Received: 10 October 2023  | Revised: 22 June 2024  | Accepted: 26 July 2024 DOI: 10.1002/ece3.70158 R E S E A R C H A R T I C L E Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100 Elgar Barboza1,2 | Nino Bravo3 | Alexander Cotrina- Sanchez4,5 | Wilian Salazar6 | David Gálvez- Paucar7 | Jhony Gonzales7 | David Saravia6 | Lamberto Valqui- Valqui2,6 | Gloria P. Cárdenas3 | Jimmy Ocaña3 | Juancarlos Cruz-L uis1 | Carlos I. Arbizu6 1Dirección de Supervisión y Monitoreo en las Estaciones Experimentales Agrarias, Instituto Nacional de Innovación Agraria (INIA), Lima, Peru 2Laboratorio de Agrostología, Instituto de Investigación en Ganadería y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas, Peru 3Estación Experimental Agraria Pucallpa, Instituto Nacional de Innovación Agraria (INIA), Pucallpa, Peru 4Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas, Peru 5Department for Innovation in Biological, Agri- Food and Forest Systems, Università Degli Studi Della Tuscia, Viterbo, Italy 6Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Lima, Peru 7Instituto de Investigación en Desarrollo Sostenible y Cambio Climático, Universidad Nacional de Frontera (UNF), Sullana, Peru Correspondence Elgar Barboza, Dirección de Supervisión Abstract y Monitoreo en Las Estaciones The development of anthropic activities and climate change effects impact world- Experimentales Agrarias, Instituto Nacional de Innovación Agraria (INIA), wide species' ecosystems and habitats. Habitats' adequate prediction can be an Lima 15024, Peru. important tool to assess current and future trends. In addition, it allows strate- Email: ebarboza@indes-ces.edu.pe gies development for their conservation. The Neltuma pallida of the forest region in Carlos I. Arbizu, Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional northern Peru, although very significant, has experienced a decline in recent years. de Innovación Agraria (INIA), Lima 15024, The objective of this research is to evaluate the current and future distribution and Peru. Email: carlos.arbizu@untrm.edu.pe conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 vari- Present address Carlos I. Arbizu, Facultad de Ingeniería y ables (bioclimatic, topographic, and soil) were processed and selected to obtain the Ciencias Agrarias, Universidad Nacional current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas, Peru and MaxEnt. The area under the curve values fell within the range of 0.93–0.95, demonstrating a strong predictive capability for both present and future poten- Funding information Peruvian government, Grant/Award tial habitats. The findings indicated that the likely range of habitats for N. pallida Number: CUI 2437731, CUI 2449640 and was shaped by factors such as the average temperature of wettest quarter, maxi- CUI 2487112 mum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd. Ecology and Evolution. 2024;14:e70158. www.ecolevol.org  | 1 of 16 https://doi.org/10.1002/ece3.70158 2 of 16  |     BARBOZA et al. of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of poli- cies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserv- ing and recovering with an ecological restoration approach. K E Y W O R D S biodiversity, coastal dry forest, google earth engine, habitat, MaxEnt T A X O N O M Y C L A S S I F I C A T I O N Biogeography 1  |  INTRODUC TION by improving primary productivity levels (Vining et al., 2022) and re- ducing rural community poverty by 5% in this ecosystem (Pécastaing Neltuma pallida is a tree, 8–20 m in height, belonging to the & Chávez, 2020). Neltuma genus (Subfamily: Caesalpinioideae). It is distrib- Species distribution modeling (SDM) is an important tool for re- uted in arid and dry tropical regions of the Americas (Hughes search because they provide spatial information on the current and et al., 2022). This species is native to arid areas of Peru, Ecuador, future environmental suitability of species (Elith & Leathwick, 2009; and Colombia (Burkart, 1981). In Peru, this species is found in the Mammola et al., 2021; Santini et al., 2021; Wang et al., 2020). Species Equatorial Dry Forest (3.45% of the country's surface area), rep- distribution modeling defines the species–environment relationship resenting 61% of the entire vegetation cover of the dry (Salazar, to estimate the geographical distribution of species under differ- Navarro-C errillo, Ancajima, et al., 2018; Salazar, Navarro- Cerrillo, ent climatic scenarios (Gobeyn et al., 2019). A variety of SDM has Cruz, & Villar, 2018), between the departments of La Libertad, been developed (Climex, Genetic Algorithm for Rule- Set Production Lambayeque, Piura, and Tumbes. It holds economic value as [GARP], BIOCLIM, and MaxEnt); however, comparative studies sug- it is used as firewood and charcoal for fuel in rural communi- gest that the MaxEnt model is the most suitable (Elith et al., 2011; ties (Caycho et al., 2023; OSINFOR, 2018). Additionally, N. pall- Liu et al., 2021; Phillips & Dudík, 2008). In dry forest ecosystems, the ida has ecological value by providing ecosystem services such potential distributions of current and future habitats of species such as controlling water erosion, soil fertility, climate regulation, as Cavanillesia platanifolia, Cordia, Erythrina velutina, Handroanthus and bioremediation (Ambite et al., 2022; Caycho et al., 2023; chrysanthus, Terminalia valverdeae, Euterpe edulis Mart, Prosopis juli- Mokgalaka- Matlala et al., 2009; OSINFOR, 2018; Santos-J allath flora, and Prosopis pallida (now N. juliflora and N. pallida) have been et al., 2012; SERFOR, 2021). This species is severely threatened, studied. Some of these habitats decrease under climate change, and the causes so far are uncertain and likely complex (Caycho while others increase (Aguirre et al., 2017; Leal et al., 2022; Oliveira et al., 2023). In the dry forest of Peru, N. pallida populations are ex- et al., 2018). periencing a decline of up to 49% (SERFOR, 2020); climate change, Neltuma pallida forms extensive forests in northern Peru drought, pests, and diseases have influenced its decline (Salazar, (Zorogastúa et al., 2011). It provides multiple ecosystem benefits as Navarro- Cerrillo, Ancajima, et al., 2018; Salazar, Navarro- Cerrillo, an environment preserver, soil protector and fertilizer, and as a food Cruz, & Villar, 2018; SERFOR, 2021). source for goat farming (Cruzado- Jacinto et al., 2019). In addition, it Climate change can have a significant impact by altering the is used in construction and firewood for populations settled in dry composition of terrestrial ecosystem communities and the perfor- forest ecosystems (Cuentas & Salazar, 2017). However, this species mance of species (Bertrand et al., 2011; Forrest, 2016). It often af- has been reduced by deforestation, urbanization, and agricultural fects the geographic distribution area of endangered species and expansión (Depenthal & Meitzner, 2017). reduces the size of their native habitats, ultimately resulting in a Studies of N. pallida have focused mainly on evaluating its phys- decline in population or even extinction (Anderegg et al., 2019; iological characteristics and its ecological and economic valuation Khanal et al., 2022). However, this will depend on the habitat of (Aguirre & Kvist, 2005; Cuentas Romero, 2015; Espinosa et al., 2012). each species (Bertin, 2008; Meir et al., 2015). The El Niño- Southern Others are related to the insect's identification associated with the Oscillation (ENSO) alters global precipitation patterns, increasing species and its biodiversity (Cruzado- Jacinto et al., 2019; SERFOR temperatures in arid areas with less frequent rainfall under nor- et al., 2022), other researchers evaluated foliar functional traits and mal conditions, and temperatures may be more moderate. El Niño- adaptability to extreme climatic events (Salazar et al., 2021; Salazar, Southern Oscillation indices, Pacific indices, Walker circulation, Navarro-C errillo, Ancajima, et al., 2018; Salazar, Navarro- Cerrillo, and the Humboldt Current are factors that control air temperature Cruz, & Villar, 2018). There have been reports of studies with limited and climate on the northern coast of Peru (Rollenbeck et al., 2015). data regarding the present and future ecological suitability distri- ENSO on the northern coast of Peru has benefited desert vegetation bution of N. pallida. Hence, studying the behavior of its habitat with 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  3 of 16 F I G U R E 1 The study area for Neltuma pallida, along with the boundaries of the different geographical departments and the georeferenced records. respect to the effect of climate change under different scenarios is forests of Peru, elucidating the relationship between the species necessary. and its habitat through response curves derived from key envi- Information on the distribution of N. pallida, spatial arrange- ronmental variables. The primary research objectives included: (1) ment, and climatically suitable habitats under current and future assessing the current and future potential distribution of N. pall- conditions can be assessed by using tools such as QGIS, RStudio, ida in Peru under different climate scenarios, (2) investigating the and MaxEnt (Bushi et al., 2022; Kalboussi & Achour, 2018; Shi influence of environmental factors on the spatial distribution of et al., 2023). Hence, in this study, we aimed to model the present N. pallida habitat, and (3) understanding how climate change will and future potential habitat distribution of N. pallida within the dry impact the future distribution and spatial patterns of N. pallida. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 of 16  |     BARBOZA et al. Addressing these inquiries can not only provide theoretical sup- in the arboreal stratum (Barboza et al., 2022; Caycho et al., 2023; port for strategic planning related to the introduction and cultiva- SERFOR, 2020). tion of N. pallida in Peru but also establish a scientific foundation Figure 2 describes the methodological flowchart to analyze the for its restoration and conservation efforts. current and future spatial distribution of the N. pallida, based on the collection, cutting, and standardization of bioclimatic, topographic, and edaphic variables according to the study area. Likewise, the 2  |  METHODS information on georeferenced presence data of N. pallida was col- lected. Finally, the MaxEnt algorithm was applied to model the cur- 2.1  |  Study area rent and future potential distribution for 2100 using the Model for Interdisciplinary Research on Climate (MIROC 6) and the different The study area consisted of the Equatorial Dry Forest, located in Shared Socioeconomic Pathways (SSP). the geographical departments of La Libertad, Lambayeque, Piura, and Tumbes in northern Peru (Figure 1) with an altitudinal range that varies from 0 to 1500 masl (Salazar et al., 2021). The mean 2.2  |  Source of data for the presence of N. pallida annual precipitation ranges from 100 to 500 mm with the months of highest rainfall from January to March, in turn, the mean an- The 132 data points on the presence of N. pallida, duly georefer- nual temperature varies from 24 to 27°C and is directly correlated enced, were retrieved (longitude, latitude, and altitude) in CSV for- with the intensity of rainfall and the thermal inertia of the Pacific mat, from the fieldwork that was carried out from June 15 to June Ocean (Rollenbeck et al., 2015). The study area presents a flat to 22, 2002 and downloaded from the GBIF only for the study area semi- flat topography with soils originating from eolian or alluvial (https:// www.g bif. org/ , accessed September 24, 2022). Those lack- deposition (Salazar, Navarro- Cerrillo, Cruz, & Villar, 2018). The study ing exact geographic coordinates were corrected through visual area comprises the Sechura desert, agricultural surfaces and forests interpretation in Google Earth Pro (Zhang et al., 2021). Likewise, where Loxopterygium huasango (Hualtaco), Neltuma spp. (Algarrobo), spatial analysis was applied in QGIS v. 3.16 to decrease the impact Bursera graveolens (Palo santo), Eriotheca ruizii (Pasayo), Capparis of spatial autocorrelation, ensuring that each pixel contains a single scabrida (Sapote), and Cordia lutea (Overo) species predominate point of presence (Liu et al., 2021; Yang et al., 2022). F I G U R E 2 Flowchart outlining the methodology to evaluate the spatial modeling of the present and future distribution of Neltuma pallida. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  5 of 16 TA B L E 1 Variables used for current and future modeling of Neltuma pallida in Variables Units Symbols MaxEnt. 1. Bioclimatic Average annual temperature °C bio01 Avearge diurnal range °C bio02 Isothermality bio03 Temperature seasonality °C bio04 Maximum temperature of warmest month °C bio05 Minimum temperature of coldest month °C bio06 Annual temperature range °C bio07 Average temperature of wettest quarter °C bio08 Average temperature of driest quarter °C bio09 Average temperature of warmest quarter °C bio10 Average temperature of coldest quarter °C bio11 Annual precipitation mm bio12 Precipitation of rainiest month mm bio13 Rainfall of driest month mm bio14 Precipitation seasonality mm bio15 Rainfall of wettest quarter mm bio16 Rainfall of driest quarter mm bio17 Precipitation of warmest quarter mm bio18 Precipitation of coldest quarter mm bio19 Minimum temperature °C Tem_min Maximum temperature °C Tem_max Average temperature °C Tem_mean Precipitation mm Prec 2. Topographic Elevation above sea level masl dem Land slope ° slope Flow direction flowd Terrain Roughness Index—TRI TRI Topographical Position Index—TPI TPI 1. Edaphic at pH in H2O pH × 10 pH 0.60 m Soil organic carbon content in fine soil fraction g/kg soc Bulk density of fine soil fraction kg/dm3 bdod Total nitrogen (N) g/kg nitrogen Clay content % clay Sand content % sand Silt content % slime Carbon stock kg/m2 ocs 2.3  |  Environmental variables set (https://w ww. world clim.o rg/ ) and procesing in Google Earth Engine (GEE) (Gorelick et al., 2017). The remaining eigth variables, Environmental factors such as soil, climate, and topography play relating to soil properties, were obtained at 30 arcsecond (~1 km) a key role in the development and distribution of flora (Yang resolution from the SoilGrids 0.5.3 database (http:// soilg rids.o rg) et al., 2022). To address these considerations, a total of 36 vari- using GEE. ables were chosen (Table 1). Twenty- three of these variables The bioclimatic, topographic, and edaphic variables were pro- constituted bioclimatic data for the current period spanning 1970– cessed at 250 m resolution. To determine the importance of each 2000 (Fick & Hijmans, 2017), while terrain-r elated factors, specifi- variable, the Jackknife method was applied (Meza et al., 2022). cally altitude, slope, flow direction, terrain roughness index, and Likewise, Pearson's correlation was employed to overcome col- topographical position index were obtained from digital elevation linearity between the variables (Aidoo et al., 2022; Dormann model (Hennig et al., 2007), downloaded from WorldClim 2.1 data et al., 2013; Leroy et al., 2016; Owens et al., 2013). The “virtual 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 of 16  |     BARBOZA et al. TA B L E 2 Assessment of the species Representation AUC distribution model's performance Current 0.94 (measured by AUC) in both the current MIROC6 SSP1–2.6 SSP2–4.5 SSP3–7.0 SSP5–8.5 environmental conditions and various climate change scenarios. 2030s 0.94 0.93 0.94 0.93 2050s 0.95 0.93 0.93 0.94 2070s 0.93 0.95 0.93 0.93 2090s 0.94 0.93 0.94 0.93 Abbreviation: AUC, area under the curve. F I G U R E 3 Reliability test of the distribution model based on area under the curve (AUC) and mean sensitivity versus specificity for Neltuma pallida. species” package in R Studio was applied to process them (Leroy 2041–2060, 2061–2080, 2081–2100 (denoted years 2030, 2050, et al., 2016), and the variables were grouped by clustering, 2070, and 2090) from Worclim (https:// www. world clim.o rg/ data/ with Pearson's correlations greater than 0.80 (Cotrina, Rojas, cmip6/ cmip6 climat e. html). et al., 2021; Wei et al., 2018). The establishment of a high- performance model was carried out with the selection of 10 vari- ables (bio3, bio4, bio6, bio8, bio9, bio12, bio15, altitude, slope, 2.5  |  Model execution flow direction). The biogeographic distribution model for N. pallida was con- structed using MaxEnt. This model assessed the likelihood of po- 2.4  |  Future scenarios of climate change tential distribution for each species based on the presence data (locations) (OSINFOR, 2013, 2016). To validate this model, 75% We use future bioclimatic data from the sixth version of the of randomly selected presence data points were used for training, MIROC 6 (Tatebe et al., 2019). It provides information related to cli- while the remaining 25% were set aside for validation, as outlined mate predictions from seasonal to decadal, future climate projec- by Liu et al. (2021). tions, being widely used at different scales (Coulibaly et al., 2023; The resulting model was subjected to validation based on the Hirabayashi et al., 2022; Kunwar et al., 2023; SERFOR, 2020; Sharma area under the curve (AUC), which was calculated using the receiver- et al., 2018). Climate data were obtained from the Coupled Model operating characteristic (ROC) method and categorized into levels Intercomparison Project (CMIP) multimodel under the six least and ranging from invalid (<0.6) to excellent (>0.9) (Peterson et al., 2008, most extreme shared socioeconomic pathways (SSP) (SSP1–2.6, 2011). To create a model of assessed species, the logistic output format SSP2–4.5, SSP3–7.0, SSP4–8.5), for projections to 2021–2040, was used, generating a plot with continuous values ranging from 0 to 1. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  7 of 16 F I G U R E 4 Relative contributions of the variables to the MaxEnt model to assess the potential habitat distribution of Neltuma pallida. F I G U R E 5 Analysis of variable flowd 0.5 contributions to the MaxEnt model to assess the potential habitat distribution of bio18 0.7 Neltuma pallida. bio02 0.7 preci 1.7 bio05 4.4 bio19 4.8 dem 6.4 soc 6.9 bio08 27 bio14 46.8 0 10 20 30 40 50 Contribution (%) This plot was subsequently reclassified into four habitat categories: (1) were reclassified into four habitats suitable categories: highly high potential (>0.6), (2) moderate (0.4–0.6), (3) low potential (0.2–0.4), (0.5 ≤ p ≤ 1.0), moderately (0.3 ≤ p < .5), little (0.1 ≤ p < .3), and unsuit- and (4) no potential (0.5–0.5) (Meza et al., 2022; Zhang et al., 2021). able (p < .1). 2.6  |  Suitable habitat classification 3  |  RESULTS To improve the performance of the model, five replications of Table 2 shows the results of the statistical method of the ROC curve cross-v alidation were done (Liu et al., 2021). Subsequently, ac- that allowed to compare the average sensitivity with the specific- cording to the results of habitat levels for each scenario, the maps ity of N. pallida in current and future conditions. For the current Variables 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 of 16  |     BARBOZA et al. F I G U R E 6 Current distribution of Neltuma pallida. distribution, average of the five repetitions reported an AUC of 0.94, temperature during the wettest quarter (bio08), the maximum temper- and a standard deviation is 0.014 (Figure 3), while, for future sce- ature in the warmest month (bio05), altitude (dem), precipitation, and narios, the AUC ranged between 0.93 and 0.95 (Table 2). precipitation during the driest month (bio14), as illustrated in Figure 4. The modeling reported that the relative contributions of the two variables, the bio14 and the bio08, were the most influenced on 3.1  |  Precision model and current distribution the distribution of N. pallida, which explained 46.8% and 27% of the species habitat distribution, respectively. On the other hand, the The Jackknife test results indicated that the primary factors influ- variables of least contribution were the bio02, the bio18, and the encing the potential habitat distribution of N. pallida were the mean flow direction with 0.7%, 0.7%, and 0.5%, respectively (Figure 5). 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  9 of 16 TA B L E 3 Current distribution range of Neltuma pallida and the percentage of change in various scenarios in northern Peru. Unsuitable Low Moderate High Climatic scenarios Períod km2 % km2 % km2 % km2 % Current 1970–2000 42,290.08 79.39 7842.13 9.12 6910.93 8.03 2975.70 3.46 2021–2040 (2030s) SSP1–2.6 38,272.20 74.72 (−4.67) 9048.63 10.52 (1.40) 7658.31 8.90 (0.87) 5039.71 5.86 (2.40) SSP2–4.5 38,925.10 75.48 (−3.91) 8506.20 9.89 (0.77) 7942.07 9.23 (1.20) 4645.47 5.40 (1.94) SSP3–7.0 38,170.53 74.60 (−4.79) 9300.23 10.81 (1.69) 7826.36 9.10 (1.06) 4721.73 5.49 (2.03) SSP5–8.5 36,867.35 73.09 (−6.30) 10,995.19 12.78 (3.66) 7479.46 8.69 (0.66) 4676.84 5.44 (1.98) 2041–2060 (2050s) SSP1–2.6 38,706.00 75.23 (−4.17) 9122.40 10.60 (1.49) 7397.95 8.60 (0.57) 4792.50 5.57 (2.11) SSP2–4.5 38,224.24 74.67 (−4.73) 9584.68 11.14 (2.03) 7640.12 8.88 (0.85) 4569.80 5.31 (1.85) SSP3–7.0 37,649.37 74.00 (−5.39) 9067.57 10.54 (1.42) 8645.90 10.05–(2.02) 4656.01 5.41 (1.95) SSP5–8.5 38,184.89 74.62 (−4.77) 9759.63 11.34 (2.23) 7610.87 8.85 (0.81) 4463.46 5.19 (1.73) 2061–2080 (2070s) SSP1–2.6 39,442.83 76.08 (−3.31) 8085.31 9.40 (0.28) 8201.18 9.53 (1.50) 4289.53 4.99 (1.53) SSP2–4.5 39,552.81 76.21(−3.18) 8544.53 9.93 (0.82) 7732.26 8.99 (0.95) 4189.25 4.87 (1.41) SSP3–7.0 42,457.93 79.59 (0.20) 7793.18 9.06 (0.06) 6179.67 7.18 (0.85) 3588.08 4.17 (0.71) SSP5–8.5 39,274.54 75.89 (−3.51) 8743.56 10.16 (1.05) 7641.68 8.88 (0.85) 4359.07 5.07 (1.61) 2081–2100 (2090s) SSP1–2.6 38,911.73 75.47 (−3.93) 9193.27 10.69 (1.57) 7107.63 8.26 (0.23) 4806.23 5.59 (2.13) SSP2–4.5 36,888.46 73.11 (−6.28) 10,026.54 11.65 (2.54) 8346.75 9.70 (1.67) 4757.10 5.53 (2.07) SSP3–7.0 37,321.61 73.62 (−5.78) 9425.35 10.96 (1.84) 7959.02 9.25 (1.22) 5312.87 6.18 (2.72) SSP5–8.5 39,487.01 76.13 (−3.26) 8229.17 9.57 (0.45) 7787.74 9.05 (1.02) 4514.93 5.25 (1.79) The current potential distribution of N. pallida was located results showed that habitat characteristics are relatively consistent throughout the study area from north to south, stretching the across the four SSPs, in the different forecast years. The high future Tumbes, Piura, Lambayeque, and La Libertad departments potential is in the central part of the geographical departaments of (Figure 6). The “highly suitable” potential habitat represented Tumbes, Piura, and Lambayeque, distributed from north to south. 3.45% (68,304.32 km2), the “moderately suitable” hábitat 8.03% The potential distribution of the N. pallida current habitat over- (6910.93 km2), the “low” habitat 9.12% (7842.13 km2), and the “not lapped with climate change scenarios to obtain characteristics of suitable” habitat 79.39% (68,304.32 km2). habitat loss, gain, or permanence spatially (Figure 8). The habitat areas of N. pallida showed a trend of area gain by 2100. The areas of surface gain were located mainly in the southwest, center-w est, 3.2  |  Potential distribution of N. pallida under and north of the study area. On the other hand, the loss zones were future climate scenarios located mainly in the center and center- south of the study area. In addition, the most stable zones under climate change scenarios are The unsuitable areas in the Equatorial Dry Forest of Peru for N. pal- located in the central part of the dry forest. lida are likely to be reduced in the coming years to become areas of “low,” “medium,” and “high” potential habitats. Table 3 shows that the “low” potential habitat in the future scenarios will increase by 4  |  DISCUSSION 12.78, 11.34, 10.16, and 11.65% by 2030 (SSP5-8 .5), 2050 (SSP5- 8.5), 2070 (SSP5-8 .5), and 2090 (SSP2-4 .5), respectively. The performance results of MaxEnt indicated high precision and reli- The “moderate” potential habitat distribution reported by 2030s ability, as the AUC values ranged between 0.93 and 0.95. If the value will increase with respect to current conditions by 9.10, 10.05, exceeds this threshold, it represents exceptionally high predictive 9.53, and 9.70% according to (SSP3–7.0), 2050s (SSP3–7.0), 2070s accuracy of the model (Swets, 1988). The current zones with “high,” (SSP1–2.6), and 2090s (SSP2–4.5), respectively. In turn, the spatial “moderate,” and “low” potential are distributed in the central areas distribution of the “high” potential habitat showed similar conditions of the departments of Tumbes, Piura, and Lambayeque, as well as in to the previous ones, with an area increase of 5.86, 5.57, 5.07, and the northern part of La Libertad, central to the study area. This study 6.18% by 2030s (SSP1–2.6), 2050s (SSP1–2.6), 2070s (SSP5–8.5), reports an increase in the surface areas from current to future condi- and 2090s (SSP3–7.0), respectively. tions of N. pallida. The results are similar to those reported by Oliveira The potential current distribution of N. pallida overlapped with et al. (2018), who analyzed the dynamics of climatic niches of Prosopis the future distribution under climate change scenarios (Figure 7), an juliflora (now N. juliflora) and Prosopis pallida (now N. pallida) in semi- habitat increase was observed by 2100 at all potentiality levels. The arid areas of Brazil. A similar trend is observed in Ethiopia, where the 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 10 of 16  |     BARBOZA et al. F I G U R E 7 Future distribution of suitable areas for Neltuma pallida under various climate change scenarios. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  11 of 16 F I G U R E 8 Distribution of potential habitat loss, gain, or permanence of Neltuma pallida habitat under climate change scenarios. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 12 of 16  |     BARBOZA et al. F I G U R E 9 Distribution of the conserved area of Neltuma pallida under different climate change scenarios, according to habitat levels (a) 2030s, (b) 2050s, (c) 2070s, and (d) 2090s. current and potential distribution of N. juliflora was mapped, report- Within the arid forests of Peru, there is a variety of species, high- ing an increase in surface area (Wakie et al., 2014). The use of satel- lighting the need to adopt effective strategies aimed at their moni- lite images such as Landsat 8 and Sentinel- 2 has also contributed toring and preservation, as emphasized by Cotrina, Bandopadhyay, to mapping N. juliflora and demonstraated its long- term surface in- et al. (2021). N. pallida is one of the main species in this ecosystem; crease under climate change scenarios (Ahmed et al., 2021; Rembold however, it is threatened by anthropogenic activities such as defor- et al., 2015). However, other species of dry forests could be affected estation, land use change, and logging (Nieuwstadt & Sheil, 2005; in future scenarios, such as Anadenanthera colubrina, Aspidosperma Qarallah et al., 2021). Efforts are currently underway to conserve the pyrifolium, and Myracrodruon urundeuva (Rodrigues et al., 2015), as biodiversity of the ecosystem and N. pallida individuals through the well as cacti (Cavalcante & Sampaio, 2022), Calycophyllum multiflo- creation of protected areas (725.69 km2, Figure 9) (SERNAP, 2023). rum (Alabar et al., 2022), Albizia multiflora, Ceiba trichistandra, and The conservation of these species is vital because they provide eco- Cochlospermum vitifolium (Manchego et al., 2017). system services that contribute to people's economy. Therefore, it The increase in the habitat distribution of N. pallida in the study is essential to implement measures that help mitigate the species' area could be related to its adaptability to extremely dry and wet population decline and ensure its sustainable use (INIA, 2020). conditions. This climatic event creates two alternative states that This research determined the current and future habitat distribu- promote survival and growth strategies, respectively (Holmgren tion areas of N. pallida. Based on this, potential species conservation et al., 2001; Salazar, Navarro- Cerrillo, Cruz, & Villar, 2018). Unlike areas were identified, and conservation plans were established (Li other species of dry forests, these changes have allowed the species et al., 2020). Additionally, future studies could utilize other climatic to develop physiological and morphological adaptations to survive models, such as the fusion of methodologies like Analytic Hierarchy in years of drought, grow after floods, and in saline soils. This adapt- Process (AHP), GEE, and GARP (Cotrina, Bandopadhyay, et al., 2021; ability appears to be strongly associated with the occurrence of El Padalia et al., 2014; Rojas- Briceño et al., 2022; Zhang et al., 2021). Niño on the northern coast of Peru (Palacios et al., 2012; Salazar In this research, we integrated tools like QGIS, Rstudio, GEE, and et al., 2021). However, N. pallida is considered an invasive species MaxEnt to identify potential distribution zones of N. pallida under in some areas such as Australia, Africa, Ethiopia, Brazil, and Pacific current and future conditions, which yielded favorable results. Islands (Ahmed et al., 2021; Gallaher & Merlin, 2010; Rembold Although MaxEnt is one of the most used models, it has certain dis- et al., 2015; Sintayehu et al., 2020). The decline in N. pallida condi- advantages in considering climatic impact, topographical, and envi- tions may be related to pest and disease attacks, forest fires, and ronmental factors (nonbiological factors), in areas heavily impacted indiscriminate logging. by human activities (Wang et al., 2020). Therefore, this model only 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  13 of 16 represents potential habitat distribution rather than real distribution ACKNOWLEDG MENTS (Liu et al., 2021). A detailed and predictive analysis of the future po- The authors thank the members of our laboratory for their assis- tential habitat distribution of species could be crucial for developing tance during the execution of this research project. C.I.A. thanks conservation programs and ensuring the species' survival probability Vicerrectorado de Investigación of UNTRM. against the effects of climate change (Khanal et al., 2022). FUNDING INFORMATION This research was funded by the following three research projects 5  |  CONCLUSIONS of the Peruvian Government: (i) “Creación del servicio de agricultura de precisión en los Departamentos de Lambayeque, Huancavelica, The area of N. pallida in northern Peru covers approximately Ucayali y San Martín 4 Departamentos,” (ii) “Mejoramiento de los 17,728.76 km2 distributed among the geographical departments of servicios de investigación y transferencia tecnológica en el manejo Tumbes, Piura, Lambayeque, and La Libertad. Also, the area con- y recuperación de suelos agrícolas degradados y aguas para riego served through protected natural areas represents 2.23% of the en la pequeña y mediana agricultura en los departamentos de Lima, distribution of this species. The critical factors influencing the dis- Áncash, San Martín, Cajamarca, Lambayeque, Junín, Ayacucho, tribution of N. pallida are related to the average temperature dur- Arequipa, Puno y Ucayali,” and (iii) Creación del Servicio de labora- ing the wettest quarter, the maximum temperature in the warmest torio de Biología Molecular para la Investigación en la Universidad month, altitude, precipitation, and precipitation levels during the Nacional de Frontera–Distrito de Sullana, with grant numbers CUI driest month. In turn, under climate change scenarios to 2100, the 2449640, CUI 2487112, and CUI 2437731, respectively. potential “high” distribution of the species showed an increase rang- ing from 3.56% to 5.25%, especially in the SSP5–8.5 scenario. These CONFLIC T OF INTERE S T S TATEMENT current and future distribution maps may help identify new areas for The authors declare no conflict of interest or competing interests. the design of conservation and population recovery strategies with a focus on ecological restoration as these areas are expected to have DATA AVAIL ABILIT Y S TATEMENT suitable conditions for the development of the species. Data for the current manuscript is available at https:// datad ryad. org/ stash/ share/ 0vbaK Z1FTU F- alH36 dpQQ7 gVQrD w0g_ mqrdM AUTHOR CONTRIBUTIONS dO8s9bc. Elgar Barboza: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (equal); methodology (equal); software ORCID (equal); visualization (equal); writing – original draft (lead); writing Carlos I. Arbizu https://orcid.org/0000-0002-0769-5672 – review and editing (lead). Nino Bravo: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); R E FE R E N C E S methodology (equal); writing – review and editing (equal). Alexander Aguirre, N., Eguiguren, P., Maita, J., Ojeda, T., Sanamiego, N., Furniss, M., Cotrina- Sanchez: Data curation (equal); formal analysis (equal); in- & Aguirre, Z. (2017). Potential impacts to dry forest species distri- vestigation (equal); methodology (equal); writing – review and ed- bution under two climate change scenarios in southern Ecuador. Neotropical Biodiversity, 3(1), 18–29. https://d oi. org/ 10.1 080/ iting (equal). Wilian Salazar: Formal analysis (equal); investigation 23766 808. 2016. 1258867 (equal); methodology (equal); supervision (equal); writing – review Aguirre, Z., & Kvist, L. (2005). Floristic composition and conservation sta- and editing (equal). David Gálvez-P aucar: Conceptualization (equal); tus of the dry forests in Ecuador. Lyonia, 8(2), 41–67. investigation (equal); visualization (equal); writing – review and edit- Ahmed, N., Atzberger, C., & Zewdie, W. (2021). Species distribution modelling performance and its implication for Sentinel- 2-b ased ing (equal). Jhony Gonzales: Conceptualization (equal); investigation prediction of invasive Prosopis juliflora in lower Awash River basin, (equal); supervision (equal); visualization (equal); writing – review Ethiopia. Ecological Processes, 10(8), 1–16. https:// doi. org/ 10. 1186/ and editing (equal). David Saravia: Investigation (equal); methodol- s1371 7-0 21-0 0285- 6 ogy (equal); visualization (equal); writing – review and editing (equal). Aidoo, O. F., Souza, P. G. C., da Silva, R. S., Júnior, P. A. S., Picanço, M. C., Osei- Owusu, J., Sétamou, M., Ekesi, S., & Borgemeister, C. Lamberto Valqui- Valqui: Investigation (equal); methodology (equal); (2022). A machine learning algorithm-b ased approach (MaxEnt) visualization (equal); writing – review and editing (equal). Gloria P. for predicting invasive potential of Trioza erytreae on a global Cárdenas: Investigation (equal); methodology (equal); writing – re- scale. Ecological Informatics, 71, 1–7. https://d oi. org/ 10. 1016/j. view and editing (equal). Jimmy Ocaña: Investigation (equal); meth- ecoinf. 2022.1 01792 Alabar, F., Politi, N., Názaro, P., Amoroso, M., & Rivera, L. (2022). Changes odology (equal); writing – review and editing (equal). Juancarlos in the potential distribution of valuable tree species based on their Cruz- Luis: Funding acquisition (equal); project administration (equal); regeneration in the neotropical seasonal dry forest of north- supervision (equal); visualization (equal); writing – review and editing western Argentina. Environmental Conservation, 49(2), 83–89. (equal). Carlos I. Arbizu: Conceptualization (equal); funding acquisi- https:// doi.o rg/1 0.1 017/ S0376 89292 2000133 Ambite, S., Ferrero, M. E., Piraino, S., Badagian, J., Muñoz, A. A., Aguilera- tion (equal); project administration (equal); supervision (equal); visu- Betti, I., Gamazo, P., Roig, F. A., & Lucas, C. (2022). Prosopis L. alization (equal); writing – review and editing (equal). woody growth in relation to hydrology in South America: A review. 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14 of 16  |     BARBOZA et al. Dendrochronologia, 76, 126017. https:// doi. org/ 10.1 016/j. dendro. Depenthal, J., & Meitzner, L. S. (2017). Community use and knowledge 2022. 126017 of Algarrobo (Prosopis pallida) and implications for Peruvian dry Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L., & Trugman, A. T. (2019). Forest conservation. Revista de Ciencias Ambientales, 52(1), 49. Widespread drought-i nduced tree mortality at dry range edges in- https:// doi.o rg/1 0. 15359/ rca.5 2- 1. 3 dicates that climate stress exceeds species' compensating mecha- Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, nisms. Global Change Biology, 25(11), 3793–3802. https://d oi.o rg/ G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., 10. 1111/g cb.1 4771 Münkemüller, T., Mcclean, C., Osborne, P. E., Reineking, B., Barboza, E., Salazar, W., Gálvez- Paucar, D., Valqui- Valqui, L., Saravia, Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). D., Gonzales, J., Aldana, W., Vásquez, H. V., & Arbizu, C. I. (2022). Collinearity: A review of methods to deal with it and a simula- Cover and land use changes in the dry forest of tumbes (peru) using tion study evaluating their performance. Ecography, 36(1), 27–46. sentinel- 2 and google earth engine data. Environmental Sciences https:// doi. org/ 10. 1111/j. 1600- 0587. 2012. 07348. x Proceedings, 22, 2. https://d oi. org/ 10. 3390/i ecf20 22- 13095 Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological Bertin, R. I. (2008). Plant phenology and distribution in relation to re- explanation and prediction across space and time. Annual Review of cent climate change. Journal of the Torrey Botanical Society, 135(1), Ecology, Evolution, and Systematics, 40, 677–697. https:// doi.o rg/ 10. 126–146. https:// doi. org/ 10. 3159/ 07- RP- 035R. 1 1146/ annur ev. ecols ys. 110308.1 20159 Bertrand, R., Lenoir, J., Piedallu, C., Dillon, G. R., de Ruffray, P., Vidal, C., Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. Pierrat, J. C., & Gégout, J. C. (2011). Changes in plant community (2011). A statistical explanation of MaxEnt for ecologists. Diversity composition lag behind climate warming in lowland forests. Nature, and Distributions, 17(1), 43–57. https://d oi. org/1 0. 1111/j. 1472- 479(7374), 517–520. https:// doi. org/1 0.1 038/ nature 10548 4642.2 010. 00725. x Burkart, A. (1981). A monograph of the genus prosopis (Leguminosae Espinosa, C. I., de la Cruz, M., Luzuriaga, A. L., & Escudero, A. (2012). subfam. Mimosoideae). Journal of the Arnold Arboretum, 57(4), Bosques tropicales secos de la región Pacífico Ecuatorial: diversi- 450–525. dad, estructura, funcionamiento e implicaciones para la conser- Bushi, D., Mahato, R., Nimasow, O. D., & Nimasow, G. (2022). MaxEnt- vación. Ecosistemas: Revista Cietifica y Tecnica de Ecologia y Medio based prediction of the potential invasion of Lantana camara L. Under Ambiente, 21(1–2), 167–179. climate change scenarios in Arunachal Pradesh. Acta Ecologica Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1- km spatial reso- Sinica. https:// doi. org/ 10.1 016/j.c hnaes. 2022.0 8.0 04 lution climate surfaces for global land areas. International Journal of Cavalcante, A. M. B., & Sampaio, A. C. P. (2022). Modeling the poten- Climatology, 37(12), 4302–4315. https://d oi. org/ 10. 1002/ joc. 5086 tial distribution of cacti under climate change scenarios in the Forrest, J. R. (2016). Complex responses of insect phenology to climate largest tropical dry forest region in South America. Journal of Arid change. Current Opinion in Insect Science, 17, 49–54. https://d oi. org/ Environments, 200, 104725. https:// doi. org/1 0. 1016/j. jaride nv. 10.1 016/j. cois. 2016.0 7. 002 2022. 104725 Gallaher, T., & Merlin, M. (2010). Biology and impacts of Pacific Island in- Caycho, E., La Torre, R., & Orjeda, G. (2023). Assembly, annotation and vasive species. 6. Prosopis pallida and Prosopis juliflora (Algarroba, analysis of the chloroplast genome of the Algarrobo tree Neltuma Mesquite, Kiawe) (Fabaceae). Pacific Science, 64(4), 489–526. pallida (subfamily: Caesalpinioideae). BMC Plant Biology, 23(1), 1–19. https:// doi. org/ 10.2 984/ 64.4. 489 https:// doi.o rg/ 10. 1186/ s1287 0-0 23- 04581- 5 Gobeyn, S., Mouton, A. M., Cord, A. F., Kaim, A., Volk, M., & Goethals, Cotrina, A., Bandopadhyay, S., Rojas Briceño, N. B., Banerjee, P., Torres P. L. M. (2019). Evolutionary algorithms for species distribution Guzmán, C., & Oliva, M. (2021). Peruvian Amazon disappearing: modelling: A review in the context of machine learning. Ecological Transformation of protected areas during the last two decades (2001– Modelling, 392, 179–195. https:// doi. org/ 10. 1016/j.e colm odel. 2019) and potential future deforestation modelling using cloud com- 2018. 11.0 13 puting and MaxEnt approach. Journal for Nature Conservation, 64, Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, 126081. https:// doi. org/ 10. 1016/j.j nc. 2021. 126081 R. (2017). Google earth engine: Planetary- scale geospatial analy- Cotrina, A., Rojas, N. B., Bandopadhyay, S., Ghosh, S., Torres Guzmán, C., sis for everyone. Remote Sensing of Environment, 202(2016), 18–27. Oliva, M., Guzman, B. K., & Salas López, R. (2021). Biogeographic https:// doi. org/1 0. 1016/j. rse. 2017.0 6.0 31 distribution of Cedrela spp. genus in Peru using maxent modeling: Hennig, T. A., Kretsch, J. L., Pessagno, C. J., Salamonowicz, P. H., & Stein, A conservation and restoration approach. Diversity, 13(6), 261. W. L. (2007). The shuttle radar topography mission. Reviews of https:// doi.o rg/1 0. 3390/d 13060 261 Geophysics, 45, 1–33. https://d oi. org/ 10.1 029/2 005R G000183 Coulibaly, A., Avakoudjo, H. G. G., Idohou, R., Vodounnon, E. J., Diallo, Hirabayashi, K., Murch, S. J., & Erland, L. A. E. (2022). Predicted impacts S., & Cherif, M. (2023). Impact of climate change on the distribution of climate change on wild and commercial berry habitats will have of Bombax costatum Pellegr. & Vuillet in Mali, West Africa. Trees, food security, conservation and agricultural implications. Science of Forests and People, 11, 100359. https:// doi. org/ 10. 1016/j. tfp. 2022. the Total Environment, 845, 157341. https:// doi. org/ 10. 1016/j.s cito 100359 tenv.2 022. 157341 Cruzado- Jacinto, L., Chávez-V illavicencio, C., & Charcape-R avelo, M. Holmgren, M., Scheffer, M., Ezcurra, E., Gutiérrez, J. R., & Mohren, G. M. (2019). Use and selection of the aerial parts of the carob tree J. (2001). El Niño effects on the dynamics of terrestrial ecosystems. Prosopis pallida (Fabaceae) by reptiles, birds and mammals in Trends in Ecology & Evolution, 16(2), 89–94. https:// doi. org/ 10. 1016/ Sechura (Piura- Peru). Revista Peruana de Biología, 26(1), 81–86. S0169- 5347(00) 02052 - 8 https:// doi.o rg/1 0.1 5381/r pb. v26i1. 15417 Hughes, C. E., Ringelberg, J. J., Lewis, G. P., & Catalano, S. A. Cuentas, M. A., & Salazar, A. Í. (2017). De la especie al ecosistema; del (2022). Disintegration of the genus Prosopis L. (Leguminosae, ecosistema a la sociedad: revalorizando el algarrobo (ProsoPis Caesalpinioideae, mimosoid clade). PhytoKeys, 205, 147–189. Pallida) y el reto de su conservación en Lambayeque y en la costa https://d oi. org/ 10.3 897/ PHYTOK EYS. 205. 75379 norte del Perú. Espacio y Desarrollo, 159(30), 129–159. https://d oi. INIA. (2020). Manual técnico para la conservación y propagación de org/ 10.1 8800/e spaci oyde sarrol lo.2 01702. 006 algarrobo (Prosopis spp.). Available online: http:// repos itorio.i nia. Cuentas Romero, M. A. (2015). El uso del espacio natural para el desar- gob. pe/h andle/2 0.5 00. 12955/1 197 Accessed on 14 April 2024. rollo del territorio: los bosques secos de algarrobo para las comuni- Kalboussi, M., & Achour, H. (2018). Modelling the spatial distribution dades rurales en Lambayeque, 1985–2015. Investiga Territorios, (2), of snake species in northwestern Tunisia using maximum entropy 105–118. (maxent) and geographic information system (GIS). Journal of 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License BARBOZA et al.     |  15 of 16 Forestry Research, 29(1), 233–245. https:// doi. org/ 10. 1007/s 1167 Peruana: Serie Técnica No 4. Lima. Available online. http:// www. 6- 017- 0436-1 osinf or. gob. pe/ portal/ data/ desta cado/ adjun to/ model amien to_ Khanal, S., Timilsina, R., Behroozian, M., Peterson, A. T., Poudel, M., nichos_ ecolog icos.p df Accessed on 14 April 2024. Alwar, M. S. S., Wijewickrama, T., & Osorio- Olvera, L. (2022). OSINFOR. (2016). Modelamiento de la distribución potencial de 18 espe- Potential impact of climate change on the distribution and conser- cies forestales en el departamento de Loreto. Lima. Available online. vation status of Pterocarpus marsupium, a near threatened south https:// www. gob. pe/ insti tucion/ osinf or/ infor mes- publi cacio nes/ Asian medicinal tree species. Ecological Informatics, 70, 101722. 83236 1- model amien to- de- la- distr ibuci on- poten cial- de- 18- espec https:// doi.o rg/ 10. 1016/j. ecoinf. 2022.1 01722 ies-f orest ales -e n- el-d epar tamen to- de- loreto Accessed on 14 April Kunwar, R. M., Thapa- Magar, K. B., Subedi, S. C., Kutal, D. H., Baral, B., 2024. Joshi, N. R., Adhikari, B., Upadhyaya, K. S., Thapa- Magar, S., Ansari, OSINFOR. (2018). Aprovechamiento forestal maderable en bosques secos A. S., Thapa, G. J., & Bhandari, A. R. (2023). Distribution of import- en el norte del Peru. Lima. Available online. https://w ww.o sinf or. gob. ant medicinal plant species in Nepal under past, present, and future pe/ publi cacio nes/ aprov echam iento - fores tal- mader able- en- bosqu climatic conditions. Ecological Indicators, 146, 109879. https:// doi. es-s ecos- e n- el-n orte- d el-p eru/ Accessed on 14 April 2024. org/1 0.1 016/j.e coli nd. 2023.1 09879 Owens, H. L., Campbell, L. P., Dornak, L. L., Saupe, E. E., Barve, N., Leal, A., Benchimol, M., Costa, H. C. M., Faria, D., & Cazetta, E. (2022). Soberón, J., Ingenloff, K., Lira-N oriega, A., Hensz, C. M., Myers, C. Impacts of landscape- scale forest loss and a dry event on the de- E., & Peterson, A. T. (2013). Constraints on interpretation of eco- mographic structure of the endangered palm Euterpe edulis Mart. logical niche models by limited environmental ranges on calibration In the Atlantic Forest. Frontiers in Forests and Global Change, 5, 1–11. areas. Ecological Modelling, 263, 10–18. https:// doi. org/ 10.1 016/j. https:// doi. org/ 10.3 389/f fgc. 2022. 909901 ecolm odel. 2013.0 4. 011 Leroy, B., Meynard, C. N., Bellard, C., & Courchamp, F. (2016). Virtualspecies, Padalia, H., Srivastava, V., & Kushwaha, S. P. S. (2014). Modeling potential an R package to generate virtual species distributions. Ecography, invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. 39(6), 599–607. https://d oi.o rg/ 10. 1111/ ecog. 01388 In India: Comparison of MaxEnt and GARP. Ecological Informatics, Li, P., Zhu, W., Xie, Z., & Qiao, K. (2020). Integration of multiple climate 22, 36–43. https://d oi. org/1 0.1 016/j. ecoinf. 2014. 04. 002 models to predict range shifts and identify management priorities Palacios, R. A., Burghardt, A. D., Frías- Hernández, J. T., Olalde-P ortugal, of the endangered Taxus wallichiana in the Himalaya–Hengduan V., Grados, N., Alban, L., & Martínez-d e la Vega, O. (2012). Mountain region. Journal of Forestry Research, 31(6), 2255–2272. Comparative study (AFLP and morphology) of three species of https:// doi. org/ 10.1 007/s 1167 6- 019-0 1009- 5 Prosopis of the section Algarobia: P. Juliflora, P. Pallida, and P. Liu, L., Guan, L., Zhao, H., Huang, Y., Mou, Q., Liu, K., Chen, T., Wang, X., Limensis. Evidence for resolution of the “P. Pallida- P. Juliflora com- Zhang, Y., Wei, B., & Hu, J. (2021). Modeling habitat suitability of plex”. Plant Systematics and Evolution, 298(1), 165–171. https:// doi. Houttuynia cordata Thunb (Ceercao) using MaxEnt under climate org/ 10.1 007/ s00606 -0 11-0 535- y change in China. Ecological Informatics, 63, 101324. https:// doi.o rg/ Pécastaing, N., & Chávez, C. (2020). The impact of El Niño phenome- 10. 1016/j.e coinf. 2021. 101324 non on dry forest- dependent communities' welfare in the northern Mammola, S., Pétillon, J., Hacala, A., Monsimet, J., Marti, S. L., Cardoso, coast of Peru. Ecological Economics, 178, 106820. https:// doi. org/ P., & Lafage, D. (2021). Challenges and opportunities of species 10. 1016/j.e cole con. 2020.1 06820 distribution modelling of terrestrial arthropod predators. Diversity Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver op- and Distributions, 27(12), 2596–2614. https:// doi.o rg/1 0.1 111/ ddi. erating characteristic analysis applications in ecological niche mod- 13434 eling. Ecological Modelling, 213(1), 63–72. https:// doi.o rg/1 0. 1016/j. Manchego, C. E., Hildebrandt, P., Cueva, J., Espinosa, C. I., Stimm, B., & ecolm odel.2 007. 11. 008 Günter, S. (2017). Climate change versus deforestation: Implications Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez- for tree species distribution in the dry forests of southern Ecuador. Meyer, E., Nakamura, M., & Araújo, M. B. (2011). Ecological niches PLoS One, 12(12), 1–19. https:// doi.o rg/ 10.1 371/ journa l. pone. and geographic distributions (MPB- 49) (p. 328). Princeton University 0190092 Press. https:// doi. org/1 0.1 515/9 7814 00840670 Meir, P., Mencuccini, M., & Dewar, R. C. (2015). Drought-r elated tree Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions mortality: Addressing the gaps in understanding and prediction. with maxent: New extensions and a comprehensive evaluation. New Phytologist, 207, 28–33. https://d oi. org/1 0.1 111/ nph.1 3382 Ecography, 31(2), 161–175. https:// doi. org/1 0. 1111/j. 0906- 7590. Meza, G., Rojas-B riceño, N. B., Cotrina Sánchez, A., Oliva-C ruz, M., 2008. 5203. x Olivera Tarifeño, C. M., Hoyos Cerna, M. Y., Ramos Sandoval, J. D., Qarallah, B., Al- Ajlouni, M., Al-A wasi, A., Alkarmy, M., Al-Q udah, E., & Torres Guzmán, C. (2022). Potential current and future distribu- Naser, A. B., Al-A ssaf, A., Gevaert, C. M., Al Asmar, Y., Belgiu, M., tion of the long-w hiskered owlet (Xenoglaux loweryi) in Amazonas & Othman, Y. A. (2021). Evaluating post-f ire recovery of Latroon and san Martin, NW Peru. Animals, 12(14), 1794. https:// doi. org/ 10. dry forest using Landsat ETM+, unmanned aerial vehicle and field 3390/ ani12 141794 survey data. Journal of Arid Environments, 193, 104587. https://d oi. Mokgalaka-M atlala, N. S., Flores-T avizón, E., Castillo-M ichel, H., Peralta- org/ 10.1 016/j.j arid env.2 021. 104587 Videa, J. R., & Gardea-T orresdey, J. L. (2009). Arsenic tolerance in Rembold, F., Leonardi, U., Ng, W.- T., Gadain, H., Meroni, M., & Atzberger, mesquite (Prosopis sp.): Low molecular weight thiols synthesis and C. (2015). Mapping areas invaded by Prosopis juliflora in Somaliland glutathione activity in response to arsenic. Plant Physiology and on Landsat 8 imagery. Remote Sensing for Agriculture, Ecosystems, Biochemistry, 47(9), 822–826. https:// doi. org/ 10. 1016/j.p laphy. and Hydrology XVII, 9637, 963723. https:// doi. org/1 0. 1117/ 12. 2009. 05.0 07 2193133 Nieuwstadt, M. G. L., & Sheil, D. (2005). Drought, fire and tree survival in Rodrigues, P. M. S., Silva, J. O., Eisenlohr, P. V., & Schaefer, C. E. G. R. a Borneo rain forest, East Kalimantan, Indonesia. Journal of Ecology, (2015). Efeitos das mudanças climáticas sobre a distribuição 93(1), 191–201. https:// doi. org/1 0. 1111/j. 1365- 2745. 2004.0 0954. x geográfica de espécies arbóreas especialistas de florestas secas Oliveira, B. F., Costa, G. C., & Fonseca, C. R. (2018). Niche dynam- tropicais brasileiras. Brazilian Journal of Biology, 75(3), 679–684. ics of two cryptic Prosopis invading south American drylands. Rojas- Briceño, N. B., García, L., Cotrina- Sánchez, A., Goñas, M., Salas, R., Biological Invasions, 20(1), 181–194. https:// doi.o rg/1 0.1 007/ s1053 Silva, J. O., & Oliva- Cruz, M. (2022). Land suitability for cocoa cul- 0-0 17-1 525-y tivation in Peru: AHP and MaxEnt modeling in a GIS environment. OSINFOR. (2013). Modelamieto espacial de nichos ecológicos para la evalu- Agronomy, 12(12), 2930. https:// doi. org/ 10. 3390/a gron omy12 ación de presencia de especies forestales maderables en la Amazonía 122930 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 16 of 16  |     BARBOZA et al. Rollenbeck, R., Bayer, F., Munchow, J., Richter, M., Rodriguez, R., & Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Atarama, N. (2015). Climatic cycles and gradients of the El Ninõ Science (New York, N.Y.), 240(4857), 1285–1293. https://d oi.o rg/1 0. core region in North Peru. Advances in Meteorology, 2015, 1–10. 1126/ scienc e. 3287615 https://d oi.o rg/ 10. 1155/2 015/7 50181 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, Salazar, P., Mendieta- Leiva, G., Navarro- Cerrillo, R. M., Cruz, G., Grados, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, N., & Villar, R. (2021). An ecological overview of Prosopis pall- M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, ida, one of the most adapted dryland species to extreme climate K., O'Ishi, R., … Kimoto, M. (2019). Description and basic evaluation events. Journal of Arid Environments, 193, 104576. https://d oi. org/ of simulated mean state, internal variability, and climate sensitiv- 10. 1016/j. jarid env. 2021. 104576 ity in MIROC6. Geoscientific Model Development, 12(7), 2727–2765. Salazar, P. C., Navarro-C errillo, R. M., Ancajima, E., Duque Lazo, J., https:// doi. org/ 10. 5194/ gmd- 12- 2727- 2019 Rodríguez, R., Ghezzi, I., & Mabres, A. (2018). Effect of climate and Vining, B. R., Hillman, A., & Contreras, D. A. (2022). El Niño southern ENSO events on Prosopis pallida forests along a climatic gradient. oscillation and enhanced arid land vegetation productivity in NW Forestry, 91(5), 552–562. https:// doi. org/ 10.1 093/f orest ry/c py014 South America. Journal of Arid Environments, 198, 104695. https:// Salazar, P. C., Navarro- Cerrillo, R. M., Cruz, G., & Villar, R. (2018). doi. org/1 0. 1016/j.j aride nv.2 021. 104695 Intraspecific leaf functional trait variability of eight Prosopis pall- Wakie, T. T., Evangelista, P. H., Jarnevich, C. S., & Laituri, M. (2014). ida tree populations along a climatic gradient of the dry forests of Mapping current and potential distribution of non-n ative prosopis northern Peru. Journal of Arid Environments, 152, 12–20. https:// doi. juliflorain the Afar region of Ethiopia. PLoS One, 9(11), 3–11. https:// org/1 0. 1016/j. jarid env. 2018. 01.0 10 doi.o rg/ 10. 1371/j ourn al. pone. 0112854 Santini, L., Benítez- López, A., Maiorano, L., Čengić, M., & Huijbregts, M. Wang, G., Wang, C., Guo, Z., Dai, L., Wu, Y., Liu, H., Li, Y., Chen, H., Zhang, A. J. (2021). Assessing the reliability of species distribution projec- Y., Zhao, Y., Cheng, H., Ma, T., & Xue, F. (2020). Integrating maxent tions in climate change research. Diversity and Distributions, 27(6), model and landscape ecology theory for studying spatiotemporal 1035–1050. https:// doi.o rg/ 10. 1111/ ddi.1 3252 dynamics of habitat: Suggestions for conservation of endangered Santos- Jallath, J., Castro- Rodríguez, A., Huezo- Casillas, J., & Torres- red-c rowned crane. Ecological Indicators, 116, 106472. https:// doi. Bustillos, L. (2012). Arsenic and heavy metals in native plants at tail- org/ 10. 1016/j. ecoli nd.2 020. 106472 ings impoundments in Queretaro, Mexico. Physics and Chemistry of Wei, B., Wang, R., Hou, K., Wang, X., & Wu, W. (2018). Predicting the the Earth, 37–39, 10–17. https:// doi. org/ 10. 1016/j.p ce.2 011. 12.0 02 current and future cultivation regions of Carthamus tinctorius L. SERFOR. (2020). Inventario Nacional Forestal y de Fauna Silvestre: Panel using MaxEnt model under climate change in China. Global Ecology 1.lima, Perú. Available online https://r eposi torio. serfor. gob. pe/ and Conservation, 16, e00477. https://d oi. org/ 10.1 016/j.g ecco. handle/ SERFOR/9 06 Accessed on 15 April 2024. 2018. e00477 SERFOR. (2021). Avances sobre la investigación de “Algarrobo” Prosopis Yang, J., Huang, Y., Jiang, X., Chen, H., Liu, M., & Wang, R. (2022). (Fabaceae) en la costa norte del Perú. Servicio Nacional Forestal y de Potential geographical distribution of the edangred plant Isoetes Fauna Silvestre (SERFOR). Available online: https:// repos itorio. ser- under human activities using MaxEnt and GARP. Global Ecology and for. gob. pe/ bitst ream/ SERFOR/ 905/3/ SERFOR% 202021% 20Ava Conservation, 38, e02186. https:// doi. org/1 0. 1016/j. gecco.2 022. nces% 20sob re% 20la% 20inv estig acion% 20de% 20alg arrobo% e02186 20en%2 0la% 20cos ta% 20nort e% 20del%2 0Peru. pdf (Accessed on Zhang, S., Liu, X., Li, R., Wang, X., Cheng, J., Yang, Q., & Kong, H. (2021). 30 April 2024). AHP- GIS and MaxEnt for delineation of potential distribution of SERFOR, SENASA, & INIA. (2022). Guía para la identificación de insec- arabica coffee plantation under future climate in Yunnan, China. tos asociados al algarrobo: Prosopis pallida (Humb. & Bonpl. ex Ecological Indicators, 132, 108339. https://d oi. org/1 0.1 016/j. ecoli Willd.) Kunth. Available online https:// cdn.w ww. gob.p e/ uploa ds/ nd.2 021. 108339 docum ent/ file/ 41044 97/ Guí apara. la. ident ifica ció n. de. insec tos. Zorogastúa, P., Quiroz Guerra, R., & Garatuza Payán, J. (2011). Evaluación asoci ados. al. algar robo. proso pis. palli da. pdf. pdf? v= 16759 78157 de cambios en la cobertura y uso de la tierra con imágenes de Accessed on 14 April 2023. satélite en Piura- Perú. Ecología Aplicada, 10(1), 13–22. SERNAP. (2023). GEO ANP. Available online https:// geo.s erna np.g ob. pe/ visor serna np/#. Lima. Peru Accessed on 18 April 2023. Sharma, S., Arunachalam, K., Bhavsar, D., & Kala, R. (2018). Modeling habitat suitability of Perilla frutescens with MaxEnt in Uttarakhand—A conser- How to cite this article: Barboza, E., Bravo, N., Cotrina- vation approach. Journal of Applied Research on Medicinal and Aromatic Plants, 10, 99–105. https:// doi.o rg/1 0.1 016/j. jarmap. 2018.0 2.0 03 Sanchez, A., Salazar, W., Gálvez-P aucar, D., Gonzales, J., Shi, X., Wang, J., Zhang, L., Chen, S., Zhao, A., Ning, X., Fan, G., Wu, N., Saravia, D., Valqui- Valqui, L., Cárdenas, G. P., Ocaña, J., Zhang, L., & Wang, Z. (2023). Prediction of the potentially suitable Cruz- Luis, J., & Arbizu, C. I. (2024). Modeling the current and areas of Litsea cubeba in China based on future climate change future habitat suitability of Neltuma pallida in the dry forest of using the optimized MaxEnt model. Ecological Indicators, 148, 110093. https:// doi. org/ 10. 1016/j. ecoli nd. 2023. 110093 northern Peru under climate change scenarios to 2100. Sintayehu, D. W., Egeru, A., Ng, W.-T ., & Cherenet, E. (2020). Regional Ecology and Evolution, 14, e70158. https://doi.org/10.1002/ dynamics in distribution of Prosopis juliflora under predicted cli- ece3.70158 mate change in Africa. Tropical Ecology, 61(4), 437–445. https://d oi. org/ 10.1 007/ s42965 - 020-0 0101 -w 20457758, 2024, 8, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.70158 by Cochrane Peru, Wiley Online Library on [29/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License