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Article

Potential Distribution and Identification of Critical Areas for the Preservation and Recovery of Three Species of Cinchona L. (Rubiaceae) in Northeastern Peru

by
Elver Coronel-Castro
1,2,*,
Gerson Meza-Mori
1,
Jose M. Camarena Torres
1,
Elí Pariente Mondragón
1,2,3,
Alexander Cotrina-Sanchez
4,
Manuel Oliva Cruz
1,
Rolando Salas López
1 and
Ricardo E. Campo Ramos
1,5,*
1
Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru
2
Herbario KUELAP, Facultad de Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru
3
Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru
4
Department for Innovation in Biological, Agri-Food and Forest Systems, Università degli Studi della Tus-cia, Via San Camillo de Lellis 4, 01100 Viterbo, Italy
5
Facultad de Ingeniería Civil y Ambiental, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(2), 321; https://doi.org/10.3390/f15020321
Submission received: 10 November 2023 / Revised: 31 December 2023 / Accepted: 5 January 2024 / Published: 8 February 2024
(This article belongs to the Special Issue Forest Coverage and Spatial Distribution of Tree Species under Regional and Global Changes)
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Abstract

:
The genus Cinchona L. has important medicinal, cultural, and economic value and is the emblematic tree of Peru. The genus is mainly found in the cloud forests of the Andes. However, the expansion of agriculture and livestock farming in the department of Amazonas is degrading these ecosystems and has reduced the size of the genus’s populations. In this work, we model the potential distribution under current conditions of three Cinchona species (C. capuli L. Anderson, C. macrocalyx Pav. Ex DC., and C. pubescens Vahl.) to identify areas with a high likelihood of species presence and their key conservation and reforestation zones. We fitted a maximum entropy (MaxEnt) model using nineteen bioclimatic variables, three topographic variables, nine edaphic variables, and solar radiation. Under current conditions, the potential distribution of C. capuli covers 17.22% (7243.98 km2); C. macrocalyx, 29.11% (12,238.91 km2); and C. pubescens, 22.94% (9647.63 km2) of the study area, which was mostly located in central and southern Amazonas. Only 24.29% (25.51% of C. capuli, 21.02% of C. macrocalyx, and 26.35% of C. pubescens) of the potential distributions are within protected areas, while 10,987.22 km2 of the surface area of the department of Amazonas is degraded, of which 29.80% covers the area of probable occurrence of C. capuli, 38.72% of C. macrocalyx, and 34.82% of C. pubescens. Consequently, it is necessary to promote additional conservation strategies for Cinchona, including the establishment of new protected areas and the recovery of degraded habitats, in order to protect this species.

1. Introduction

Andean montane forests hold globally significant levels of biodiversity, harboring an exceptional variety of species and much endemism [1,2]. These ecosystems provide essential ecosystem services, such as carbon sequestration [3] and streamflow regulation during dry seasons [4]. In addition, the organic soil of these forests has unique hydraulic properties, including high water infiltration and retention [5,6]. However, their steep topography makes them vulnerable to erosion during heavy rains [7]. Although they face significant threats such as climate change and natural disturbances [8,9], it is human activities (population expansion and resource extraction) that are accelerating their deterioration [7]. These pressures are growing and are expected to continue due to the persistent demand for forest resources and food [10].
The Cinchona genus originates from the Andean valleys of South America [11] and is located in the tropical region of the Andes mountain range [12,13]. The greatest diversity of species is observed in the mountains of Peru [14], with northern Peru identified as one of the genus’s diversity hotspots [15]. Around 12 species of Cinchona have been documented in the Amazonas department, mainly inhabiting mountain forests between 600 and 3400 m above sea level. However, it faces threats from human activities such as agriculture, livestock farming, and urban expansion [16] that negatively impact Cinchona populations [13,15,16,17]. Research efforts in the Amazonas department have predominantly focused on aspects such as the diversity and taxonomy of the genus [18], as well as its propagation [16,17,19]. Recent studies on Cinchona in Peruvian territory and the Amazonas department have analyzed its distribution as a focus for conservation and restoration [20,21,22]. Despite these efforts, there is a lack of more-detailed investigations on how different species of Cinchona are distributed. These species have different habitats, environmental conditions, uses, and conservation levels, requiring more specific studies to better understand population dynamics and identify effective conservation strategies to implement.
Species distribution models (SDMs) are tools that link species occurrence information to a range of bioclimatic, topographic, and edaphic variables. These tools play a vital role in protecting species, studying biogeography, and obtaining information on the impacts of climate change [23,24,25,26]. Within the field of SDMs, the maximum entropy algorithm (MaxEnt) estimates the potential distribution of a species, based on the principle that the most accurate prediction is achieved by optimizing the entropy of the distribution under specific environmental conditions [26]. This model stands out for its superior predictive accuracy compared to other models and its ability to work efficiently with small sample sizes [27,28]. The MaxEnt algorithm has been used extensively in modelling the distribution of various species, addressing present conditions and climate change scenarios [28]. It has been applied in a wide range of contexts, such as plant conservation, especially for endangered and endemic species [29,30,31,32,33,34]. It has also been useful in modeling the distribution of invasive species, forestry management, and agriculture [35,36,37,38].
In this work, the potential distribution of three Cinchona species (C. capuli, C. macrocalyx, and C. pubescens) in the department of Amazonas, northeastern Peru, was modeled. The research delved into how these species are connected to conservation areas and how regional degradation could impact them. Given their status as forest species, they face several threats due to their timber value and preference for fertile soils, often converted for agricultural and livestock purposes. The aim of this analysis extended beyond merely mapping the current distribution of these species; it also sought to identify protected areas and those with potential for their conservation and restoration, acknowledging their endangered status. To address this, specific questions were posed: (1) What is the potential distribution area of C. capuli, C. macrocalyx, and C. pubescens in the department of Amazonas? (2) Do the potential distribution areas of these species align with the conservation areas within the department? (3) Is it necessary to conserve and restore the distribution area of these Cinchona species? For each species, (i) a baseline file of georeferenced presence records and environmental variables was constructed, (ii) maps of currently suitable environmental habitats were generated, and (iii) potential areas for conservation and restoration were identified.

2. Materials and Methods

2.1. Study Area

This research was carried out in the department of Amazonas, located in the northeast of the Peruvian Andean zone (Figure 1). Amazonas covers about 42,050 km2 of mountainous area, mostly covered by the closed canopy rainforest and Andean Forest, with geographical coordinates ranging from 3°0′ to 7°2′ south latitude and 77°0′ to 78°42′ west longitude. This area exhibits a significant variation in altitude, ranging from 120 m above sea level in the north to 4900 in the south. The department stands out for its impressive ecological diversity, manifested in the presence of four distinct ecosystems: lowland forest, highland forest or yunga, the Andean forests and grasslands, and tropical dry forest [39].
The department of Amazonas exhibits a wide range of climatic conditions, ranging from hot and humid areas to hot and dry regions. Temperatures vary significantly, with highs of 40 °C in the northern lowland forests and lows of 2 °C in the southern highlands. Water availability is also variable in the department, with some areas experiencing an annual shortage of around 924 mm of precipitation, while other areas receive an excess of up to 3000 mm per year [39]. Amazonas is the department in Peru with the third-highest number of conservation areas, including natural protected areas (NPA), regional conservation areas (RCA), and private conservation areas (PCA) [40]. Despite these conservation measures, approximately 11,000 km2 (27.4%) of the department’s territory has suffered degradation [41]. This is due to inappropriate agricultural practices [42,43,44], unsustainable forest use [45], deforestation driven by road construction, and the unregulated expansion of urban areas [46,47,48].

2.2. Records of Cinchona Species

Records of Cinchona capuli, C. macrocalyx, and C. pubescens (Figure S1) in the department of Amazonas, northeastern Peru, were collected via field trips conducted between March 2021 and June 2023. These trips were made to collect botanical samples of these genera. Simultaneously, specimens were stored in the KUELAP, USM, and MOL herbaria were examined. Photographs of exsiccata from international herbaria, such as ECON, K, MO, and US, were also collected [49]. Additionally, occurrence data previously selected from the Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/ (accessed on 2 April 2023)) were utilized following a taxonomic review of the samples. The records that lacked a photograph of the specimen or presented uncertain data were excluded. In total, 110 records of the three Cinchona species were obtained for the department, including 16 on C. capuli, 33 on C. macrocalyx, and 61 on C. pubescens (Figure 1, Table S1).

2.3. Bioclimatic, Environmental, Topographic, and Edaphic Variables

A literature review was conducted to identify variables that could contribute to the distribution models [50]. This included 19 bioclimatic variables and one environmental variable (radiation), obtained from the WorldClim version 2 database at 30 s (~1 km) resolution (http://www.worldclim.org/ (accessed on 22 June 2023)) [51]; three topographic variables (elevation, aspect, and slope) derived from the Digital Elevation Model (DEM) at a spatial resolution of 250 m downloaded from the CGIAR Consortium for Spatial Information portal (http://srtm.csi.cgiar.org/ (accessed on 26 June 2023)) [52]; and nine edaphic variables, obtained from Soil Grids (https://soilgrids.org/ (accessed on 29 June 2023)) at a spatial resolution of 250 m. The bioclimatic and environmental layers were used in their current conditions (1970–2000 average), as they are widely used in ecological studies due to their free availability, global coverage, and good quality [53]. The data sets were re-sampled at a resolution of 250 m, and in total, 32 thematic layers were obtained. All variables were converted to the ASCII (American Standard Code for Information Interchange) format (Table 1).

2.4. Variable Selection

The variables used in the distribution models may exhibit a high degree of correlation, potentially leading to less-reliable predictions due to the similar contributions of two or more variables [27,53]. To address this issue and mitigate multicollinearity [54,55], the following criteria were considered: (i) extracting pixel values of the 32 variables from georeferenced species records [56,57,58]; (ii) determining the number of variables groups using K-means clustering with the factoextra package in R (Figure 2), which served as input for (iii) constructing dendrograms and selecting one variable per group [54] (Figure 3). However, in cases where certain groups contained more than one variable, a Pearson correlation analysis was conducted within each group, and variables with higher correlation (|r| > 0.7) were removed. This threshold is considered appropriate to prevent collinearity from distorting model estimation [54,59,60,61]. The application of these criteria resulted in the selection of a subset of eight variables for C. capuli (Figure 2a) and C. macrocalyx (Figure 2b), and nine for C. pubescens (Figure 2c).

2.5. Model Construction

The maximum entropy algorithm was used to create potential distribution models, utilizing the open-source software MaxEnt version 3.4.1. The georeferenced records were randomly divided into two sets: one for training (75% of the records) and one for validation (25% of the records) of each model. Ten replicates were made for each species using the bootstrap method with a maximum of 1000 iterations each, enabling the algorithm to make a more realistic prediction [26]. The convergence threshold was set at 0.00001, meaning that the algorithm continued to iterate until the difference between successive iterations was lower than this value. Additionally, a maximum limit of 10,000 background points was set for the modelling process [62]. The other default settings were left unchanged, due to MaxEnt’s ability to automatically select the most appropriate function based on the amount of data available for the model. This automatic selection of settings is important to optimize the performance of the model by adapting it to the specific characteristics of the available data [28]. To validate the models, the area under the curve (AUC) method, derived from the receiver operating characteristic (ROC) [63], was used. This method assigns a score that reflects the model’s predictive performance. Based on the AUC values obtained, five differentiated performance levels were established [64]: “excellent” for AUC > 0.9, “good” for AUC in the range of 0.8–0.9, “acceptable” for AUC between 0.7 and 0.8, “poor” for AUC in the range of 0.6–0.7, and “invalid” for AUC less than 0.6. The main advantage of this approach is its independence from subjective thresholds, which gives a higher degree of objectivity in assessing results [65]. The results were presented in a logistic format [66], generating a map of continuous probability values ranging from 0 to 1 for the potential distribution ranges. These values were then categorized into four potential distributions [31,32,66]: “high” for values greater than 0.6, “moderate” for values between 0.4 and 0.6, “low” for values between 0.2 and 0.4, and “no potential distribution” for values below 0.2.

2.6. Conservation and Recovery of Cinchona Species

A superimposition process was performed using the three maps, each depicting the current potential distribution of one of the three Cinchona species. This process utilized the Shapefile from the Peru Protected Natural Areas System, last updated in 2023 and accessible on the SERNANP geoserver (available at https://geo.sernanp.gob.pe/visorsernanp/# (accessed on 11 September 2023)). The aim was to identify areas within the department where protected Cinchona species are located under various conservation modalities. These modalities include national parks (NPs), national sanctuaries (NSs), communal reserves (CRs), reserved zones (RZs), buffer zones (BZs), regional conservation areas (RCAs), and private conservation areas (PCAs) [40]. Additionally, the layers displaying the potential distributions of Cinchona species were overlaid onto the map of degraded areas in Peru (2001–2021). This map, crafted by the Ministry of the Environment, was based on estimating the total or partial loss of certain essential ecosystem components such as water, soil, and species. This loss impacts the natural structure and functioning of these ecosystems, diminishing their ability to sustain diverse living organisms, including humans; that is, their capacity to provide ecosystem services. Indicators recommended by the United Nations Convention to Combat Desertification and Drought on Land Degradation Neutrality (LDN) were utilized [41]. This analysis allowed the identification of areas within the department where Cinchona species have distribution potential but are currently degraded, showing potential for restoration. Additionally, it pinpointed zones of potential species distribution within conservation areas or those experiencing no degradation, which could be suitable for conservation efforts [40,41].

3. Results

3.1. Contribution of the Variables

The contributions of environmental variables (Table S1) and the results of the jackknife test (Figure S2) revealed the cumulative contribution of classified variables affecting the habitat suitability of the three Cinchona species as follows: edaphic variables (80.3% for C. capuli, 79.4% for C. macrocalyx, and 13.1% for C. pubescens), climatic variables (19.5% for C. capuli, 2.2% for C. macrocalyx, and 86.3% for C. pubescens), and topographic variables (0.2% for C. capuli, 18.4% for C. macrocalyx, and 0.6% for C. pubescens). However, at a general level, it was observed that only three variables in each model significantly contributed. In the distribution model of C. capuli, the variables cec, bio03, and bio04 accounted for 99.7% of the contribution, whereas for C. macrocalyx, the variables cec, aspect, and clay contributed 94.7% to the distribution model. Additionally, the variables bio06, bio14, and cec contributed 91.1% in the distribution model of C. pubescens (Table 2).

3.2. Performance of the Distribution Pattern of the Model

Three current distribution models were generated for the Cinchona species. The results showed that all three species achieved area under the curve (AUC) values greater than 0.9, indicating excellent predictive performance in each case (as detailed in Table 3). These results demonstrate a highly reliable capability of predicting the distribution of these species (Figure S2).

3.3. Occurrence of Cinchona Species

Under current edaphoclimatic conditions, the potential distribution (high, moderate, and low) of the three Cinchona species in the department of Amazonas was determined. C. capuli covers an area of 7243.98 km2 (1891.76 km2 high, 2171.41 km2 moderate, and 3180.81 km2 low probability), representing 17.22% of the department, C. macrocalyx has a potential distribution area of 12 238.91 km2 (5120.35 km2 high, 3073.51 km2 moderate, and 4045.05 km2 low probability), accounting for 29.11% of the study area. C. pubescens covers an area of 9647.63 km2 (4142.04 km2 high, 2348.38 km2 moderate, and 3157.21 km2 low probability), equivalent to 22.94% of the territory of Amazonas (Figure 4). The distribution of these species is mainly concentrated in the central and southern parts of the department in the provinces of Bongará, Chachapoyas, Luya, and Rodríguez de Mendoza. In the north, the province of Utcubamba also shows potential for the distribution of these species, particulary C. mcacrocalyx. Mountain ecosystems, such as the montane and altimontane forests (Pluvial) of Yunga, present a greater distribution potential for these species. However, C. macrocalyx can also be found in Jalca and grasslands (Figure 5).

3.4. Conservation of Cinchona Species and Recovery Areas

In the department of Amazonas, it was found that 25.51% of the area with potential occurrence for C. capuli is located within conservation areas, covering a surface area of 1848.39 km2. This represents 14. 98% of the total surface area of the conservation areas and includes 4.60% (113.92 km2) of the territory of national sanctuaries (NSs), 39.06% (280.17 km2) of regional conservation areas (RCAs), 67.96% (753.89 km2) of private conservation areas (PCAs), 0.67% (9.15 km2) of reserved zones (RZs), and 10.37% (691 km2) of the buffer zone (BZ). In total, 370.84 km2 of high-probability areas for C. capuli occurrence are within these conservation areas.
Similarly, 21.02% (2572.95 km2) of the area with potential for C. macrocalyx occurrence is located within conservation areas, constituting 20.86% of the surface areas. This encompasses 10.12% (250.45 km2) of NS, 48.03% (344.53 km2) of RCA, 86.83% (963.15 km2) of PCA, 6.02% (82.28 km2) of RZ, and 13.99% (932.54 km2) of BZ territories. The probability of C. macrocalyx occurrence is high in 8.38% (1033.98 km2) of the conservation areas.
Finally, regarding C. pubescens, 26.34% (2541.86 km2) of the area with potential occurrence lies within conservation areas, which is 20.60% of the total are. This includes 10.29% (254.67 km2) of NS, 56.65% (406.37 km2) of RCA, 86.21% (956.24 km2) of PCA, 0.93% (12.7 km2) of RZ, and 13.68% (911.88 km2) of BZ. Finally, in the conservation areas of Amazonas, 10.15% are highly probable for the potential distribution of C. pubescens (Table 4).
The three Cinchona species are all distributed in one NS (Cordillera Colán National Sanctuary—CCNS), one RCA (except for C. macrocalyx, which is present two RCAs), seventeen PCAs, one RZ (Río Nieva), and the BZ of both the Alto Mayo Protected Forest and the CCNS (Figure 6).
In the study area, it was identified that approximately 10,987.22 km2 of the territory has experienced degradation. Of this, 29.80% overlaps with the zone of potential presence of C. capuli, 38.72% with that of C. macrocalyx, and 34.82% with the zone of C. pubescens (Table 5, Figure 7). Furthermore, within these degraded areas, 1018.42 km2, 1962.38 km2, and 1719.69 km2 were found to have a high probability of harboring C. capuli, C. macrocalyx, and C. pubescens, respectively. Consequently, applying appropriate conservation and management measures could potentially recover these areas.

4. Discussion

This paper presents the potential distribution of three species of the genus Cinchona (C. capuli, C. macrocalyx, and C. pubescens) in the department of Amazonas, northeastern Peru. The study specifically focused on their spatial distribution in conservation and degraded areas to identify habitats of these Cinchona species with potential for recovery in the department. This is the first attempt to use the species distribution model (SDM) with the maximum entropy modelling technique to predict and identify areas likely to harbor C. capuli, C. macrocalyx, and C. pubescens in Amazonas [67]. This model has previously been applied at departmental [21], regional [22,68], national, and global levels [20]. In contrast to prior studies on modelling the distribution of the genus [20,21,22,68], this study not only utilized occurrence data from virtual databases, herbarium databases, and floristic inventories, but also included species collected in the field. These species were taxonomically identified using morphological and morphometric characteristics [13,14,15]. Additionally, we thoroughly reviewed data related to botanical samples deposited in herbaria to build a database reflecting the actual presence of the studied species. This approach is crucial as the genus Cinchona can be confused with its close relatives Remijia and Ladenbergia [69]; approximately 330 names have been attributed to the Cinchona species, most of which are currently synonyms and/or correspond to related genera [11,14]. Furthermore, the biodiversity data obtained from GBIF often contain taxonomic and georeferencing errors [70], which can lead to incorrect model calibrations [71]. Hence, our study was based on a maximum entropy model and is thus believed to be better than previous studies in predictive accuracy and tolerance, especially considering the smaller scale of our sample (at the department level) [28]. The results of this analysis predict more specific distribution (at the species level, as opposed to the entire genus, as in previous studies), enabling the establishment of more effective forest management strategies [31,72].
Spatial distribution plays a key role in understanding a species [72]. In this study, a model based on current data was used [70]. The analysis required several species-specific variables including nineteen bioclimatic, three topographic, and nine edaphic variables, as well as solar radiation variables [73]. It was determined that only 1891.76 km² of the Amazonas department’s territory exhibits a high potential distribution area for C. capuli. This may be attributed to the species’s limited occurrence in Peruvian territory, previously recorded only in the department of Piura, northern Peru [13,15], and in southern Ecuador [14,74]. C. macrocalyx and C. pubescens are species with larger areas of high probability of occurrence in Amazonas, comprising 5120.35 km² and 4142.04 km², respectively. The potential distribution of these species is supported by the fact that C. macrocalyx is mainly found in the Loja macro-region and specifically in the southern and northern Andean areas of the south of Ecuador, extreme north of Peru, central southern Peru, and Bolivia. C. pubescens is found mainly along the slope of the South American Andes [74]. A high probability of encountering these three species can be primarily expected in the central and southern parts of the Amazonas department, a prediction that coincides with Vergara et al. [22], who identified this department as having the potential for the occurrence for the entire Cinchona genus in Peru. Another reason for the possible distribution potential of the species in this area is the prevalence of cloud forests (montane forests), which are suitable habitats for the genus [11,75]. Contrarily, García et al. [20] did not identify Amazonas as a department with a high likelihood of potential distribution areas for Cinchona, and only identifies a high probability of their presence in the southern part of the province of Chachapoyas (district of Leimebamba). The difference in predictions might stem from García et al. [20] not incorporating edaphic variables [22], which can significantly influence the model, considering Cinchona species grow in fertile, well-drained soils, mostly with a thick layer of organic matter, a sandy loam texture, and an acidic-to-neutral pH [76,77], often rich in iron, nitrogen, and potassium [78].
Although Amazonas is notable for its high number of conservation areas, this does not necessarily translate into the effective preservation of Cinchona species [79]. Approximately 24.29% of the estimated potential distribution for these species in the department is protected within conservation areas (25.51% for C. capuli, 21.02% for C. macrocalyx, and 26.34% for C. pubescens). However, it is estimated that 34.45% of the department’s categorized degraded territory overlaps with the potential distribution area of these Cinchona species (29.80% for C. capuli, 38.72% for C. macrocalyx, and 34.82% for C. pubescens). The high percentages of degraded areas and the low presence of Cinchona species in conservation areas indicate threats from deforestation for agricultural and livestock expansion [16], as well as forest fires that menace the entire genus in Peruvian territory [15]. Consequently, the restoration of this degraded land is feasible through recovery plans that include afforestation and reforestation with Cinchona species [80]. The Ministry of Agriculture and Irrigation approved the “Action Plan for Forest Repopulation with Cinchona genus species (Quinine Tree) 2020–2022,” aiming to produce and plant 145 hectares with Cinchona species (Cinchona calisaya Wedd., C. officinalis L., and C. pubescens Vahl) in Amazonas, Cajamarca, Cusco, Huánuco, Junín, Lambayeque, Lima, Pasco, Piura, and Puno [81]. However, the species selection criteria and repopulation zones remain undisclosed [82]. In Amazonas, several public institutions are executing forestry projects, mainly promoting the planting of introduced species like Pinus sp., Eucalyptus sp., and Cupressus sp., among others [83], which often overlooks the native flora species in Amazonas, such as C. capuli, categorized as Near Threatened (NT); C. pubescens, evaluated as Least Concern (LC) with an unknown population; and C. macrocalyx, categorized as Least Concern (LC) with a stable population [84]. Paradoxically, species that are less protected often face greater threats [85].
Therefore, the creation and implementation of a regional action plan for reforestation using species of the genus Cinchona is proposed. This strategy aims to conserve the genus, restore forests, preserve natural ecosystems, and contribute to climate change mitigation [86,87,88]. In summary, this research was focused on predicting the possible distribution of three species belonging to the genus Cinchona in the department of Amazonas, considering current climatic conditions. Additionally, it also analyzed the extent to which this potential distribution overlaps with conservation areas and degraded areas. However, future research could explore the distribution of these species in the context of climate change, similar to a study by Rojas et al. [32], which examined five timber tree species in the Amazonas, Peru. This would be especially relevant, as identifying areas with stable distribution both presently and in the future could be crucial for the success of any conservation or restoration initiative [73,89]. The limitations encountered in this study include the scarcity of available local climatic information, leading us to work with global data obtained from the WorldClim version 2 database (http://www.worldclim.org/ (accessed on 22 June 2023)), as well as the lack of local information on soil variables and the limited presence of records of species in the department. These limitations on the influencing factors could have impacted the model calibration [90]; however, the integration of various environmental variables and real-time data contributed to making our model more accurate compared to previous studies on the genus [20,21,22]. Therefore, it is recommended that future studies continue investigating distribution models of these and other Cinchona species, considering anthropogenic variables, soil variable values from soil analyses, and actual climatic values from the area obtained from meteorological stations, as this will enhance the prediction of potential distribution within the genus [91]. Nevertheless, the information generated in this study holds significant importance as it provides a theoretical foundation for new distribution studies and broader or more specific research that offers insights for forest management, afforestation, and/or the conservation of the montane forests in the southern and central regions of the Amazonas department, which serve as ideal habitats for these Cinchona species.

5. Conclusions

The successful modeling of the potential distribution of C. capuli, C. macrocalyx, and C. pubescens under current conditions revealed a 23.09% occurrence probability in the Amazonas department. This includes 17.22% for C. capuli, 29.11% for C. macrocalyx, and 22.94% for C. pubescens, spanning an average area of approximately 10,000 km2. Of this, only 24.29% of the potential distribution area for these species is under protection in conservation areas within the department. Meanwhile, 34.45% of the potential area for these species is in a degraded state. This degradation poses a significant threat to the occurrence areas of C. capuli, C. macrocalyx, and C. pubescens in Amazonas, primarily due to deforestation for agricultural and livestock expansion and recurring forest fires. To safeguard the Cinchona species, a range of conservation strategies are proposed. These include creating new protected areas and recovering degraded habitats through reforestation efforts with C. capuli, C. macrocalyx, and C. pubescens plantations. The development and implementation of a regional action plan for forest repopulation with Cinchona-genus species is suggested. Such strategies are crucial not only for conserving the genus but also for restoring forests, preserving natural ecosystems, and mitigating climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15020321/s1, Figure S1: Field collections records of the Cinchona genus: (a) C. capuli, (b) C. pubescens, (c) C. macrocalyx., Figure S2: Jackknife test showing how different environmental variables affect the modelling of the distribution of the three Cinchona species in the department of Amazonas, northeastern Peru., Figure S3: ROC curve of the MaxEnt model for the three Cinchona species. Table S1: Presence records utilized in modelling the potential distribution of the three species of the genus Cinchona in the department of Amazonas, northeastern Peru. Table S2: Calculations of the proportions of influence of environmental variables in the MaxEnt model.

Author Contributions

Conceptualization, E.C.-C. and G.M.-M.; data curation, J.M.C.T. and R.S.L.; formal analysis, E.C.-C., G.M.-M. and E.P.M.; funding acquisition, M.O.C. and R.E.C.R.; investigation, E.C.-C., G.M.-M., J.M.C.T., E.P.M., M.O.C., R.E.C.R. and R.S.L.; methodology, E.C.-C., G.M.-M., J.M.C.T. and R.S.L.; project administration, M.O.C. and R.E.C.R.; software, E.C.-C., J.M.C.T. and G.M.-M.; supervision, E.P.M., M.O.C. and R.E.C.R.; writing—original draft preparation, E.C.-C.; writing—review and editing, E.C.-C., G.M.-M., J.M.C.T., E.P.M., A.C.-S., M.O.C., R.S.L. and R.E.C.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was mainly financed by CUI Project 2261386 “Creation of Laboratory Services of Genetic Resources of Biodiversity and Conservation of Wild Species of the National University Toribio Rodriguez de Mendoza, Amazonas Region”. Other financing entities of this research are the CUI Project N° 312235 “Creation of a Geomatics and Remote Sensing Laboratory Service of the National University Toribio Rodriguez, Amazonas Region”- GEOMATICA, and the Vice Rectorate of Research of the National University Toribio Rodriguez de Mendoza of Amazonas”.

Data Availability Statement

The data presented in this study are available on request from the corresponding author (privacy).

Acknowledgments

The authors are grateful for the support provided by the Instituto de Investigaciones para el Desarrollo Sostenible de Ceja de Selva of the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas and the KUELAP herbarium for allowing the review of the Cinchona botanical samples deposited in their facilities.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Young, K.R.; León, B. Biodiversity conservation in Peru’s eastern montane forests. Mt. Res. Dev. 2000, 20, 208–211. [Google Scholar] [CrossRef]
  2. Marian, F.; Ramírez Castillo, P.; Iñiguez Armijos, C.; Günter, S.; Maraun, M.; Scheu, S. Conversion of Andean montane forests into plantations: Effects on soil characteristics, microorganisms, and microarthropods. Biotropica 2020, 52, 1142–1154. [Google Scholar] [CrossRef]
  3. Souza, C.R.; Mariano, R.F.; Maia, V.A.; Pompeu, P.V.; Dos Santos, R.M.; Fontes, M.A.L. Carbon stock and uptake in the high-elevation tropical montane forests of the threatened Atlantic Forest hotspot: Ecosystem function and effects of elevation variation. Sci. Total Environ. 2023, 882, 163503. [Google Scholar] [CrossRef]
  4. Bruijnzeel, L.A. Hydrological Impacts of Converting Tropical Montane Cloud Forest to Pasture, with Initial Reference to Northern Costa Rica. Final Technical Report DFID-FRP Project No. R7991; Vrije Universiteit Amsterdam: Amsterdam, The Netherlands, 2006; pp. 2–4. [Google Scholar]
  5. Osorio, A.; Bahamon, C. Dinámica de la Humedad del Suelo en Bosque alto Andino en el Páramo de Guerrero, Cundinamarca-Colombia. Master’s Thesis, Universidad Nacional de Colombia, Medellín, Colombia, 2008. [Google Scholar]
  6. Tobón, C.; Bruijnzeel, L.A.; Frumau, A. Physical and hydraulic properties of Tropical Montane Cloud Forest soils and their changes after conversion to pasture. In Proceedings of the Second International Symposium: Science for Conserving and Managing Tropical Montane Cloud Forests, Waimea, HI, USA, July 27–1 August 2004. [Google Scholar]
  7. Leigh, E.G. Tropical Forest Ecology: A View from Barro Colorado Island; Oxford University Press: New York, NY, USA, 1999. [Google Scholar]
  8. Brummitt, N.; Lughadha, E.N. Biodiversity: Where’s hot and where’s not. Conserv. Biol. 2003, 17, 1442–1448. Available online: https://www.jstor.org/stable/3588969 (accessed on 4 September 2023). [CrossRef]
  9. Barthlott, W.; Mutke, J.; Rafiqpoor, M.D.; Kier, G.; Kreft, H. Global centres of vascular plant diversity. Nova Acta Leopold 2005, 92, 61–83. [Google Scholar]
  10. Rands, M.R.; Adams, W.M.; Bennun, L.; Butchart, S.H.; Clements, A.; Coomes, D.; Entwistle, A.; Hodge, I.; Kapos, V.; Scharlemann, J.P.; et al. Biodiversity conservation: Challenges beyond 2010. Science 2010, 329, 1298–1303. [Google Scholar] [CrossRef] [PubMed]
  11. Aymard, G.A. Breve reseña de los aspectos taxonómicos y nomenclaturales actuales del género Cinchona (Rubiaceae-Cinchoneae). Rev. Acad. Colomb. Cienc. Exactas Físicas Nat. 2019, 43, 234–241. [Google Scholar] [CrossRef]
  12. Garmendia, A. El Árbol de la Quina (Cinchona spp.), Distribución, Caracterización de su Hábitat y Arquitectura; Universidad Técnica Particular de Loja: Loja, Ecuador, 2005. [Google Scholar]
  13. Huamán, L.K. Evaluación Morfológica y del Estado de Conservación de Seis Especies del Género Cinchona L. (Rubiaceae) en los Andes del Norte y Centro de Perú. Master’s Thesis, Universidad Nacional Mayor de San Marcos, Lima, Perú, 2020. [Google Scholar]
  14. Andersson, L. A revision of the genus Cinchona (Rubiaceae-Cinchoneae). Mem. N. Y. Bot. Gard. 1998, 80, 1–75. [Google Scholar]
  15. Huamán, L.; Albán, J.; Chilquillo, E. Aspectos taxonómicos y avances en el conocimiento del estado actual del árbol de la Quina (Cinchona officinalis L.) en el Norte de Perú. Ecol. Apl. 2019, 18, 145–153. [Google Scholar] [CrossRef]
  16. Sánchez, T.; Silva, G.; Chichipe, A.K.; Trigoso, M.; Bobadilla, L.G.; Jiménez, G.Y. Influencia de Sustratos y Concentraciones de ácido indol butírico en la Propagación Vegetativa de Cinchona officinalis L.(quina) en Amazonas, Perú. Rev. Cuba. Cienc. For. 2020, 8, 241–251. Available online: http://scielo.sld.cu/pdf/cfp/v8n2/2310-3469-cfp-8-02-241.pdf (accessed on 8 September 2023).
  17. Castañeda, J.H.V.; Sabaleta, E.L.; Hernández, M.K.B.; Meza, S.N.V.; Sánchez, L.M.Q. Comparación de sustratos en la propagación sexual y asexual del árbol de la quina (Cinchona officinalis). Rev. Investig. Agroprod. Sustentable 2019, 2, 77–85. [Google Scholar] [CrossRef]
  18. Perez Ocampo, J. Diversidad y Distribución de las Quinas (Cinchona, Rubiaceae) Usando DNA Barcoding para su Conservación en Amazonas. Master’s Thesis, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Perú, 2021. [Google Scholar]
  19. Huamán Vela, M.H. Efecto de Reguladores de Crecimiento y Sustratos en la Propagación Vegetativa de Cinchona pubescens Vahl (Quina) en Microtunel, Amazonas, Perú. Bachelor’s Thesis, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Perú, 2023. [Google Scholar]
  20. García, L.; Veneros, J.; Chavez, S.G.; Oliva, M.; Rojas-Briceno, N.B. World historical mapping and potential distribution of Cinchona spp. in Peru as a contribution for its restoration and conservation. J. Nat. Conserv. 2022, 70, 126290. [Google Scholar] [CrossRef]
  21. Vargas Aguilar, C.A. Mapas de Distribución Geográfica Histórica y Actual de Cinchona sp. en la Región Amazonas y el Perú. Bachelor’s Thesis, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Perú, 2021. [Google Scholar]
  22. Vergara, A.J.; Cieza-Tarrillo, D.; Ocaña, C.; Quiñonez, L.; Idrogo-Vasquez, G.; Muñoz-Astecker, L.D.; Auquiñivin-Silva, E.A.; Cruzalegui, R.J.; Arbizu, C.I. Current and Future Spatial Distribution of the Genus Cinchona in Peru: Opportunities for Conservation in the Face of Climate Change. Sustainability 2023, 15, 14109. [Google Scholar] [CrossRef]
  23. Gómez-Díaz, J.A.; Baena, M.L.; González-Zamora, A.; Delfín-Alfonso, C.A. Potential present and future distributions of the genus Atta of Mexico. PLoS ONE 2023, 18, e0292072. [Google Scholar] [CrossRef]
  24. Tafesse, B.; Bekele, T.; Demissew, S.; Dullo, B.W.; Nemomissa, S.; Chala, D. Conservation implications of mapping the potential distribution of an Ethiopian endemic versatile medicinal plant, Echinops kebericho Mesfin. Ecol. Evol. 2023, 13, e10061. [Google Scholar] [CrossRef]
  25. Qi, Y.; Xian, X.; Zhao, H.; Yang, M.; Zhang, Y.; Yu, W.; Liu, W. World Spread of Tropical Soda Apple (Solanum viarum) under Global Change: Historical Reconstruction, Niche Shift, and Potential Geographic Distribution. Biology 2023, 12, 1179. [Google Scholar] [CrossRef]
  26. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  27. Aguirre-Gutiérrez, J.; Carvalheiro, L.G.; Polce, C.; van Loon, E.E.; Raes, N.; Reemer, M.; Biesmeijer, J.C. Fit-for-purpose: Species distribution model performance depends on evaluation criteria–Dutch hoverflies as a case study. PLoS ONE 2013, 8, e63708. [Google Scholar] [CrossRef]
  28. Merow, C.; Smith, M.J.; Silander, J.A., Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 2013, 36, 1058–1069. [Google Scholar] [CrossRef]
  29. Bai, Y.; Wei, X.; Li, X. Distributional dynamics of a vulnerable species in response to past and future climate change: A window for conservation prospects. PeerJ 2018, 6, e4287. [Google Scholar] [CrossRef] [PubMed]
  30. Cavalcante, R.A.D.S.; Nascimento, F.A.O.D.; Pereira, M.A.S.B.F.; Souza, D.P.D.; Fontana, A.P.; Cotarelli, V.M.; Oliveira, M.A.; Moura Júnior, E.G.D. Ampliação do conhecimento biogeográfico de Pleurophora pulchra (Lythraceae) com enfoque em biologia da conservação. Rodriguésia 2019, 70, e02772017. [Google Scholar] [CrossRef]
  31. Cotrina Sánchez, D.A.; Barboza Castillo, E.; Rojas Briceño, N.B.; Oliva, M.; Torres Guzman, C.; Amasifuen Guerra, C.A.; Bandopadhyay, S. Distribution models of timber species for forest conservation and restoration in the Andean-Amazonian landscape, North of Peru. Sustainability 2020, 12, 7945. [Google Scholar] [CrossRef]
  32. Rojas Briceño, N.B.; Cotrina Sanchez, D.A.; Barboza Castillo, E.; Barrena Gurbillon, M.A.; Sarmiento, F.O.; Sotomayor, D.A.; Oliva, M.; Salas López, R. Current and future distribution of five timber forest species in Amazonas, Northeast Peru: Contributions towards a restoration strategy. Diversity 2020, 12, 305. [Google Scholar] [CrossRef]
  33. Song, X.; Jiang, Y.; Zhao, L.; Jin, L.; Yan, C.; Liao, W. Predicting the Potential Distribution of the Szechwan Rat Snake (Euprepiophis perlacea) and Its Response to Climate Change in the Yingjing Area of the Giant Panda National Park. Animals 2023, 13, 3828. [Google Scholar] [CrossRef] [PubMed]
  34. Driver, S.M.; Eversole, C.B.; Unger, D.R.; Kulhavy, D.L.; Schalk, C.M.; Hung, I.K. Assessing the Impacts of Climate Change on the At-Risk Species Anaxyrus microscaphus (The Arizona Toad): A Local and Range-Wide Habitat Suitability Analysis. Ecologies 2023, 4, 762–778. [Google Scholar] [CrossRef]
  35. Abdelaal, M.; Fois, M.; Fenu, G.; Bacchetta, G. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecol. Inform. 2019, 50, 68–75. [Google Scholar] [CrossRef]
  36. Li, M.; Zhang, H. Predicting the Distribution of the Invasive Species Atractosteus spatula, the Alligator Gar, in China. Water 2023, 15, 4291. [Google Scholar] [CrossRef]
  37. Ouyang, X.; Chen, A.; Li, Y.; Han, X.; Lin, H. Predicting the potential distribution of pine wilt disease in China under climate change. Insects 2022, 13, 1147. [Google Scholar] [CrossRef] [PubMed]
  38. Han, Q.; Liu, Y.; Jiang, H.; Chen, X.; Feng, H. Evaluation of Climate Change Impacts on the Potential Distribution of Wild Radish in East Asia. Plants 2023, 12, 3187. [Google Scholar] [CrossRef]
  39. Vargas, R.; Clima, J. Estudios Temáticos para la Zonificación Ecológica Económica del Departamento de Amazonas; Instituto de Investigaciones de la Amazonía Peruana (IIAP) & Programa de Investigaciones en Cambio Climático, Desarrollo Territorial y Ambiente (PROTERRA): Chachapoyas, Perú, 2010; Volume 1, pp. 1–27. [Google Scholar]
  40. SERNANP. Áreas Naturales Protegidas de Administración Nacional con Categoría Definitiva; SERNANP: Lima, Perú, 2020. [Google Scholar]
  41. MINAM. Estudio para la Identificación de Áreas Degradadas y Propuesta de Monitoreo; MINAM: Lima, Perú, 2021; Available online: https://geoservidor.minam.gob.pe/recursos/intercambio-de-datos/ (accessed on 11 September 2023).
  42. Yonz, J.A.V.; Quispe, W.S.A. Deforestación y pérdida de bosques en la Provincia de Condorcanqui, Amazonas, Perú, durante el período 2001–2021. Ing. Investig. 2023, 5, e790. [Google Scholar] [CrossRef]
  43. López, R.S.; Calampa, N.J.R.; Castillo, E.B.; Chchiple, M.E.M.; Oliva, M. Microzonificación agroecológica de sistemas agrosilvopastoriles empleando un modelo de procesamiento basado en SIG en parcelas en la provincia de Bongará, Amazonas (Perú). Rev. Investig. Agroprod. Sustentable 2019, 1, 40–50. [Google Scholar] [CrossRef]
  44. Raymundo Jáuregui, A.A. Evaluación de la Pérdida del Suelo Mediante el Modelo Rusle en la Microcuenca del río Jucusbamba, Provincia de Luya, Región Amazonas (Perú). Master’s Thesis, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Perú, 2018. [Google Scholar]
  45. Shanee, N.; Shanee, S. Land Trafficking, Migration, and Conservation in the “No-Man’s Land” of Northeastern Peru. Trop. Conserv. Sci. 2016, 9, 1940082916682957. [Google Scholar] [CrossRef]
  46. Salas, L.R.; Barboza, C.E.; Oliva, C.M. Dinámica multitemporal de índices de deforestación en el distrito de Florida, departamento de Amazonas, Perú. Rev. INDES 2014, 2, 18–27. [Google Scholar] [CrossRef]
  47. Rojas, B.N.B.; Barboza, C.E.; Maicelo, Q.J.L.; Oliva, C.S.M.; Salas, L.R. Deforestación en la Amazonía peruana: Índices de cambios de cobertura y uso del suelo basado en SIG. Boletín Asoc. Geógrafos Españoles 2019, 81, 1–34. [Google Scholar] [CrossRef]
  48. Mendoza, C.M.E.; Salas, L.R.; Barboza, C.E. Análisis multitemporal de la deforestación usando la clasificación basada en objetos, distrito de Leymebamba (Perú). Rev. INDES 2015, 3, 67–76. [Google Scholar] [CrossRef]
  49. Thiers, B. Index Herbariorum: A Global Directory of Public Herbaria and Associated Staff; New York Botanical Garden’s Virtual Herbarium: New York, NY, USA, 2020; Available online: http://sweetgum.nybg.org/science/ih/ (accessed on 12 September 2023).
  50. Armas, A.; De Lombaerde, E.; Sanczuk, P.; van Meerbeek, K.; Carón, M.; Honnay, O.; Verbeeck, H.; Duchateau, L.; Schrevens, E.; Verheyen, K.; et al. Distribucion Actual de la Cinchona Pubescens y Cinchona Calisaya y el Impacto Futuro en sus Hábitats a Causa del Cambio Climático. 2022. Available online: https://www.researchgate.net/publication/362610014_Distribucion_actual_de_la_Cinchona_pubescens_y_Cinchona_calisaya_y_el_impacto_futuro_en_sus_habitats_a_causa_del_cambio_climatico (accessed on 15 September 2023).
  51. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  52. Meza Mori, G.; Rojas-Briceño, N.B.; Cotrina Sánchez, A.; Oliva-Cruz, M.; Olivera Tarifeño, C.M.; Hoyos Cerna, M.Y.; Ramos Sandoval, J.D.; Torres Guzmán, C. Potential Current and Future Distribution of the Long-Whiskered Owlet (Xenoglaux loweryi) in Amazonas and San Martin, NW Peru. Animals 2022, 12, 1794. [Google Scholar] [CrossRef]
  53. Tanner, E.P.; Papeş, M.; Elmore, R.D.; Fuhlendorf, S.D.; Davis, C.A. Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species’ distributional shifts. PLoS ONE 2017, 12, e0184316. [Google Scholar] [CrossRef]
  54. Kariyawasam, C.S.; Kumar, L.; Ratnayake, S.S. Invasive plant species establishment and range dynamics in Sri Lanka under climate change. Entropy 2019, 21, 571. [Google Scholar] [CrossRef]
  55. Zhang, Y.; Tang, J.; Ren, G.; Zhao, K.; Wang, X. Global potential distribution prediction of Xanthium italicum based on Maxent model. Sci. Rep. 2021, 11, 16545. [Google Scholar] [CrossRef]
  56. Marchi, M.; Sinjur, I.; Bozzano, M.; Westergren, M. Evaluating WorldClim Version 1 (1961–1990) as the baseline for sustainable use of forest and environmental resources in a changing climate. Sustainability 2019, 11, 3043. [Google Scholar] [CrossRef]
  57. Yang, M.; Sun, L.; Yu, Y.; Zhang, H.; Malik, I.; Wistuba, M.; Yu, R. Predicting the Potential Geographical Distribution of Rhodiola L. in China under Climate Change Scenarios. Plants 2023, 12, 3735. [Google Scholar] [CrossRef]
  58. Meza Mori, G.; Barboza Castillo, E.; Torres Guzmán, C.; Cotrina Sánchez, D.A.; Guzman Valqui, B.K.; Oliva, M.; Bandopadhyay, S.; Salas López, R.; Rojas Briceño, N.B. Predictive modelling of current and future potential distribution of the spectacled bear (Tremarctos ornatus) in Amazonas, northeast Peru. Animals 2020, 10, 1816. [Google Scholar] [CrossRef]
  59. De Marco, P.; Nóbrega, C.C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 2018, 13, e0202403. [Google Scholar] [CrossRef]
  60. Brun, P.; Thuiller, W.; Chauvier, Y.; Pellissier, L.; Wüest, R.O.; Wang, Z.; Zimmermann, N.E. Model complexity affects species distribution projections under climate change. J. Biogeogr. 2020, 47, 130–142. [Google Scholar] [CrossRef]
  61. Bekele, M.; Seifu, A.; Wube, S.; Tilaye, G. Modeling the Current and future potential distribution of Arabis alpina using MaxEnt softwarein selected mountains of Ethiopia. Sustain. Agric. Food Environ. Res. 2023, 12. [Google Scholar] [CrossRef]
  62. Guzman, B.K.; Cotrina-Sánchez, A.; Allauja-Salazar, E.E.; Olivera Tarifeno, C.M.; Ramos Sandoval, J.D.; Hoyos Cerna, M.Y.; Barboza, E.; Torres Guzmán, C.; Oliva, M. Predicting potential distribution and identifying priority areas for conservation of the Yellow-tailed Woolly Monkey (Lagothrix flavicauda) in Peru. J. Nat. Conserv. 2022, 70, 126302. [Google Scholar] [CrossRef]
  63. He, Y.; Ma, J.; Chen, G. Potential geographical distribution and its multi-factor analysis of Pinus massoniana in China based on the maxent model. Ecol. Indic. 2023, 154, 110790. [Google Scholar] [CrossRef]
  64. Zvidzai, M.; Mawere, K.K.; N’andu, R.J.; Ndaimani, H.; Zanamwe, C.; Zengeya, F.M. Application of maximum entropy (MaxEnt) to understand the spatial dimension of human–wildlife conflict (HWC) risk in areas adjacent to Gonarezhou National Park of Zimbabwe. Ecol. Soc. 2023, 28, 18. [Google Scholar] [CrossRef]
  65. Hentschel, A.E.; Beijert, I.J.; Bosschieter, J.; Kauer, P.C.; Vis, A.N.; Lissenberg-Witte, B.I.; van Moorselaar, R.J.A.; Steenbergen, R.D.M.; Nieuwenhuijzen, J.A. Bladder cancer detection in urine using DNA methylation markers: A technical and prospective preclinical validation. Clin. Epigenet. 2022, 14, 1–9. [Google Scholar] [CrossRef] [PubMed]
  66. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  67. Waheed, M.; Arshad, F.; Majeed, M.; Haq, S.M.; Aziz, R.; Bussmann, R.W.; Ali, K.; Subhan, F.; Jones, D.A.; Zaitouny, A. Potential distribution of a noxious weed (Solanum viarum Du-nal), current status, and future invasion risk based on MaxEnt modeling. Geol. Ecol. Landsc. 2023, 1–16. [Google Scholar] [CrossRef]
  68. Llatas Mires, E. Modelamiento de la Distribución Natural del Género Cinchona en el norte del Perú. Bachelor’s Thesis, Universidad Nacional Autónoma de Chota, Chota, Perú, 2023. [Google Scholar]
  69. González-Orozco, C.E.; Guillén, E.G.; Cuvi, N. Changes of Cinchona distribution over the past two centuries in the northern Andes. R. Soc. Open Sci. 2023, 10, 230229. [Google Scholar] [CrossRef]
  70. Varela, S.; Lobo, J.M.; Hortal, J. Using species distribution models in paleobiogeography: A matter of data, predictors and concepts. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2011, 310, 451–463. [Google Scholar] [CrossRef]
  71. Varela, S.; Mateo, R.G.; García-Valdés, R.; Fernández-González, F. Macroecología y ecoinformática: Sesgos, errores y predicciones en el modelado de distribuciones. Ecosistemas 2014, 23, 46–53. [Google Scholar] [CrossRef]
  72. Godsoe, W.; Franklin, J.; Blanchet, F.G. Effects of biotic interactions on modeled species’ distribution can be masked by environmental gradients. Ecol. Evol. 2017, 7, 654–664. [Google Scholar] [CrossRef]
  73. Cotrina Sanchez, A.; Rojas Briceno, N.B.; Bandopadhyay, S.; Ghosh, S.; Torres Guzman, C.; Oliva, M.; Guzman, B.K.; Salas Lopez, R. Biogeographic distribution of Cedrela spp. genus in Peru using MaxEnt modeling: A conservation and restoration approach. Diversity 2021, 13, 261. [Google Scholar] [CrossRef]
  74. Gonzalez-Orozco, C. Regiones biogeográficas del género Cinchona L. (Rubiaceae-Cinchoneae). Rev. Noved. Colomb. 2021, 16, 135–156. [Google Scholar] [CrossRef]
  75. Albán, J. Etnobotánica de Rubiaceae peruanas. Ph.D. Thesis, Universidad Nacional Mayor de San Marcos, Lima, Perú, 2013. [Google Scholar]
  76. Asicona Caba, P. Evaluación de Cuatro Sustratos en Semilleros de Quina (Cinchona Ledgeriana; Rubiaceae) en Escuintla. Master’s Thesis, Universidad Rafael Landívar, Escuintla, Guatemala, 2013. [Google Scholar]
  77. Fernandez Zarate, F.H.; Huaccha Castillo, A.E.; Quiñones Huatangari, L.; Seminario Cunya, A.; Vaca Marquina, S.P. Producción del Árbol de la Quina (Cinchona micrantha R. y P.); Universidad Nacional de Jaén: Jaén, Perú, 2022. [Google Scholar]
  78. Padilla, T. Estudio Fenológico y Análisis de las Características del Suelo Donde se Desarrolla Cinchona officinalis L. Cuatro Relictos Boscosos de la Provincia de Loja. Master’s Thesis, Universidad Nacional de Loja, Loja, Ecuador, 2017. [Google Scholar]
  79. Monteferri, B.; Cuba, A.; Zari, L.; Grandez, P. Áreas de Conservación Privada en el Perú: Avances y Propuestas a 20 Años de su Creación; Sociedad Peruana de Derecho Ambiental: Lima, Perú, 2019. [Google Scholar]
  80. Bonn Challenge. A Global Effort to Bring 150 Million Hectares of Deforested and Degraded Land into Restoration by 2020 and 350 Million Hectares by 2030. Available online: https://www.bonnchallenge (accessed on 18 September 2023).
  81. MINAGRI. Resolución Directoral Ejecutiva N° 067-2020-MINAGRI-DVDIAR-AGRO RURAL-DE: Aprobación del Plan de Acción para el Repoblamiento Forestal con Especies del género cinchona (Árbol de la Quina) 2020–2022. Available online: https://www.gob.pe/institucion/agrorural/normas-legales/2778257-067-2022-midagri-dvdafir-agro-rural-de (accessed on 20 September 2023).
  82. Albán-Castillo, J.; Chilquillo, E.; Melchor-Castro, B.; Arakaki, M.; León, B.; Suni, M. Cinchona L. “Árbol de la Quina”: Repoblamiento y reforestación en el Perú. Rev. Peru. Biol. 2020, 27, 423–426. [Google Scholar] [CrossRef]
  83. Mostacero-Leon, J.; Mejia-Coico, F.; Pelaez-Pelaez, F.; Charcape-Ravelo, M. Especies madereras nativas del norte del perú. Rebiol 1998, 16, 67–78. [Google Scholar]
  84. IUCN. The IUCN Red List of Threatened Species. Available online: https://www.iucnredlist.org/ (accessed on 21 September 2023).
  85. Fajardo, J.; Lessmann, J.; Bonaccorso, E.; Devenish, C.; Muñoz, J. Combined use of systematic conservation planning, species distribution modelling, and connectivity analysis reveals severe conservation gaps in a megadiverse country (Peru). PLoS ONE 2014, 9, e0114367. [Google Scholar] [CrossRef]
  86. IITO. ITTO Guidelines for the Restoration, Management and Rehabilitation of Degraded and Secondary Tropical Forests; Internaitonal Tropical Timber Organization: Yokohama, Japan, 2002. [Google Scholar]
  87. Burnett, K.M.; Ticktin, T.; Bremer, L.L.; Quazi, S.A.; Geslani, C.; Wada, C.A.; Kurashima, N.; Mandle, L.; Pascua, P.; Depraetere, T.; et al. Restoring to the future: Environmental, cultural, and management trade-offs in historical versus hybrid restoration of a highly modified ecosystem. Conserv. Lett. 2019, 12, e12606. [Google Scholar] [CrossRef]
  88. Griscom, B.W.; Adams, J.; Ellis, P.W.; Houghton, R.A.; Lomax, G.; Miteva, D.A.; Schlesinger, W.H.; Shoch, D.; Siikamäki, J.V.; Smith, P.; et al. Natural climate solutions. Proc. Natl. Acad. Sci. USA 2017, 114, 11645–11650. [Google Scholar] [CrossRef]
  89. Dai, X.; Wu, W.; Ji, L.; Tian, S.; Yang, B.; Guan, B.; Wu, D. MaxEnt model-based prediction of potential distributions of Parnassiawightiana (Celastraceae) in China. Biodivers. Data J. 2022, 10, e81073. [Google Scholar] [CrossRef]
  90. Wang, Y.; Zhao, R.; Zhou, X.; Zhang, X.; Zhao, G.; Zhang, F. Prediction of potential distribution areas and priority protected areas of Agastache rugosa based on Maxent model and Marxan model. Front. Plant Sci. 2023, 14, 1200796. [Google Scholar] [CrossRef]
  91. Zhao, X.; Zheng, Y.; Wang, W.; Wang, Z.; Zhang, Q.; Liu, J.; Zhang, C. Habitat Suitability Evaluation of Different Forest Species in Lvliang Mountain by Combining Prior Knowledge and MaxEnt Model. Forests 2023, 14, 438. [Google Scholar] [CrossRef]
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Figure 1. Occurrence data of Cinchona capuli, C. macrocalyx, and C. pubescens in Amazonas, northeastern Peru.
Figure 1. Occurrence data of Cinchona capuli, C. macrocalyx, and C. pubescens in Amazonas, northeastern Peru.
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Figure 2. Optimal number of clusters among bioclimatic variables for modeling the potential distribution of (a) Cinchona capuli, (b) C. macrocalyx y (c) C. pubescens in the department of Amazonas, northeastern Peru.
Figure 2. Optimal number of clusters among bioclimatic variables for modeling the potential distribution of (a) Cinchona capuli, (b) C. macrocalyx y (c) C. pubescens in the department of Amazonas, northeastern Peru.
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Figure 3. Dendrograms illustrating the clusters analysis of bioclimatic variables for modeling the potential distribution of C. capuli (a), C. macrocalyx (b), and C. pubescens (c) in Amazonas, northeastern Peru.
Figure 3. Dendrograms illustrating the clusters analysis of bioclimatic variables for modeling the potential distribution of C. capuli (a), C. macrocalyx (b), and C. pubescens (c) in Amazonas, northeastern Peru.
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Figure 4. Potential distribution areas of C. capuli, C. macrocalyx, and C. pubescens in the department of Amazonas, northeastern Peru.
Figure 4. Potential distribution areas of C. capuli, C. macrocalyx, and C. pubescens in the department of Amazonas, northeastern Peru.
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Figure 5. Potential distribution of three Cinchona species in the department of Amazonas, and their relationship with provinces and ecosystems. Where: (a) potential distribution of C. capuli, (b) potential distribution of C. macrocalyx, (c) potential distribution of C. pubescens and (d) ecosystems present in the department.
Figure 5. Potential distribution of three Cinchona species in the department of Amazonas, and their relationship with provinces and ecosystems. Where: (a) potential distribution of C. capuli, (b) potential distribution of C. macrocalyx, (c) potential distribution of C. pubescens and (d) ecosystems present in the department.
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Figure 6. Relationship between potential distribution areas of Cinchona species and conservation areas (CA) in the department of Amazonas. Where: (a) potential distribution of C. capuli in CA, (b) potential distribution of C. macrocalyx in CA and (c) potential distribution of C. pubescens in CA.
Figure 6. Relationship between potential distribution areas of Cinchona species and conservation areas (CA) in the department of Amazonas. Where: (a) potential distribution of C. capuli in CA, (b) potential distribution of C. macrocalyx in CA and (c) potential distribution of C. pubescens in CA.
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Figure 7. Potential distribution areas of (a) C. capuli, (b) C. macrocalyx, and (c) C. pubescens located within degraded areas in the department of Amazonas. Where: (d) represents the degraded area in the department.
Figure 7. Potential distribution areas of (a) C. capuli, (b) C. macrocalyx, and (c) C. pubescens located within degraded areas in the department of Amazonas. Where: (d) represents the degraded area in the department.
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Table 1. Variables chosen for the distribution model.
Table 1. Variables chosen for the distribution model.
VariableSymbolUnits
1. Environmental variables
1.1. Bioclimatic
Annual Mean Temperaturebio01°C
Mean Diurnal Rangebio02°C
Isothermalitybio03°C
Temperature Seasonalitybio04°C
Max Temperature of Warmest Monthbio05°C
Min Temperature of Warmest Monthbio06°C
Annual Temperature Rangebio07°C
Mean Temperature of Wettest Quarterbio08°C
Mean Temperature of Driest Quarterbio09°C
Mean Temperature of Warmest Quarterbio10°C
Mean Temperature of Coldest Quarterbio11°C
Annual Precipitationbio12Millimeter
Precipitation of Wettest Monthbio13Millimeter
Precipitation of Driest Monthbio14Millimeter
Precipitation Seasonalitybio15Millimeter
Precipitation of Wettest Quarterbio16Millimeter
Precipitation of Driest Quarterbio17Millimeter
Precipitation of Warmest Quarterbio18Millimeter
Precipitation of Coldest Quarterbio19Millimeter
1.2. RadiationradiacMJ m−2 día−1
2. Topographies
Elevation above mean sea leveldemmals
Cardinal orientation of the slopeaspect°
Slope of the terrainslope°
3. Edaphic variables
Bulk density of the fine earth fractionbdodcg/cm3
Clay contentclay%
Volumetric fraction of coarse fragmentscoarsecm3/dm3 (vol %)
Sand contentsand%
Slime contentSilt%
Cation exchange capacityceccmol kg−1
Total nitrogennitrogcg/kg
Organic carbonsocg kg−1
pH in H2OpHpH × 10
Table 2. Variables with major contributions to MaxEnt modelling of the three Cinchona species in Amazonas, Peru.
Table 2. Variables with major contributions to MaxEnt modelling of the three Cinchona species in Amazonas, Peru.
SpecieVariable 1 (%)Variable 2 (%)Variable 3 (%)Total Contribution
C. capulicec (80.2%)bio03 (13.9%)bio04 (5.6%)99.7%
C. macrocalyxcec (69.2%)aspect (18.4%)clay (7.1%)94.7%
C. pubescensbio06 (62.4%)bio14 (20.6%)cec (8.1%)91.1%
Table 3. Performance of the MaxEnt model of the three Cinchona species in the department of Amazonas.
Table 3. Performance of the MaxEnt model of the three Cinchona species in the department of Amazonas.
SpecieC. capuliC. macrocalyxC. pubescens
AUC0.9920.9810.995
Table 4. Potential distribution areas of C. capuli, C. macrocalyx, and C. pubescens that are protected in conservation areas present in the department of Amazonas, northeastern Peru.
Table 4. Potential distribution areas of C. capuli, C. macrocalyx, and C. pubescens that are protected in conservation areas present in the department of Amazonas, northeastern Peru.
SpeciePNA ModalitiesGeographic Area (km2)Potential Area (km2)Total (km2)%
Low%Moderate%High%
C. capuliNS2475.2573.92.9926.581.0713.440.54113.924.60
RCA717.35228.3931.8447.686.654.10.57280.1739.06
PCA1109.25268.7324.23263.1223.72222.0920.02753.8967.96
RA1366.859.150.6700.0000.009.150.67
BZ6668.07348.185.22211.873.18131.211.97691.2610.37
Total 12,336.77928.357.53549.254.45370.843.011848.3914.98
C. macrocalyxNS2475.25120.044.8544.841.8185.573.46250.4510.12
RCA717.35164.1822.8991.2612.7289.0912.42344.5348.03
PCA1109.25235.5621.24259.1823.37471.4142.50963.1586.83
RA1366.8567.774.9613.260.971.250.0982.286.02
BZ6668.07331.544.97214.873.22386.665.80932.5413.99
Total 12,336.77919.097.45623.415.051033.988.382572.9520.86
C. pubescensNS2475.2575.483.0538.541.56140.655.68254.6710.29
RCA717.35199.3127.78114.2315.9292.8312.94406.3756.65
PCA1109.25167.6215.11227.6620.52561.2650.60956.2486.21
RA1366.859.620.702.050.151.030.0812.70.93
BZ6668.07161.852.43153.562.30596.478.95911.8813.68
Total 12,336.77613.884.98536.044.351251.5910.152541.8620.60
Table 5. Areas with recovery potential for conserving and recovering the three species of Cinchona in the department of Amazonas, Peru.
Table 5. Areas with recovery potential for conserving and recovering the three species of Cinchona in the department of Amazonas, Peru.
SpecieDegraded AreasGeographic Area (km2)Potential Area (km2)Total %
Low%Moderate%High%(km2)
C. capuliDepartment of Amazonas10,987.221203.4710.951052.319.581018.429.273274.229.80
C. macrocalyx1232.1511.211059.999.651962.3817.864254.5238.72
C. pubescens1118.5210.18987.988.991719.6915.653826.1934.82
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Coronel-Castro, E.; Meza-Mori, G.; Torres, J.M.C.; Mondragón, E.P.; Cotrina-Sanchez, A.; Oliva Cruz, M.; López, R.S.; Campo Ramos, R.E. Potential Distribution and Identification of Critical Areas for the Preservation and Recovery of Three Species of Cinchona L. (Rubiaceae) in Northeastern Peru. Forests 2024, 15, 321. https://doi.org/10.3390/f15020321

AMA Style

Coronel-Castro E, Meza-Mori G, Torres JMC, Mondragón EP, Cotrina-Sanchez A, Oliva Cruz M, López RS, Campo Ramos RE. Potential Distribution and Identification of Critical Areas for the Preservation and Recovery of Three Species of Cinchona L. (Rubiaceae) in Northeastern Peru. Forests. 2024; 15(2):321. https://doi.org/10.3390/f15020321

Chicago/Turabian Style

Coronel-Castro, Elver, Gerson Meza-Mori, Jose M. Camarena Torres, Elí Pariente Mondragón, Alexander Cotrina-Sanchez, Manuel Oliva Cruz, Rolando Salas López, and Ricardo E. Campo Ramos. 2024. "Potential Distribution and Identification of Critical Areas for the Preservation and Recovery of Three Species of Cinchona L. (Rubiaceae) in Northeastern Peru" Forests 15, no. 2: 321. https://doi.org/10.3390/f15020321

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