Journal for Nature Conservation 70 (2022) 126302 Contents lists available at ScienceDirect Journal for Nature Conservation journal homepage: www.elsevier.com/locate/jnc Predicting potential distribution and identifying priority areas for conservation of the Yellow-tailed Woolly Monkey (Lagothrix flavicauda) in Peru Betty K. Guzman a,*, Alexander Cotrina-Sánchez a,b, Elvis E. Allauja-Salazar c, Christian M. Olivera Tarifeño d, Jhonny D. Ramos Sandoval d, Marlon Y. Hoyos Cerna d, Elgar Barboza e, Cristóbal Torres Guzmán a, Manuel Oliva a a Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Perú b Department for Innovation in Biological, Agri-Food and Forest Systems, Università degli Studi della Tuscia, Via San Camillo de Lellis, 4, 01100 Viterbo, Italy c Naturaleza y Cultura Internacional – NCI, Lima, Perú d Servicio Nacional de Áreas Naturales Protegidas por el Estado – SERNANP, Lima, Perú e Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria – INIA, Lima, Perú A R T I C L E I N F O A B S T R A C T Keywords: Species distribution models (SDMs) provide conservationist with spatial distributions estimations of priority MaxEnt species. Lagothrix flavicauda (Humboldt, 1812), commonly known as the Yellow-tailed Woolly Monkey, is one of Species distribution models the largest primates in the New World. This species is endemic to the montane forests of northern Peru, in the Natural protected areas departments of Amazonas, San Martín, Huánuco, Junín, La Libertad, and Loreto at elevation from1,000 to 2,800 Threats, extinction m. It is classified as “Critically Endangered” (CR) by the International Union for Conservation of Nature (IUCN) as well as by Peruvian legislation. Furthermore, it is listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Research on precise estimates of its potential distri- bution are scare. Therefore, in this study we modeled the potential distribution area of this species in Peru, the model was generated using the MaxEnt algorithm, along with 80 georeferenced occurrence records and 28 environmental variables. The total distribution (high, moderate, and low) for L. flavicauda is 29,383.3 km2, having 3,480.7 km2 as high potential distribution. In effect, 22.64 % (6,648.49 km2) of the total distribution area of L. flavicauda is found within Natural Protected Areas (NPAs), with the following categories representing the largest areas of distribution: Protected Forests (1,620.41 km2), Regional Conservation Areas (1,976.79 km2), and Private Conservation Areas (1,166.55 km2). After comparing the predicted distribution with the current NPAs system, we identified new priority areas for the conservation of the species. We, therefore, believe that this study will contribute significantly to the conservation of L. flavicauda in Peru. 1. Introduction feature (Serrano-Villavicencio et al., 2021). L. flavicauda is endemic to northern and central Peru along the eastern side of the Andean Cordil- The Yellow-tailed Woolly Monkey, Lagothrix flavicauda (Humboldt, lera (Serrano-Villavicencio et al., 2021). This area is considered a 1812), was first described over 200 years ago and believed extinct before biodiversity hotspot of the Tropical Andes (Shanee, 2011). L. flavicauda being rediscovered in 1974. It is amongts the rarest and largest monkeys has been recorded mainly between 1,400 and 2,800 m above sea level in the new world, but also one of the least studied (Leo, 1980; Mitter- (m.a.s.l) in the departments of Amazonas, San Martín (Shanee, 2011), meier et al., 1977; Shanee, 2011). The fur of L. flavicauda is a deep Loreto (Patterson & López, 2014), La Libertad (Parker & Barkley, 1981), mahogany color in both males and females. It has a dark grey pelage Huánuco (Aquino et al., 2016; Shanee, 2011), and recently in the with a white patch, supraorbital hairs around its snout, and a gold- department of Junín (McHugh et al., 2019). yellow patch on the genital region which is a principal distinctive The Yellow-tailed Woolly Monkey’s population is drastically * Corresponding author. E-mail address: bguzman@indes-ces.edu.pe (B.K. Guzman). https://doi.org/10.1016/j.jnc.2022.126302 Received 26 May 2022; Received in revised form 5 November 2022; Accepted 9 November 2022 Available online 14 November 2022 1617-1381/© 2022 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 declining mainly due to habitat loss and hunting pressure (Shanee & 1,300,000 km2) located between parallels 0◦03′00′’ and 18◦30′0′’ south, Shanee, 2014; Shanee & Shanee, 2015). It is currently estimated that its and meridians 68◦30′00′’ and 81◦30′00′’ west, sharing borders with total available habitat has reduced by 82 % and its population size by as Ecuador and Colombia to the north, Brazil to the east, Bolivia to the much as 93 % (Serrano-Villavicencio et al., 2021). The threats the spe- southeast, Chile to the south, and the Pacific Ocean to the west. The cies faces include habitat loss and fragmentation, hunting, logging altitudinal gradient of this region starts from 0 m.a.s.l. in the north and (Aquino et al., 2017; Leo, 1980; Shanee & Shanee, 2014; Shanee, 2011), reaches up to 6,800 m.a.s.l (Mataraju Mountain; Cotrina et al., 2021). conversion of forest cover for cattle and crops (Aquino et al., 2015), and The NPAs belong to the National System of Natural Areas Protected by mining (Shanee & Shanee, 2014). In addition to this, L. flavicauda has a the Peruvian State (SINANPE; SERNANP, 2022) and the categories low reproductive rate, restricted range, and a potential large reduction present in the study area are National Reserve (NR), National Park (NP), of niche availability caused by climate change in Amazonas, San Martín, Protected Forest (PF), Hunting Reserve (HR), Communal Reserve (CR), and Huánuco (Serrano-Villavicencio et al., 2021). For these reasons, the Landscape Reserve (LR), Historic Sanctuary (HS), National Sanctuary IUCN/SSC Primate Specialist Group (PSG) and the International Prima- (NS), Wildlife Refuge (WR), Reserved Zone (RZ), Regional Conservation tological Society (IPS), between 2008 and 2010, considered L. flavicauda Areas (RCA), and Private Conservation Areas (PCA, Fig. 1). to be one of the most endangered primate species in the world (Mitter- meier et al., 2009). With a declining population trend, it is currently 2.2. Occurrence records of L. flavicauda classified as “Critically Endangered” (CR) according to the A4cd criteria of the International Union for Conservation of Nature and Natural Re- Georeferenced records (latitude/longitude) of sightings were ob- sources (IUCN) Red List of Threatened Species (Shanee et al., 2021). It is tained from six sources of information: (i) Population Distribution of also included in Appendix I of the Convention on International Trade in Priority CITES Species of the Ministry of Environment (MINAM), Endangered Species of Wild Fauna and Flora (CITES; UNEP-WCMC, available at: https://geoservidor.minam.gob.pe/recursos/intercambio- 2021), categorized as a “Critically Endangered” species in Peruvian de-datos/; (ii) Global Biodiversity Information Facility (GBIF) plat- legislation (D.S. N◦ 004–2014-MINAGRI; MINAGRI, 2014), and included form, available at: https://www.gbif.org/, through QGIS Occurrences in the Red Book of Threatened Wild Fauna of Peru (SERFOR, 2018). Plugin of QGIS version 3.16; (iii) surveillance reports from park rangers In the last 15 years, several efforts, using information available at the of the National Service of Natural Areas Protected by the Peruvian State time, have been made to establish natural protected areas for the con- (SERNANP); (iv) scientific publications, specifically McHugh et al. servation of L. flavicauda, focusing on specific departments such as (2019); (v) reports from private researchers, obtained through personal Amazonas and San Martín (Buckingham & Shanee, 2009; Shanee et al., communications; and finally, (vi) georeferenced sightings of the species 2007). Nevertheless, one of the challenges in complementing these obtained (observational method) during field expeditions in Berlin conservation efforts is the identification and analysis of new areas of Forest PCA and Hierba Buena Allpayaku PCA. potential distribution. Species Distribution Models (SDMs) can help to Subsequently and having in mind that MaxEnt performs well determine the ecological requirements of specific species and predict compared to other commonly used techniques (Elith, Graham, et al., their potential range based on ecology and biogeography (Zhang et al., 2006; Wisz et al., 2008). However, it is sensitive to sampling biases 2019). SDMs are tools for mapping, monitoring, and predicting the (Anderson & Gonzalez, 2011; Phillips et al., 2009). Therefore, consid- potential distribution of wild flora and fauna (Miller, 2010), based on ering as a basis the reduction of inherent geographic biases associated the presence data of a given species in combination with predictive with the collection data (Boria et al., 2014), and added to that consid- environmental variables through statistical and cartographic procedures ered in previous studies in mountainous areas with high geographic (Guisan & Zimmermann, 2000). heterogeneity (Anderson & Raza, 2010; Pearson et al., 2007). Through SDMs have been increasingly applied in different studies on wildlife filtering, we reduced from 136 to 80 occurrence records, following the distribution, for example, in large mammals such as Helarctos malayanus following criteria that may affect model overfitting and correlations (Nazeri et al., 2012), Cervus nippon, Capricornis crispus, Sus scrofa, between variables, (i) records with insufficient identification, i.e., not Macaca fuscata, Ursus thibetanus (Saito et al., 2014), Tremarctos ornatus identified to species level; (ii) elimination of records with species names (Meza et al., 2020), Ailurus fulgens (Su et al., 2021). Examples for pri- not related to the species under study, and (iii) elimination of records mates include such as L. flavicauda, Aotus miconax and Lagothrix cana with missing or duplicate coordinates (Gueta & Carmel, 2016). The final (Cotrina et al., 2022; Shanee, 2016). Although no algorithm is consid- 80 occurrence records (Fig. 1), are located between the years 1974 to ered to be the “best” (Qiao et al., 2015), the most frequently used are 2012. It is known that sample size influences the results of SDMs, and it bioclimatic modeling (BIOCLIM), domain environmental envelope is considered that no algorithm predicts consistently well with a small (DOMAIN), ecological niche factor analysis (ENFA), Generalized Addi- sample size: n < 30 (Wisz et al., 2008), and that once the sample size tive Model (GAM), genetic algorithm for rule-set production (GARP), reaches a value of around 70, the reliability of the model becomes in- and Maximum Entropy Model (MaxEnt). Nevertheless, MaxEnt dependent of sample size (Jiménez-Valverde et al., 2009). Given this, we modeling has been widely used because it performs well with either consider 80 occurrence records for our sample size is acceptable. incomplete data or presence-only data (Zhang et al., 2019), demon- strating higher predictive accuracy, which together with its easy use and 2.3. Cartographic variables combination with geographic information systems, yields optimal and defensible results (Elith, et al., 2006; Meza et al., 2020; Nazeri et al., In the present study, 28 mapping variables were used (Table 1), 2012; Pearson et al., 2007; Wisz et al., 2008). including 19 bioclimatic variables; three topographic variables (alti- The objectives of this study are to: (1) to use a Maximum Entropy tude, slope, and aspect); species requirements (tree cover, tree height, (MaxEnt) approach to determine the current potential distribution of water source availability, and ecosystems), considering that the species L. flavicauda and (2) to compare the predicted distribution with the is found in the premontane and montane forests of the eastern slopes of current system of Natural Protected Areas (NPAs) to identify priority the Peruvian Andes, on steep slopes up to 60 %, trees with heights of areas for the conservation of L. flavicauda in Peru. 18–40 m, canopy and subcanopy, at heights of 8–15 m, and soil strata between 15 and 25 m above ground level 53 % of the time, > 25 m above 2. Materials and methods ground level 32 % of the time, between 10 and 15 m above ground 12 % of the time, and only 3 % of their time below 10 m (Serrano-Villavi- 2.1. Study area cencio et al., 2021); and other environmental conditions (air humidity and solar radiation). Bioclimatic variables and solar radiation were This study encompasses the entire territory of Peru (approximately obtained from the high spatial resolution global weather and climate 2 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Fig. 1. Study area and occurrence records of L. flavicauda. 3 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Table 1 Phillips et al., 2006). Considering, that collinearity can lead within Variables for MaxEnt modeling of L. flavicauda in Peru. variables to problems of overfitting or ambiguous interpretation (Zhong Variable Units Symbol et al., 2021), consequently, collinearity represents a minor problem than over-fitting or data uncertainty (De Marco & Nóbrega, 2018). Our final Bioclimatic Annual Mean Temperature ◦C bio_01 set included 14 variables (Table 2). Mean Diurnal Range ◦C bio_02 Isothermality bio_03 2.5. Building the model Temperature Seasonality ◦C bio_04 Max Temperature of Warmest Month ◦C bio_05 Min Temperature of Coldest Month ◦C bio_06 We used MaxEnt 3.4.4 (Philips et al., 2021) to model the habitat Annual Temperature Range ◦C bio_07 suitability of L. flavicauda in the study area. Randomly selected, 75 % Mean Temperature of Wettest Quarter ◦C bio_08 and 25 % of the georeferenced records were used for training and model Mean Temperature of Driest Quarter ◦C bio_09 validation, respectively. The algorithm was run using 10 replicates in Mean Temperature of Warmest Quarter ◦C bio_10 5,000 iterations with different random partitions, Bootstrap method, Mean Temperature of Coldest Quarter ◦C bio_11 Annual Precipitation mm bio_12 where the training data is selected by sampling with replacement from Precipitation of Wettest Month mm bio_13 the occurrence points, with the number of samples equaling the total Precipitation of Driest Month mm bio_14 number of occurrence points (Phillips, 2017), a convergence threshold Precipitation Seasonality mm bio_15 of 0.000001, and 10,000 maximum background points. Furthermore, we Precipitation of Wettest Quarter mm bio_16 Precipitation of Driest Quarter mm bio_17 used the Jackknife method to measure the importance of variables in Precipitation of Warmest Quarter mm bio_18 habitat mapping (Cotrina et al., 2021; Meza et al., 2020). The area under Precipitation of Coldest Quarter mm bio_19 the curve (AUC) obtained from a ROC curve was used to evaluate our Topographic model (Phillips et al., 2006). The AUC is an effective autonomous Elevation above mean sea level msnm elevation threshold index capable of assessing the ability of a model to discrimi- Slope of the terrain % slope Cardinal orientation of the slope – aspect nate occurrence from absence (Gebrewahid et al., 2020). It is a model Species requirements performance measure (Mohammad-Reza et al., 2018) that has been Tree cover % tree_cover widely used in species distribution modeling (Elith et al., 2006; Saha Tree height m dosel et al., 2021). In general, the AUC should be between 0.5 and 1: when the Distance to water sources m water_d Ecosystems type ecosystems AUC equals 0.5, model performance is equivalent to the pure guess; Other environmental variables therefore, model performance is rated as failed (0.5–0.6), poor Solar radiation kJ m− 2 day− 1 radiation (0.6–0.7), fair (0.7–0.8), good (0.8–0.9) and excellent (0.9–1) (Manel Relative humidity % humidity_r et al., 2001; Phillips et al., 2006). The logistic output format was chosen to obtain the species model by generating a raster of continuous values database, WorldClim ver 2.1 (https://www.worldclim.org/; Fick & in a range from 0 to 1. The obtained raster was reclassified into four Hijmans, 2017), with a spatial resolution of 30 arcseconds (~1 km), and ranges: (1) “high potential habitat” (>0.6), (2) ”moderate potential bioclimatic information was used under current conditions (average habitat” (0.4–0.6), (3) “low potential habitat” (0.2–0.4), and (4) ”no 1970–2000). Topographic variables were derived from the 90-meter potential“ habitat (<0.2), considering previous studies (Cotrina et al., spatial resolution Digital Elevation Model (DEM) dataset downloaded 2021; Cotrina et al., 2020; Gebrewahid et al., 2020; Mohammad-Reza from the EarthEnv-DEM90 portal (http://www.earthenv.org/DEM) et al., 2018; Saha et al., 2021; Yang et al., 2013; Zhang et al., 2019). sourced from CGIAR-CSI SRTM v4.1 and ASTER GDEM v2 data products (Robinson et al., 2014). Proximity to water was generated using the 2.6. Identification of potential conservation areas Euclidean distance algorithm (at 250 m of spatial resolution) from the water network described in the national charts of the National The identification of potential conservation areas is based on the Geographic Institute (IGN) of Peru, available through the Ministry of intersection between the high potential distribution of L. flavicuada and Education (MINEDU, 2002). The tree cover variable PROBAV-LC100- the NPAs created in the Peruvian territory of national, regional, and v3.0.1, was downloaded from Copernicus Global Land Service: Land private administration, obtained from the Geoserver (https://geo. Cover 100 m: collection 3: epoch 2015: Globe (Buchhorn et al., 2020). sernanp.gob.pe/visorsernanp/) managed by SERNANP. Tree height was downloaded from Mapping global forest canopy height (Potapov et al., 2021) and the ecosystem layer was obtained from the geoserver of the Ministry of Environment of Peru (https://geoservidor. minam.gob.pe/recursos/intercambio-de-datos/). Finally, relative hu- midity was obtained from the surface climate dataset (New et al., 2002), Table 2 and to standardize spatial data formats, we interpolated them using the Relative contributions (%) of environmental variables to the MaxEnt model of ordinary Kriging method in ArcGis version 10.5, with semi-variogram L. flavicauda in Peru. models: gaussian, spherical and exponential (Varouchakis, 2019). Variable Percent contribution Permutation importance Together, all 28 variables had a spatial resolution of 30 arcseconds (approximately 1 km2), which were converted to ASCII format to be ecosystems 31.7 1.3 elevation 25.5 22.6 used in the model. bio_15 13 14.1 bio_6 6.8 38.2 bio_14 6 1.4 2.4. Selection of environmental variables humidity_r 4.4 2.2 bio_10 4.4 1.5 In species distribution models, variables to be used are crucial, as dosel 2.5 0.2 water_d 1.6 1.4 some are biologically significant and others are of little importance if tree_cover 1.4 0.5 they are all incorporated into the model (Tanner et al., 2017). In that bio_13 1.1 1.3 sense, to avoid overfitting the model, we used only the most informative bio_3 0.6 1.3 variables (>1% contribution) selected using the permutation method bio_4 0.5 10.8 implemented in the MaxEnt program (Martínez-Meyer et al., 2021; bio_18 0.4 3.2 4 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 3. Results 0.9). By evaluating model performance, we assess the accuracy of pre- dictive models based on machine learning and ensure confidence in the 3.1. Model performance and importance of environmental variables results obtained (Cotrina et al., 2021). We selected 14 out of 28 envi- ronmental variables to model the potential distribution of L. flavicauda. The model performance showed an area under the curve (AUC) value In our model, 87.4 % of the potential distribution was driven by the of 0.990 (Fig. 2a), considered excellent (0.8 < AUC < 0.9). The response variables of ecosystems (Yunga montane forests), elevation, precipita- curves (Fig. 2c-q) reflect the dependence of the predicted suitability on tion seasonality (bio_15), min temperature of the coldest month both the selected variable and the dependencies induced by the corre- (bio_06), precipitation of the driest month (bio_14), and the percentage lations between the selected variables and other variables. of relative humidity. In terms of contribution and permutation, altitude (elevation above mean sea level) emerges as the most significant vari- 3.2. Potential distribution of L. flavicauda able (percent contribution: 25.5 and permutation importance: 22.6), proving to be a determining factor in the distribution range, as you are The distribution of L. flavicauda under current climatic and envi- only evaluating one species. This confirms elevation as one of the main ronmental conditions was identified predominantly in the northern and characteristics of the species’ habitat, with values recorded between central lands of Peru along the eastern side of the Andean Cordillera, 1,918 – 2,529 m.a.s.l. and steep slopes between 0 % and 60 % (Almeyda covering 29,383.28 km2 (2.3 %) of the study area. This potential habitat et al., 2019; Serrano-Villavicencio et al., 2021). On the other hand, distribution covers around eight departments of the Peruvian territory, although forest composition and especially the dominance of mature distributed as follows: “high potential” habitat, 3,480.7 km2 (0.3 %), foraging trees for L. flavicauda seem to be major factors in determining “moderate potential” 8,007.3 km2 (0.6 %), and “low potential”, habitat use; there is no clear influence of forest structure on habitat use 17,895.2 km2 (1.4 %). The IUCN map, however, shows that the resident by this species (Almeyda et al., 2019), which could explain the low distribution of L. flavicauda is 24,240.32 km2 (Fig. 3) distributed in six values of contribution and permutation of canopy and tree height var- departments of Peru (Amazonas, San Martín, Loreto, Cajamarca, La iables in our model. Libertad and Huánuco). SDMs are considered a set of numerical tools that combine obser- vations of species occurrence with environmental variables to answer 3.3. Priority areas for the conservation of L. flavicauda the relationship between a species and its environment (More et al., 2022); however, in theory, species SDMs are based on the realized niche The intersection between the potential distribution (Fig. 3) and the concept, and some studies suggest that they do not fully inform on biotic NPAs (Fig. 1) are considered priority areas for the conservation of the interactions (Wisz et al., 2013). Large-scale species patterns are influ- species under study within the scope of the NPAs (Fig. 4). It was iden- enced by both abiotic predictors and biotic interaction variables (Zim- tified that the total distribution of L. flavicauda covers 22.64 % mermann et al., 2010). Therefore, considering the potentially important (6,648.49 km2) of the territory of the NPAs, with a high potential dis- implications of biotic interactions in shaping species distribution pat- tribution of 1,258.59 km2, a moderate potential distribution of 1,799.25 terns, the application of spatially explicit modeling tools is considered km2, and finally a low potential distribution of 3,590.66 km2. The po- challenging (Wisz et al., 2013). Considering the modeling of our study tential distribution of L. flavicauda was located in PF: 1,620.41 km2 over a large geographic extent and with available data of coarse-grained, (5.51 %), HR: 4.65 km2 (0.02 %), NP: 839.85 km2 (2.86 %), CR: 237.88 this limits the measurement of biotic interactions in detail (Araújo & km2 (0.81 %), NS: 443.67 km2 (1.51 %), RZ: 363.34 km2 (1.24 %), RCA: Luoto, 2007; Schweiger et al., 2012). Therefore, the use of highly ac- 1,976.79 km2 (6.73 %) and in PCA: 1,166.55 km2 (3.97 %) (Fig. 4; curate observational data (GPS coordinates), and variables with a high Table 3). spatial resolution (e.g., temperature, precipitation), which in turn will make our predictions of current and future distribution more accurate, 4. Discussion will be required in the future for better accuracy in determining habitat categories (Graham et al., 2008; McPherson et al., 2006; Wisz et al., 4.1. Model performance and importance of environmental variables 2013). Our model obtained an excellent predictive performance (AUC > Fig. 2. Model performance based on the area under the curve (AUC) (a), Jackknife test of the significance of environmental variables for the MaxEnt model (b), and mean response curves of the 100 replicate MaxEnt runs (red) and standard deviation (blue) (c-p). 5 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Fig. 2. (continued). 4.2. Potential distribution of L. flavicauda tailed Woolly Monkey. Our distribution considers a “high potential” habitat of 3,480.7 km2 for L. flavicauda under current climatic and Our study presents new information on the distribution of Yellow- environmental conditions, largely lower than the species’ original 6 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Fig. 3. Potential distribution of L. flavicauda in Peru. national range previously estimated at 11,240 and 12,863 km2 (Leo, 5,143 km2 south of the Peruvian territory. It could also be due to a 1980). It is even smaller than the area considered by Shanee, (2016), difference in scales, considering that the IUCN classification is compli- who estimated the current maximum extent of occurrence of the species cated by issues of spatial scale, i.e., the finer the scale at which the at ~ 39,060 km2, of which 22,460 km2 were classified as good and distributions or habitats of taxa are represented, the smaller the area 16,600 km2 as very good. However, our distribution is close to the occupied. Thus, the choice of scale at which range is estimated may distribution reported by Cotrina et al., (2022), who considered a high influence the outcome of Red List assessments and could be a source of potential distribution of 3,354.74 km2 in the Peruvian territory. inconsistency and bias, and result in estimates that are more likely to Our study, along with research by Cotrina et al., (2022) and Shanee, exceed thresholds for the species’ threat categories (UICN, 2012). (2016), constitute the first efforts to consider the use of SDMs as a However, our results agree with the IUCN in the distribution of probabilistic decision-making tool for this species, which allows the L. flavicauda in the territories of San Martín, Amazonas, La Libertad, prediction and identification of geographical areas of potential habitat Huánuco, Pasco, and Loreto. by using the Maximum Entropy modeling technique (Elith, et al., 2006; Soberón & Townsend Peterson, 2005). 4.3. Priority areas for the conservation of L. flavicauda L. flavicauda has been recorded from several localities since its rediscovery in 1970 (Mittermeier et al., 1977). In 2019, an L. flavicauda Modeling the distribution of a species provides insight into its ecol- population was discovered in the department of Junín (Fig. 1), in the ogy, which has many applications in conservation, through the identi- Inchatoshi Kametsha Conservation Concession, near Pampa Hermosa fication of areas with a higher probability of occurrence to guide future River, far south of the rest of the species’ distribution (Serrano-Villavi- survey expeditions (Nazeri et al., 2012). cencio et al., 2021). This may explain why our potential distribution Peru’s National System of State Protected Areas (SINANPE) con- model differs from the IUCN Extant (resident) range of the species by siders a number and categories of protected areas, each with a different 7 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Fig. 4. Priority areas for conservation of L. flavicauda. 8 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 Table 3 Total potential area of distribution protected by the Natural Protected Area modalities in Peru. Categories Name Low Moderate High Total % km2 km2 km2 Protected forest de Pui Pui 4.92 – – San Matias-San Carlos 12.94 – – 1620.41 5.51 Alto Mayo 531.83 562.43 508.29 Hunting reserves Sunchubamba 4.65 – – 4.65 0.02 National Park de Cutervo 33.90 3.52 – 839.85 2.86 del Río Abiseo 604.04 18.65 – Yanachaga-Chemillén 167.12 – – Cordillera Azul 4.23 – – Ichigkat Muja-Cordillera del Cóndor 2.07 – – Yanachaga-Chemillén – 1.68 – Communal reserve Yanesha 2.11 – – 237.88 0.81 El Sira 18.50 – – Tuntanain 3.37 – – Chayu Naín 47.78 78.28 87.83 National Sanctuaries Pampa Hermosa 16.91 – – 443.67 1.51 Tabaconas-Namballe 66.98 11.44 – Cordillera de Colán 75.00 95.45 177.88 Reserved Zones Río Nieva 52.44 100.50 210.40 363.34 1.24 Regional Consevation Areas Cordillera Escalera 61.04 13.23 – 1976.79 6.73 Vista Alegre-Omia 145.46 277.61 31.26 Bosques Tropicales Estacionalmente Secos del Marañón 39.73 2.43 – Bosques de Shunté y Mishollo 826.34 191.45 – Bosque Montano de Carpish 83.04 – – Bosques El Chaupe, Cunia y Chinchiquilla 109.46 43.06 0.85 Paramos y Bosques Montanos de Jaén y Tabaconas 63.64 67.40 15.31 Codo del Pozuzo 5.48 – – Private Conservation Areas Paraje Capiro Llaylla 1.07 – – 1166.55 3.97 Bosques Montanos y Páramos Chicuate - Chinguelas 36.15 – – San Pedro de Chuquibamba 0.85 – – Llamapampa - La Jalca 100.98 32.23 Cavernas de Leo 0.12 – – La Pampa del Burro – 0.62 27.15 Bosque Berlín – – 0.59 Los Chilchos 240.39 98.68 – Bosque de Palmeras de la Comunidad Campesina Taulia Molinopampa 40.30 65.43 0.87 Huaylla Belén - Colcamar 16.18 7.63 – Milpuj - La Heredad 0.13 – – Copallín 25.75 24.15 51.37 San Marcos 0.49 – – Hierba Buena - Allpayacu 3.26 12.72 6.84 Tilacancha 9.25 – – La Niebla Forest 0.47 – – Arroyo Negro 1.03 0.52 0.01 Comunal San Pablo - Catarata Gocta 12.26 13.37 0.41 Páramos y Bosques Montanos, Paraíso de la Comunidad Campesina San Felipe 5.10 – – Copal Cuilungo 0.27 5.27 20.20 Páramos y Bosques Montanos San Miguel de Tabaconas 62.99 28.45 5.30 Monte Puyo (Bosque de Nubes) 14.89 41.08 99.93 Páramos y Bosques Montanos de la Comunidad Campesina San Juan de Sallique 15.33 1.92 – Bosques Montanos y Páramos de Huaricancha 12.41 – – Abra Patricia - Alto Nieva – 0.06 14.09 San Antonio 3.57 – – Huiquilla 4.41 – – Total 3590.66 1799.25 1258.59 6648.49 22.64 level of protection (Table 3). These protected areas cover a total po- Primate dominance, abundance, and distribution for most species are tential distribution area of L. flavicauda of 6,648.49 km2, which repre- mainly associated with habitat alteration and loss due to deforestation sents only 22.64 % of the total potential distribution area, of which the for agriculture, livestock, and extraction of timber and other forest re- high potential represents only 4.28 % (1,258.59 km2). These figures sources (Aquino et al., 2017). IUCN considers these activities to be the confirm that, the current network of protected areas is insufficient to main threats to the world’s primates, and they are responsible for 36 % conserve the current suitable habitat for L. flavicauda. Protected areas of the species inhabiting the Neotropics being threatened and their are an efficient tool for conserving forests; however, deforestation oc- populations declining by 63 % (Estrada et al., 2017). Estimates using curs on a smaller scale than outside their boundaries (Buckingham & predictions of future climate change suggest a further 7 % reduction in Shanee, 2009). In this sense, we recommend increasing the size and habitat availability for L. flavicauda over the next 50 years (Serrano- effectiveness of the current network of NPAs, considering the connec- Villavicencio et al., 2021; Shanee, 2016). tivity between them based on this type of study as a criterion when However, considering the 24 Private Conservation Areas where there choosing new sites for the creation of new natural protected areas, and is a high potential distribution for L. flavicauda (Table 3) we agree with complementary strategies, such as reforestation, environmental educa- Shanee, (2016) and Shanee et al., (2015) that a large number of private tion and community management with the population involved in the and communal protected areas makes these mechanisms extremely work of conservation and protection of L. flavicauda. important for the survival of the species, especially in areas with higher 9 B.K. Guzman et al. J o u r n a l f o r N a t u r e C o n s e r v a t i o n 70 (2022) 126302 human population density. This is because L. flavicauda can survive in Aquino, R., García, G., & Charpentier, E. (2016). Distribution and current status of the highly disturbed habitats but only temporarily (Serrano-Villavicencio peruvian yellow-tailed woolly monkey (Lagothrix flavicauda) in Montane forests of the Región Huánuco. Peru. Primate Conservation, 30(1), 31–37. et al., 2021). Aquino, R., García, G., Charpentier, E., & López, L. (2017). Estado de conservación de To effectively conserve L. flavicauda, we strongly recommend Lagothrix flavicauda y otros primates en bosques montanos de San Martín y intensive conservation of the high potential habitat areas identified by Huánuco. Perú. Revista Peruana de Biologia, 24(1), 025–034. https://doi.org/10.15 381/rpb.v24i1.13101. our model as well as the areas on the IUCN map inhabited by this spe- Aquino, R., Zárate, R., López, L., García, G., & Charpentier, E. (2015). Current Status and cies. Ultimately, our model provides knowledge of the potential distri- Threats to Lagothrix flavicauda and Other Primates in Montane Forest of the Región bution of the species for a better understanding of the habitat Huánuco. Primate Conservation, 29, 31–41. https://doi.org/10.1896/052.029.0111 preferences of the yellow-tailed woolly monkey in Peru; it also offers a Araújo, M. B., & Luoto, M. (2007). The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16(6), basis for the formulation of policies such as the national conservation 743–753. https://doi.org/10.1111/j.1466-8238.2007.00359.x plan for the species. Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012 5. 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