are predicted to maintain distribution ranges but have high historical deforestation rates (Killeen et al., 2007); (3) plan buffering activities in areas adjacent to the protected areas in light of the nearby mining activities and energy development projects; (4) coordinate protected areas to work on the objectives of species protection; and (5) incorporate climate change into planning conservation activities. The application of these recommendations will depend on the strength of public institutions and other conservation entities.

5. Conclusions

Correlative models carry significant uncertainties for projecting future species distribution, but this first estimate could still be useful for the conservation of poorly known species like the Andean biodiversity inhabiting these countries. The hypothesis of trends in the range reduction of most birds, even in the full dispersal scenario indicate that they may be very threatened, and consequently most species may not be secure in protected areas. The need to plan the biodiversity conservation is urgent, reducing the non-climate stressors as well as managing protected areas in view of rapid climate change. We expect the results obtained in this study and the main recommendations proposed will be useful for the appropriate authorities in management decisions if they decide to conserve the birds.

Acknowledgments

We thank the Colección Boliviana de Fauna of the Bolivian Museum of Natural history for providing the study species database and the platforms Gbif, Ebird and Xeno-canto for granting free access to the bird database and the ornithologist that contributed this information. We appreciate the reviewers for valuable comments that improved the manuscript. This research was supported by the V. del R. Avalos scholarship of the Agencia de Cooperación Internacional de Chile (AGCI).

Appendix A. Supplementary data

Supplementary material related to this article can be found online at http://dx.doi.org/10.1016/j.gecco.2015.08.004. 468 V. del R. Avalos, J. Hernández / Global Ecology and Conservation 4 (2015) 459–469

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