Prioritisation of Native Tree Species for Biodiversity Conservation, Carbon Capture, and Livelihoods Improvement in Shade-Grown Coffee Regions of Chiapas, Mexico

Abstract

Coffee production, particularly in shade-grown farms, plays a crucial role in the livelihoods of Mexican farmers. Shade-grown coffee systems are also recognised for supporting biodiversity and enhancing carbon capture. Nevertheless, the geographical heterogeneity of Mexico makes the selection of tree species in these agroforestry systems challenging. This study develops region-specific priority lists to conserve biodiversity, improve carbon capture, and support the livelihoods of producers across nine coffee-growing regions within the state of Chiapas. We identified the tree species distributed in each region using an extensive dataset from the Global Biodiversity Information Facility and a novel approach that enhanced spatial resolution of the prioritisation process, despite biases in collection efforts. A set of 23 criteria, including conservation status, carbon content, and documented uses by local communities, was compiled from databases and literature reviews and used to calculate a priority score for each species. Based on these scores, a list of 20 recommended species was generated for each region. However, additional participatory validation is needed to translate these lists into practice. A similarity analysis revealed that geographically proximate regions shared similar species composition. Overall, this study provides a transparent framework for regionally tailored shade-tree selection to inform conservation and restoration planning in coffee agroforestry landscapes.

1. Introduction

Mexico is one of the leading coffee producers in Latin America, with 15 coffee-growing states and approximately 564,445 hectares of productive land [1]. In addition to its production volume, Mexico is recognised for its high-quality coffee, which significantly contributes to national income through both domestic and export markets [2,3]. However, this sector has faced persistent challenges over recent decades, including producers abandoning their land or switching to other crops, coffee leaf rust [4], fluctuating coffee prices exacerbated by the COVID-19 pandemic, and the increasing impacts of climate change [4,5,6,7]. These pressures align with national and international policy priorities that promote climate-resilient agricultural systems and nature-based solutions in productive landscapes.

Most coffee produced in Mexico is Coffea arabica L. (85%) and is managed as rustic systems or traditional and commercial polycultures, characterised by varying proportions of native and introduced shade trees and different levels of canopy cover [8]. These agroforestry systems maintain a diverse and relatively dense tree cover, with <10% of the coffee farms in Mexico managed as sun coffee [9]. These shade-grown systems are predominantly managed by smallholder farmers (90% <2.5 ha), often from indigenous communities, whose practices support both agricultural resilience and socio-cultural continuity [10]. Because most shade coffee is produced in smallholder and indigenous territories, interventions promoting native shade trees can also contribute to rural development, equity, and culturally appropriate resource management policies.

Unlike coffee sun coffee or shade monocultures, coffee farms with diversified shade offer substantial environmental benefits. They support native biodiversity, provide critical ecosystem services such as carbon capture, and enable income diversification through the sustainable use of native tree species [9,11,12,13]. Additionally, diversified coffee agroforestry systems enhance soil health and boost ecosystem services and resilience, as the specific composition of shade trees determines soil-function co-benefits—such as enhanced nutrient cycling through deep-root capture and soil aggregation via consistent organic inputs—which are essential for sustaining the biodiversity and carbon storage potential of coffee agroforestry systems [14].

Mexico’s principal coffee-growing regions overlap with areas of high biological and biocultural richness, highlighting the importance of promoting reforestation and biodiversity conservation strategies within these agroecosystems [11,15]. These multiple benefits directly support international commitments on biodiversity and climate, including the Global Biodiversity Framework and national climate strategies that emphasise sustainable land use and ecosystem service protection.

Tree diversification contributes to biodiversity conservation, ecological restoration, and the enhancement of carbon capture and can provide local communities with valuable resources such as edible or medicinal plants [16,17,18,19]. Diversifying the shade trees in these environments not only increases tree diversity but also promotes interactions with other groups of organisms, such as birds and mycorrhizal fungi, as has been reported for the Soconusco region in Chiapas [20]. Such ecological outcomes are increasingly recognised in restoration-oriented policies that prioritise native species, ecosystem functionality, and biodiversity monitoring in productive landscapes.

In this context, enhancing native tree species in coffee farms has gained attention as a promising approach for strengthening agroforestry systems, due to tree diversity’s link with resilience to climate change in forest ecosystems [21]. The increasing recognition of shade-grown coffee as a biodiversity-friendly product has encouraged the development of certifications and market-based incentives aimed at improving environmental and social outcomes. For instance, the recent “Café de Sombra Natural” (CSN) certification by Mexico’s Ministry of Environment and Natural Resources [22] includes a catalogue of native tree species to guide reforestation and biodiversity conservation on certified farms. However, operationalising such schemes requires transparent, regionally appropriate, species selection tools that balance conservation priorities, carbon outcomes, and farmer utility. A growing number of projects have been carried out to select suitable tree species for reforestation in coffee farms, proposing methodologies to prioritise native taxa with high carbon capture capacity and documented local uses [23,24,25]. Shade-tree diversification and participation in payment programmes for environmental services, such as carbon or biodiversity credits, have also been proposed as viable strategies to enhance producers’ income and prevent the intensification of coffee production or the conversion of shade coffee into other crop monocultures [13]. Priority lists grounded in biodiversity data and evidence of local-use can improve the credibility and adoption of these policy instruments.

Chiapas is Mexico’s leading coffee-producing state, accounting for 41% of the country’s total coffee production [3]. Coffee farms in this state have an average shade density of 47 to 53% [26]. Chiapas is also one of Mexico’s two hotspots of tree biodiversity, making it a priority region for targeted conservation and reforestation actions [27].

Given their geographical extent, high arboreal diversity, and overlap with montane cloud forests, shade-grown coffee areas in Chiapas represent excellent opportunities for mitigating climate change and protecting native biodiversity [28]. However, achieving these outcomes requires regional species selection aligned with national and international commitments/goals, such as biodiversity conservation, climate change mitigation, and sustainable rural development. Additionally, practical considerations, such as the extent of canopy cover, must be considered to avoid compromising coffee yields due to excessive shade [13].

Selecting regionally suitable tree species for shade coffee farms is a key step in successful diversification while maintaining coffee productivity and quality at small-scale coffee farms [29,30]. To achieve this, developing a regional prioritisation method that incorporates species records is a critical initial step, both increasing methodological resolution and enhancing the efficiency of the selection process. The objective of this study was to provide an initial regional prioritisation for the nine coffee-growing areas in Chiapas, focusing on optimising tree cover with three goals: (1) preserving native and endangered biodiversity, (2) enhancing carbon capture, and (3) improving the livelihoods of producers through selecting useful taxa. By providing region-specific, evidence-based species lists, this study offers decision support for certification standards, restoration programmes, and incentive schemes targeting sustainable coffee landscapes.

2. Materials and Methods

2.1. Study Area

Species prioritisations were developed using the same algorithm for each of the nine coffee regions in Chiapas: Altos, Centro, Fraylesca, Fronteriza, Istmo-Costa, Norte, Selva, Sierra, and Soconusco (Figure 1,

Table 1. Climatic information and the number of records and species obtained from tree databases to develop the prioritisations for the nine coffee regions selected in Chiapas, Mexico. Sources: Hernández-Martínez et al. [26], GBIF [31], SEMARNAT [22].

Region	Mean Annual Rainfall (mm)	Mean Annual Temperature (°C)	Average Altitude (m)	Number of Records	Density of Records per Hectare	Native Tree Species Considered in Prioritisations	Native Tree Species Reported in Coffee Farms
Altos	1894	20	1332	4253	0.24	416	149
Centro	1405	23	978	7523	0.61	860	238
Fraylesca	1903	22	1252	7195	0.30	588	194
Fronteriza	2632	22	920	3914	0.36	537	176
Istmo-Costa	2423	24	795	430	0.02	182	84
Norte	2113	22	1047	3759	0.17	463	171
Selva	2312	22	988	7643	0.15	728	209
Sierra	1762	21	1285	3868	0.09	584	195
Soconusco	3754	24	776	6177	0.18	670	220
Total	-	-	-	44,762		1362	286

). These regions were proposed by Hernández-Martínez et al. [26] and reflect a diverse range of agro-climatic, production, and socioeconomic conditions in each coffee-producing region (Table 1).

2.2. Data Collection

The list of native tree species (n = 2934 taxa for Mexico) used in this study is an updated version of the checklist published by Telléz et al. [27], adding taxa described in recent years and new records from bibliographic reviews. Georeferenced records of individuals of each tree species were retrieved from the Global Biodiversity Information Facility (GBIF) [31], and the dataset from Téllez et al. [27]. All records were cleaned using the R (version 2.6-8) package “CoordinateCleaner” [32], which deletes duplicates and records in the sea (scale of the default reference = 110), using country centroids (buffer = 1 km), near country capitals (buffer = 10 km), and within museums or botanic gardens (buffer = 100 m). Subsequently, cleaned records (n = 956,410) were filtered to the polygons of the coffee regions and used to identify the native species distributed in each one of the nine coffee regions using QGIS 3.36.3 [33] (Figure 2). An approximation of the regional abundance of the tree native species was obtained from the number of records of the species within each region and considered a relevant criterion for the species selection [34], together with previous reports of their use as shade trees in coffee farms (

Table 2. Attributes considered in the prioritisations and associated normalised values obtained for all tree species considered in the study from 0 to 1. CR: Critically Endangered. EN: Endangered. VU: Vulnerable. NT: Near Threatened. See the text for more information on each attribute.

Goal	Attribute	Attribute Values
Common	1. Reported in shade-grown coffee farms (SC)	Reported (1)		Not reported (0)
	2. Regional abundance (RA)	Normalised abundance per species per region (0–1)
Biodiversity conservation (BC)	3. Endemism to Mexico (EM)	Endemic (1)		Not endemic (0)
	4. Endemism to Chiapas (EC)	Endemic (1)		Not endemic (0)
	5. IUCN category (RL)	CR (1)	EN (0.75)	VU (0.5)	NT (0.25)
	6. NOM-059 category (NC)	P (1)	A (0.66)		Pr (0.33)
	7. Protected in situ (Pi)	Not protected (1)		Protected (0)
	8. Protected ex situ (Pe)	Not protected (1)		Protected (0)
Carbon capture (CC)	9. Carbon content (CCo)	Value given by Doraisami et al.’s [52] dataset (0–1)
	10. Growth rate (GR)	Fast (1)	Medium (0.5)		Slow (0)
	11. Use as fuel (UF)	Not used (1)		Used (0)
	12. Biomass per species (B)	Standardised value given by CONAFOR’s [53] dataset (0–1)
	13. Mean wood density (WD)	Standardised value given by Ricker’s et al. [54] dataset (0–1)
	14. High carbon capture (HC)	Yes (1)		No (0)
	15. Recommended for reforestation (RR)	Yes (1)		No (0)
Improving livelihoods (IL)	16. Use as animal food (AF)	Reported (1)		Not reported (0)
	17. Use as food (F)	Reported (1)		Not reported (0)
	18. Use as material (M)	Reported (1)		Not reported (0)
	19. Use as medicine (Me)	Reported (1)		Not reported (0)
	20. Use as poison (P)	Reported (1)		Not reported (0)
	21. Social uses (SU)	Reported (1)		Not reported (0)
	22. Nitrogen fixing (NF)	Reported (1)		Not reported (0)
	23. Presence in cloud forest (CF)	Present (1)		Absent (0)

, Supplementary Material Table S1). Previous analyses were conducted considering only the presence/absence of species in coffee-growing regions resulting in algorithms that lacked sufficient resolution due to incidental records of species with high utility values. Species reported in coffee farms were obtained from an exhaustive bibliographic review [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51] and are included in the Supplementary Material Table S1. Additional data were incorporated for all taxa on the list to provide the criteria for prioritisation and to align with the three main goals of the project: Biodiversity conservation, Carbon capture, and Improving livelihoods. No specific adjustments were made for missing values, since scores were calculated using only the available information for each species.

2.2.1. Biodiversity Conservation Goal

A total of six attributes related to biodiversity conservation were considered (Table 2). These included: the distribution and endemism in Mexico and Chiapas obtained from Villaseñor [55]; species conservation status from the IUCN Red List of Threatened Species [56], and the Mexican regional assessments were recovered from the “Norma Oficial Mexicana NOM-059-SEMARNAT-2010” [57]; the status of protection in situ and ex situ was downloaded using the BGCI tool “Global Tree Portal” [58].

2.2.2. Carbon Capture Goal

Seven attributes were used to describe variation in carbon capture potential (Table 2). Carbon content (the fraction of carbon in dry biomass) of each tree species was obtained from the GLOWCAD database [52]. Values reported as a percentage (0 to 100) were divided by 100 to standardise values prior to prioritisation. Three growth rate categories (fast, medium and slow) were proposed following the family information from Condit et al. [59], and the report of use as fuel was derived from the World Checklist of Useful Plant Species [60]. Mean biomass was calculated for species with data reported in the National Forest and Soil Inventory from 2015 to 2020 [53]. Wood density data were obtained from the public dataset published by Ricker et al. [54]. The biomass and wood density data were filtered to include only those measurements that met the definition of a tree proposed by Téllez et al. [27]. Means per species were then calculated, and the data were subsequently standardised using the method min-max normalisation prior to prioritisation. High carbon capture capacity and recommendations for reforestation information were incorporated into the dataset as binary variables (0–1) based on an intensive literature review building on the revision made by Pompa-García and Sigala-Rodríguez [61], including more than 75 papers.

2.2.3. Improving Livelihoods Goal

Eight additional attributes were used to describe information relative to improving livelihoods. Information on particular uses was obtained from the World Checklist of Useful Plant Species [60], considering six of the ten categories proposed: animal food, food, materials, medicines, poisons, and social uses. Taxa belonging to the Fabaceae family were considered potential nitrogen-fixers. Lastly, species’ presence in cloud forests was added from an extensive bibliographical review and field observations made by the authors.

2.3. Prioritisation and Weighting of the Criteria

Nine independent prioritisations were generated using the 23 attributes described in Table 2, one for each coffee region in Chiapas. Three online meetings were held to determine the weighting of the criteria according to the study’s goals (October and November 2024). All co-authors (researchers with extensive experience in coffee agroforestry, forest ecology, ecosystem services, and sustainable land management) participated in the assignment of attribute weights. These meetings followed the Delphi methodology modified from Garson [62], as well as the protocol established in Flores-Ortiz et al. [25]. After at least three iterative rounds of discussion, new versions of species lists, incorporating participant feedback, were presented. The process was repeated until the divergence between expert opinions was reduced and a consensus was reached. Since this study focused on coffee farms, the first criterion, “Reported in shade-grown coffee farms”, was relevant to the project’s three goals. The second criterion, “Regional abundance”, was also relevant to all the goals.

Table 3. Consensus weighting of attributes defined in expert meetings and subsequently used in prioritisations of tree species for enriching shade cover in nine coffee regions in Chiapas, Mexico.

Attribute/Goal	Biodiversity Conservation	Carbon Capture	Improving Livelihoods
Reported in shade coffee farms (SC)	0.1	0.1	0.15
Regional abundance (RA)	0.1	0.1	0.1
Endemism to Mexico (EM)	0.1	0	0
Endemism to Chiapas (EC)	0.2	0	0
IUCN category (RL)	0.2	0	0
NOM-059 category (NC)	0.1	0	0
Protected in situ (Pi)	0.1	0	0
Protected ex situ (Pe)	0.1	0	0
Carbon content (CCo)	0	0.1	0
Growth rate (GR)	0	0.175	0
Use as fuel (UF)	0	0.1	0
Biomass per species (B)	0	0.1	0
Mean wood density (WD)	0	0.05	0
High carbon capture (HC)	0	0.175	0
Recommended for reforestation (RR)	0	0.1	0
Use as animal food (AF)	0	0	0.1
Use as food (F)	0	0	0.15
Use as material (M)	0	0	0.05
Use as medicine (Me)	0	0	0.1
Use as poison (P)	0	0	0.05
Social uses (SU)	0	0	0.1
Nitrogen fixing (NF)	0	0	0.15
Presence in cloud forest (CF)	0	0	0.05
TOTAL	1	1	1

and Formulas (1)–(4) summarise the final weighting used on the prioritisations. To avoid bias each goal was assigned 33% of the total influence on the final priority score.

Formula to calculate the biodiversity conservation score:

BCs = (SC*0.1) + (RA*0.1) + (EM*0.1) + (EC*0.2) + (RL*0.2) + (NC*0.1) + (Pi*0.1) + (Pe*0.1)

(1)

Formula to calculate the carbon capture score:

CCs = (SC*0.1) + (RA*0.1) + (CCo*0.1) + (GR*0.175) + (UF*0.1) + (B*0.1) + (WD*0.05) + (HC*0.175) + (RR*0.1)

(2)

Formula to calculate the improving livelihoods score:

ILs = (SC*0.15) + (RA*0.1) + (AF*0.1) + (F*0.15) + (M*0.05) + (Me*0.1) + (P*0.05) + (SU*0.1) + (NF*0.15) + (CF*0.05)

(3)

Formula to calculate the final priority score:

PS = (BCs + CCs + ILs)/3

(4)

2.4. Species Similarity Analysis

A similarity analysis was carried out to visualise the similarity of the species composition in each of the nine regions. This analysis was performed using the Bray–Curtis Dissimilarity coefficient to compare both the presence/absence of the species and their abundance in each region [63]. The analysis was carried out in R (version 2.6-8) using the package “vegan” [64]. Hierarchical clustering was performed using a UPGMA (Unweighted Pair Group Method with Arithmetic Mean) analysis to obtain the final dendrogram from the same software.

3. Results

3.1. Status of the Chiapas Arboreal Diversity and Its Presence in the Coffee Regions

A total of 1653 native tree species were reported for Chiapas, of which 1362 were distributed within the coffee-growing regions, representing 82% of the state’s total native arboreal flora. The 1362 species distributed in coffee regions belong to 119 families, with Fabaceae being the most diverse with 215, Rubiaceae with 110, and Lauraceae with 85 taxa. At the genus level, 457 genera were reported. Quercus L. stood out with 37 species, followed by Eugenia P.Micheli ex L. with 36 and Lonchocarpus Kunth with 30.

From these taxa, 286 were previously reported in the literature as companion trees of coffee farms, for example, Cedrela odorata L., Ceiba pentandra (L.) Gaertn., or Enterolobium cyclocarpum (Jacq.) Griseb. Approximately 94% of the total number of tree species have been evaluated by the IUCN, and around 23% are either considered to be at risk (Critically Endangered, Endangered, or Vulnerable) or in the category of Near Threatened. For example, the micro endemic species Amyris chiapensis Lundell, Arachnothryx sousae Borhidi, or Calliandra ricoana H.M.Hern. & R.Duno. A total of 648 of the 1362 species had reported values of biomass, while 364 had reported values of wood density. The uses that stood out in the greatest number of species were food, with 32% of the taxa, and medicinal use, including 30% of the species distributed in the coffee regions. Some of those species are widely known and utilised, such as Bixa orellana L., Persea americana Mill., and Psidium guajava L.

3.2. Regional Prioritisation

Nine different prioritisations are presented, one for each one of Chiapas’s coffee regions. The 20 taxa with the highest priority are presented in

Table 4. Top 20 ranked native tree species from the prioritisation conducted to enrich shade cover in each of the nine coffee-growing regions of Chiapas, Mexico. Taxonomic information of the 39 native tree species included in the regional prioritisations is reported in Appendix A.

Rank	Altos	Centro	Fraylesca	Fronteriza	Istmo-Costa	Norte	Selva	Sierra	Soconusco
1	Leucaena leucocephala	Gliricidia sepium	Gliricidia sepium	Inga inicuil	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium
2	Litsea glaucescens	Bursera simaruba	Leucaena leucocephala	Swietenia macrophylla	Bursera simaruba	Leucaena leucocephala	Leucaena leucocephala	Leucaena leucocephala	Leucaena leucocephala
3	Inga vera	Leucaena leucocephala	Inga inicuil	Liquidambar styraciflua	Cecropia obtusifolia	Liquidambar styraciflua	Bursera simaruba	Bursera simaruba	Inga inicuil
4	Guazuma ulmifolia	Guazuma ulmifolia	Bursera simaruba	Bursera simaruba	Manilkara zapota	Bursera simaruba	Swietenia macrophylla	Inga inicuil	Cecropia obtusifolia
5	Psidium guajava	Swietenia macrophylla	Cecropia obtusifolia	Cecropia obtusifolia	Liquidambar styraciflua	Cedrela odorata	Inga inicuil	Cedrela odorata	Bursera simaruba
6	Bursera simaruba	Cedrela odorata	Guazuma ulmifolia	Brosimum alicastrum	Inga vera	Dendropanax arboreus	Cedrela odorata	Cecropia obtusifolia	Guazuma ulmifolia
7	Cecropia obtusifolia	Cecropia obtusifolia	Cedrela odorata	Cedrela odorata	Guazuma ulmifolia	Guazuma ulmifolia	Guazuma ulmifolia	Guazuma ulmifolia	Cedrela odorata
8	Manilkara zapota	Cordia alliodora	Hymenaea courbaril	Litsea glaucescens	Cordia alliodora	Hymenaea courbaril	Dendropanax arboreus	Cordia alliodora	Trema micranthum
9	Trema micranthum	Trema micranthum	Cordia alliodora	Inga vera	Hymenaea courbaril	Cordia alliodora	Cecropia obtusifolia	Vachellia pennatula	Cordia alliodora
10	Persea americana	Inga vera	Liquidambar styraciflua	Manilkara zapota	Brosimum alicastrum	Manilkara zapota	Brosimum alicastrum	Hymenaea courbaril	Hymenaea courbaril
11	Liquidambar styraciflua	Manilkara zapota	Trema micranthum	Cordia alliodora	Malpighia glabra	Litsea glaucescens	Cordia alliodora	Dendropanax arboreus	Manilkara zapota
12	Prunus serotina	Hymenaea courbaril	Inga vera	Dendropanax arboreus	Trema micranthum	Inga vera	Manilkara zapota	Litsea glaucescens	Inga vera
13	Inga punctata	Brosimum alicastrum	Dendropanax arboreus	Guazuma ulmifolia	Inga punctata	Trema micranthum	Inga vera	Manilkara zapota	Brosimum alicastrum
14	Trophis racemosa	Vachellia pennatula	Litsea glaucescens	Trophis racemosa	Psidium guajava	Ceiba pentandra	Trema micranthum	Inga vera	Carpinus caroliniana
15	Vachellia pennatula	Dendropanax arboreus	Inga punctata	Theobroma cacao	Spondias mombin	Prunus serotina	Ceiba pentandra	Carpinus caroliniana	Dendropanax arboreus
16	Carica papaya	Hamelia patens	Manilkara zapota	Ceiba pentandra	Hamelia patens	Theobroma cacao	Inga punctata	Ceiba pentandra	Litsea glaucescens
17	Acaciella angustissima	Litsea glaucescens	Byrsonima crassifolia	Trema micranthum	Plumeria rubra	Vachellia pennatula	Trophis racemosa	Trema micranthum	Theobroma cacao
18	Dendropanax arboreus	Byrsonima crassifolia	Ceiba pentandra	Malpighia glabra	Inga flexuosa	Ochroma pyramidale	Litsea glaucescens	Brosimum alicastrum	Ceiba pentandra
19	Calliandra houstoniana	Ceiba pentandra	Brosimum alicastrum	Psidium guajava	Carpinus caroliniana	Psidium guajava	Malpighia glabra	Liquidambar styraciflua	Enterolobium cyclocarpum
20	Plumeria rubra	Malpighia glabra	Psidium guajava	Vachellia pennatula	Palicourea padifolia	Persea americana	Prunus serotina	Theobroma cacao	Schizolobium parahyba

.

A total of 39 species were included among the top 20 species comprising the nine prioritisation lists (taxonomic information is included in Appendix A,

Table A1. Taxonomic information of the 39 native tree species included in the nine regional prioritisations.

Family	Species Name	Taxonomic Author
Altingiaceae	Liquidambar styraciflua	L.
Anacardiaceae	Spondias mombin	L.
Apocynaceae	Plumeria rubra	L.
Araliaceae	Dendropanax arboreus	(L.) Decne. & Planch.
Betulaceae	Carpinus caroliniana	Walter
Boraginaceae	Cordia alliodora	(Ruiz & Pav.) Oken
Burseraceae	Bursera simaruba	(L.) Sarg.
Cannabaceae	Trema micranthum	(L.) Blume
Caricaceae	Carica papaya	L.
Fabaceae	Acaciella angustissima	(Mill.) Britton & Rose
Fabaceae	Calliandra houstoniana	(Mill.) Standl.
Fabaceae	Enterolobium cyclocarpum	(Jacq.) Griseb.
Fabaceae	Gliricidia sepium	(Jacq.) Kunth
Fabaceae	Hymenaea courbaril	L.
Fabaceae	Inga flexuosa	Schltdl.
Fabaceae	Inga inicuil	Schltdl. & Cham. ex G.Don
Fabaceae	Inga punctata	Willd.
Fabaceae	Inga vera	Willd.
Fabaceae	Leucaena leucocephala	(Lam.) de Wit
Fabaceae	Schizolobium parahyba	(Vell.) S.F.Blake
Fabaceae	Vachellia pennatula	(Schltdl. & Cham.) Seigler & Ebinger
Lauraceae	Litsea glaucescens	Kunth
Lauraceae	Persea americana	Mill.
Malpighiaceae	Byrsonima crassifolia	(L.) Kunth
Malpighiaceae	Malpighia glabra	L.
Malvaceae	Ceiba pentandra	(L.) Gaertn.
Malvaceae	Guazuma ulmifolia	Lam.
Malvaceae	Ochroma pyramidale	(Cav. ex Lam.) Urb.
Malvaceae	Theobroma cacao	L.
Meliaceae	Cedrela odorata	L.
Meliaceae	Swietenia macrophylla	King
Moraceae	Brosimum alicastrum	Sw.
Moraceae	Trophis racemosa	(L.) Urb.
Myrtaceae	Psidium guajava	L.
Rosaceae	Prunus serotina	Ehrh.
Rubiaceae	Hamelia patens	Jacq.
Rubiaceae	Palicourea padifolia	(Willd. ex Schult.) C.M.Taylor & Lorence
Sapotaceae	Manilkara zapota	(L.) P.Royen
Urticaceae	Cecropia obtusifolia	Bertol.

). Fabaceae had the highest number of species, with 12 taxa (30%), followed by Malvaceae with 4 (10%). Inga (4 spp.) stood out as the most represented genus in the prioritisations. Key species were identified for their presence in more than one region. For example, five species were considered in the nine coffee regions: Bursera simaruba (L.) Sarg., Guazuma ulmifolia Lam., Inga vera Willd., Manilkara zapota (L.) P.Royen, and Trema micranthum (L.) Blume. The specific contribution of each goal to the overall priority score is reported visually for each region in the graphs presented in Appendix B (Figure A1, Figure A2, Figure A3, Figure A4, Figure A5, Figure A6, Figure A7, Figure A8 and Figure A9). The priority species lists for each one of the three goals can be consulted in Appendix C (

Table A2. Top 20 prioritised species for each coffee region in Chiapas according to the biodiversity conservation score.

Ranking	Altos	Centro	Fraylesca	Fronteriza	Istmo-Costa	Norte	Selva	Sierra	Soconusco
1	Clethra chiapensis	Lonchocarpus foveolatus	Ateleia tenorioi	Magnolia mayae	Aiouea chiapensis	Lonchocarpus foveolatus	Calliandra ricoana	Ateleia tenorioi	Arachnothryx sousae
2	Picramnia deflexa	Amyris chiapensis	Maytenus stipitata	Damburneya leucocome	Ctenardisia purpusii	Hampea breedlovei	Hampea breedlovei	Aiouea chiapensis	Sommera parva
3	Aiouea chiapensis	Magnolia perezfarrerae	Aiouea chiapensis	Hampea montebellensis	Pseudomiltemia davidsonii	Orthion montanum	Magnolia mayae	Ctenardisia purpusii	Eugenia ovandensis
4	Chamaedorea cataractarum	Mortoniodendron ocotense	Chamaedorea cataractarum	Dalbergia stevensonii	Carpinus caroliniana	Erythrina pudica	Sommera parva	Daphnopsis flavida	Aiouea chiapensis
5	Juniperus gamboana	Orthion montanum	Ardisia breedlovei	Ardisia breedlovei	Saurauia madrensis	Magnolia sharpii	Lonchocarpus comitensis	Licaria glaberrima	Ctenardisia purpusii
6	Licaria glaberrima	Picramnia deflexa	Ctenardisia purpusii	Licaria glaberrima	Chamaedorea tepejilote	Ocotea matudae	Damburneya leucocome	Pseudomiltemia davidsonii	Daphnopsis flavida
7	Magnolia sharpii	Robinsonella pilosissima	Daphnopsis flavida	Palicourea breedlovei	Prunus tetradenia	Wimmeria acuminata	Orthion montanum	Wimmeria acuminata	Licaria glaberrima
8	Ocotea matudae	Hampea montebellensis	Eugenia breedlovei	Magnolia montebelloensis	Persea schiedeana	Yucca lacandonica	Juglans pyriformis	Yucca lacandonica	Ocotea matudae
9	Wimmeria acuminata	Gliricidia robusta	Licaria glaberrima	Wimmeria montana	Gonzalagunia tacanensis	Lonchocarpus santarosanus	Hampea montebellensis	Cordia colimensis	Ateleia glabrata
10	Ocotea psychotrioides	Lonchocarpus sumiderensis	Pseudomiltemia davidsonii	Carpinus caroliniana	Dendropanax populifolius	Hampea nutricia	Dalbergia stevensonii	Wimmeria montana	Wimmeria montana
11	Lonchocarpus santarosanus	Aiouea chiapensis	Yucca lacandonica	Magnolia schiedeana	Trema micranthum	Lonchocarpus latimarginatus	Chamaedorea cataractarum	Carpinus caroliniana	Carpinus caroliniana
12	Ocotea congregata	Bunchosia breedlovei	Carpinus caroliniana	Saurauia madrensis	Malpighia glabra	Euonymus chiapensis	Ardisia breedlovei	Magnolia schiedeana	Beaucarnea goldmanii
13	Rogiera breedlovei	Stenanona migueliana	Saurauia madrensis	Mortoniodendron guatemalense	Inga flexuosa	Mortoniodendron uxpanapense	Magnolia sharpii	Beaucarnea goldmanii	Dalbergia tucurensis
14	Wimmeria montana	Chamaedorea cataractarum	Maytenus matudae	Litsea glaucescens	Persea americana	Palicourea thornei	Wimmeria acuminata	Saurauia madrensis	Saurauia madrensis
15	Carpinus caroliniana	Juniperus gamboana	Hesperocyparis lusitanica	Saurauia villosa	Vachellia cornigera	Wimmeria montana	Yucca lacandonica	Prunus tartarea	Coccoloba chiapensis
16	Dalbergia tucurensis	Erythrina pudica	Leucaena leucocephala	Myrica cerifera	Aiouea montana	Magnolia faustinomirandae	Ocotea psychotrioides	Hesperocyparis lusitanica	Mortoniodendron guatemalense
17	Leucaena leucocephala	Eugenia breedlovei	Litsea glaucescens	Vatairea lundellii	Myriocarpa longipes	Carpinus caroliniana	Lonchocarpus santarosanus	Leucaena leucocephala	Prunus erythroxylon
18	Litsea glaucescens	Licaria glaberrima	Saurauia villosa	Chamaedorea tepejilote	Coccoloba barbadensis	Dalbergia tucurensis	Damburneya inconspicua	Litsea glaucescens	Hesperocyparis lusitanica
19	Chrysophyllum cainito	Magnolia sharpii	Chamaedorea tepejilote	Inga xalapensis	Sideroxylon portoricense	Saurauia madrensis	Arachnothryx lineolata	Saurauia villosa	Leucaena leucocephala
20	Myrica cerifera	Parathesis cintalapana	Yucca gigantea	Pleuranthodendron lindenii	Palicourea padifolia	Aiouea breedlovei	Ocotea congregata	Myrica cerifera	Litsea glaucescens

), Appendix D (

Table A3. Top 20 prioritised species for each coffee region in Chiapas according to the carbon capture score.

Ranking	Altos	Centro	Fraylesca	Fronteriza	Istmo-Costa	Norte	Selva	Sierra	Soconusco
1	Cecropia obtusifolia	Gliricidia sepium	Inga inicuil	Inga inicuil	Gliricidia sepium	Gliricidia sepium	Inga inicuil	Inga inicuil	Inga inicuil
2	Litsea glaucescens	Cecropia obtusifolia	Gliricidia sepium	Cecropia obtusifolia	Cecropia obtusifolia	Cedrela odorata	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium
3	Manilkara zapota	Cedrela odorata	Cecropia obtusifolia	Cedrela odorata	Manilkara zapota	Litsea glaucescens	Cecropia obtusifolia	Cecropia obtusifolia	Cecropia obtusifolia
4	Pinus pseudostrobus	Litsea glaucescens	Cedrela odorata	Litsea glaucescens	Brosimum alicastrum	Manilkara zapota	Cedrela odorata	Cedrela odorata	Cedrela odorata
5	Liquidambar styraciflua	Manilkara zapota	Litsea glaucescens	Manilkara zapota	Liquidambar styraciflua	Pinus pseudostrobus	Litsea glaucescens	Litsea glaucescens	Litsea glaucescens
6	Dendropanax arboreus	Brosimum alicastrum	Manilkara zapota	Brosimum alicastrum	Bursera simaruba	Liquidambar styraciflua	Manilkara zapota	Manilkara zapota	Manilkara zapota
7	Byrsonima crassifolia	Pinus pseudostrobus	Brosimum alicastrum	Pinus pseudostrobus	Inga vera	Dendropanax arboreus	Brosimum alicastrum	Brosimum alicastrum	Brosimum alicastrum
8	Quercus xalapensis	Liquidambar styraciflua	Pinus pseudostrobus	Liquidambar styraciflua	Chrysophyllum mexicanum	Byrsonima crassifolia	Pinus pseudostrobus	Pinus pseudostrobus	Dendropanax arboreus
9	Bursera simaruba	Dendropanax arboreus	Liquidambar styraciflua	Dendropanax arboreus	Cordia alliodora	Bursera simaruba	Liquidambar styraciflua	Liquidambar styraciflua	Byrsonima crassifolia
10	Inga vera	Byrsonima crassifolia	Dendropanax arboreus	Byrsonima crassifolia	Guazuma ulmifolia	Simira salvadorensis	Dendropanax arboreus	Dendropanax arboreus	Bursera simaruba
11	Chrysophyllum mexicanum	Swietenia macrophylla	Byrsonima crassifolia	Swietenia macrophylla	Aiouea montana	Inga vera	Byrsonima crassifolia	Byrsonima crassifolia	Simira salvadorensis
12	Leucaena leucocephala	Bursera simaruba	Quercus xalapensis	Bursera simaruba	Ficus yoponensis	Chrysophyllum mexicanum	Swietenia macrophylla	Quercus xalapensis	Inga vera
13	Guazuma ulmifolia	Simira salvadorensis	Bursera simaruba	Simira salvadorensis	Ficus obtusifolia	Cordia alliodora	Bursera simaruba	Bursera simaruba	Chrysophyllum mexicanum
14	Ficus cotinifolia	Inga vera	Inga vera	Inga vera	Inga flexuosa	Leucaena leucocephala	Simira salvadorensis	Inga vera	Cordia alliodora
15	Alnus acuminata	Chrysophyllum mexicanum	Chrysophyllum mexicanum	Chrysophyllum mexicanum	Hymenaea courbaril	Guazuma ulmifolia	Inga vera	Chrysophyllum mexicanum	Leucaena leucocephala
16	Trophis racemosa	Cordia alliodora	Cordia alliodora	Cordia alliodora	Carpinus caroliniana	Ficus cotinifolia	Chrysophyllum mexicanum	Cordia alliodora	Guazuma ulmifolia
17	Pinus oocarpa	Leucaena leucocephala	Leucaena leucocephala	Guazuma ulmifolia	Psidium oligospermum	Heliocarpus appendiculatus	Cordia alliodora	Leucaena leucocephala	Ficus cotinifolia
18	Inga flexuosa	Guazuma ulmifolia	Guazuma ulmifolia	Alnus acuminata	Platanus mexicana	Aiouea montana	Swartzia cubensis	Guazuma ulmifolia	Alnus acuminata
19	Quercus rugosa	Ficus cotinifolia	Ficus cotinifolia	Heliocarpus appendiculatus	Psidium guajava	Pseudolmedia glabrata	Leucaena leucocephala	Ficus cotinifolia	Heliocarpus appendiculatus
20	Prunus serotina	Alnus acuminata	Heliocarpus appendiculatus	Pseudolmedia glabrata	Sideroxylon portoricense	Trophis racemosa	Guazuma ulmifolia	Alnus acuminata	Aiouea montana

) and Appendix E (

Table A4. Top 20 prioritised species for each coffee region in Chiapas according to the improving livelihoods score.

Ranking	Altos	Centro	Fraylesca	Fronteriza	Istmo-Costa	Norte	Selva	Sierra	Soconusco
1	Leucaena leucocephala	Gliricidia sepium	Gliricidia sepium	Ceiba pentandra	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium	Gliricidia sepium
2	Spondias mombin	Leucaena leucocephala	Leucaena leucocephala	Spondias mombin	Hymenaea courbaril	Leucaena leucocephala	Leucaena leucocephala	Leucaena leucocephala	Leucaena leucocephala
3	Trema micranthum	Hymenaea courbaril	Hymenaea courbaril	Trema micranthum	Spondias mombin	Hymenaea courbaril	Samanea saman	Hymenaea courbaril	Hymenaea courbaril
4	Guazuma ulmifolia	Enterolobium cyclocarpum	Enterolobium cyclocarpum	Erythrina berteroana	Trema micranthum	Ceiba pentandra	Enterolobium cyclocarpum	Enterolobium cyclocarpum	Enterolobium cyclocarpum
5	Plumeria rubra	Cassia grandis	Ceiba pentandra	Cordia alliodora	Cordia alliodora	Spondias mombin	Ceiba pentandra	Ceiba pentandra	Cassia grandis
6	Carica papaya	Ceiba pentandra	Spondias mombin	Guazuma ulmifolia	Guazuma ulmifolia	Trema micranthum	Myroxylon balsamum	Myroxylon balsamum	Ceiba pentandra
7	Bixa orellana	Myroxylon balsamum	Trema micranthum	Theobroma cacao	Plumeria rubra	Erythrina berteroana	Spondias mombin	Spondias mombin	Myroxylon balsamum
8	Malpighia glabra	Spondias mombin	Cordia alliodora	Plumeria rubra	Malpighia glabra	Cordia alliodora	Trema micranthum	Trema micranthum	Spondias mombin
9	Sapindus saponaria	Trema micranthum	Guazuma ulmifolia	Carica papaya	Calliandra houstoniana	Guazuma ulmifolia	Erythrina berteroana	Erythrina berteroana	Trema micranthum
10	Calliandra houstoniana	Erythrina berteroana	Theobroma cacao	Bixa orellana	Persea americana	Theobroma cacao	Cordia alliodora	Cordia alliodora	Erythrina berteroana
11	Persea americana	Cordia alliodora	Plumeria rubra	Malpighia glabra	Cecropia obtusifolia	Carica papaya	Guazuma ulmifolia	Guazuma ulmifolia	Cordia alliodora
12	Cecropia obtusifolia	Guazuma ulmifolia	Carica papaya	Sapindus saponaria	Bursera simaruba	Bixa orellana	Theobroma cacao	Theobroma cacao	Guazuma ulmifolia
13	Bursera simaruba	Theobroma cacao	Bixa orellana	Calliandra houstoniana	Inga vera	Malpighia glabra	Plumeria rubra	Plumeria rubra	Theobroma cacao
14	Inga vera	Plumeria rubra	Malpighia glabra	Cecropia obtusifolia	Psidium guajava	Sapindus saponaria	Carica papaya	Carica papaya	Plumeria rubra
15	Psidium guajava	Carica papaya	Sapindus saponaria	Bursera simaruba	Inga punctata	Calliandra houstoniana	Bixa orellana	Bixa orellana	Carica papaya
16	Tecoma stans	Bixa orellana	Calliandra houstoniana	Inga vera	Cestrum nocturnum	Annona squamosa	Malpighia glabra	Malpighia glabra	Bixa orellana
17	Inga punctata	Malpighia glabra	Annona squamosa	Schizolobium parahyba	Manilkara zapota	Persea americana	Sapindus saponaria	Sapindus saponaria	Malpighia glabra
18	Vachellia pennatula	Sapindus saponaria	Persea americana	Psidium guajava	Euphorbia pulcherrima	Bursera simaruba	Calliandra houstoniana	Calliandra houstoniana	Sapindus saponaria
19	Cestrum nocturnum	Calliandra houstoniana	Cecropia obtusifolia	Tecoma stans	Brosimum alicastrum	Inga vera	Annona squamosa	Persea americana	Calliandra houstoniana
20	Manilkara zapota	Annona squamosa	Bursera simaruba	Inga punctata	Hamelia patens	Psidium guajava	Persea americana	Cecropia obtusifolia	Annona squamosa

).

3.3. Species Similarity Analysis

This analysis assessed the species composition across the nine coffee regions in Chiapas, considering both the presence of taxa and their abundance, measured by the number of records for each species in the area. The dendrogram (Figure 3) shows that areas such as Centro and Selva have similar species compositions. Istmo-Costa stands out as the most dissimilar region, suggesting it has a notably distinct tree flora. For example, the presence of species such as Spondias mombin L., Inga flexuosa Schltdl., and Palicourea padifolia (Willd. ex Schult.) C. M. Taylor & Lorence. On the other hand, Sierra, Fraylesca, and Soconusco form a cohesive group, indicating similarity between them.

4. Discussion

4.1. Status of the Chiapas Arboreal Diversity and Its Presence in the Coffee Regions

Chiapas stands out as one of the most biodiverse states in Mexico, itself a megadiverse country, with around 55% of the national tree diversity [27,55]. The percentage of these taxa overlapping with the coffee regions is also significant, accounting for 82% of the total tree diversity of the state (n = 1362 taxa). This could be related to their reported overlap with cloud forests, as well as the arboreal variability across the elevation gradient of the regions [28]. The high concentration of tree diversity within coffee landscapes highlights their strategic importance for national and subnational conservation planning.

While there have been previous studies reporting the tree diversity in Chiapas coffee regions, these appear to be an underestimate. For example, Reyes-Reyes et al. [65] reported 23 species in coffee farms of the Soconusco region in Chiapas. In comparison, Soto-Pinto et al. [35,36] reported 61 to 77 woody species in farms of Northern Chiapas, including both native and introduced species. However, the extensive bibliographic review carried out for this work revealed the presence of 286 species of native trees reported in the coffee farms, contributing to the knowledge of diversity in coffee farms and highlighting the relevance of these farms in the maintenance of the arboreal diversity at the landscape scale. These findings reinforce the role of shade coffee systems as complementary conservation areas within broader forested mosaics.

4.2. Prioritisation Goals

Since species selection plays a crucial role in conservation and reforestation projects, various methodologies have been proposed, each based on different criteria tailored to the specific study objectives [66,67]. This prioritisation method using a balanced approach was designed to fulfil the three goals of the project simultaneously of biodiversity conservation, carbon capture, and improving livelihoods. The selected species have high priority scores across all three objectives (Appendix B), making them excellent candidates for diversification projects in coffee farms. This integrated approach is consistent with current policy frameworks that promote multifunctional landscapes delivering environmental and socio-economic benefits.

In terms of biodiversity conservation, promoting the propagation and use of species that are under a threat category could be useful to preserve the population of the endangered tree species in situ, even though they might be underestimated compared to species that have historically been used on coffee farms [25]. Incorporating threatened taxa into productive systems may therefore complement formal protected-area strategies.

In Mexico, agroforestry has been shown to enhance carbon capture, not only by promoting the growth of new trees but also by preventing the burning of these areas [38,68,69]. In Chiapas, this same result has been reported for silvopastoral systems [70]. The selected species’ rapid growth, significant carbon storage capacity, and the fact that they are not used as fuel contribute to their potential for capturing atmospheric carbon and storing it in the soil [52]. Farm diversification also contributes to storing carbon in the soil, beyond its benefits on aboveground carbon capture. The selection of specific shade-tree species is critical because their unique traits drive essential soil-function pathways: deep-root systems facilitate nutrient cycling via ‘nutrient pumping’ from subsoil layers, while diverse litter inputs and root turnover promote soil aggregation and porosity [14]. These characteristics support the integration of shade coffee systems into climate mitigation and associated carbon capture mechanisms.

Additionally, the reported uses for the livelihood improvement goal could help diversify the income of coffee producers and be utilised within farms to support the growth of coffee plants [60]. Nevertheless, an essential part of the selection process should be the interest of local communities in the tree species, whether this is because their growth does not interfere with the coffee plants’ development or because they could be useful for purposes such as food or medicine [71,72]. Participatory validation therefore remains essential to refine the results of this study and ensure long-term adoption by growers.

4.3. Regional Prioritisation

This is the first work that presents individual species prioritisations for all the coffee regions in Chiapas. Furthermore, the analysis included a considerable number of records for each region (Table 1), adding a precision component that previous studies had lacked by considering the total biodiversity of a state or country [25]. Although the number of records reflects the sampling intensity in each region and has biases, historical data remains our best alternative for understanding the distribution patterns and abundance of species [34]. In data-limited contexts, such approaches provide a pragmatic basis for spatially explicit decision-making.

Selections of suitable trees for shade-grown coffee farms have been done for the Ixhuatán and Tapalapa farms, reporting 25 species with a higher potential to be used as shade trees in the farms [73]. Some of those taxa, such as Cordia alliodora (Ruiz & Pav.) Oken, Persea americana and various Inga species were identified and are also reported in the lists proposed in this paper, supporting the results of our analysis. In contrast to the methodology employed in this paper, Yépez and collaborators conducted interviews with coffee producers to gather data on the attributes used in their prioritisation. While this approach provides valuable insights into the needs and preferences of farmers, it requires intensive fieldwork and is more challenging to scale across larger regions. Our approach complements participatory methods by providing an initial evidence-based shortlist for further local validation, such as the approach carried out in some coffee regions in Veracruz [25].

Fabaceae stood out as the family with the highest number of relevant species in our study, with 11 taxa. This group has been previously documented for its consistent presence in shade-grown coffee farms, as well as the significance of its species to coffee producers [23,39]. Due to its nitrogen fixation ability, Fabaceae species enrich the soil with this essential nutrient for coffee plants and positively influence the biodiversity associated with the organic matter [74,75]. Moraceae and Malvaceae have also been reported for their species’ presence in coffee farms in Mexico, and even in African countries such as Ethiopia [75,76].

Inga Mill., on the other hand, was the genus with more relevant species in the prioritisations (4 spp.): Inga flexuosa Schltdl., I. inicuil Schltdl. & Cham. ex G. Don, I. punctata Willd., and I. vera. In Veracruz, Mexico, previous prioritisations have suggested Inga inicuil, I. vera, and I. punctata as optimal species for reforestation [25]. I. inicuil in particular has also been studied to understand its germination under different temperatures and the effect of climate change on its distribution [77]. In other countries, such as Ethiopia and Costa Rica, it has been shown that farms with Inga densiflora Benth. promote carbon stock, improve water uptake without competing with coffee plants, and provide a better microclimate for the farms without compromising the farmers’ yield [78,79,80].

4.4. Species Similarity Analysis

In addition to its high tree diversity, Chiapas’ arboreal composition differs across regions and climate zones, making it more complicated to establish general species selection methodologies [81,82,83]. In this work, a practical classification of the coffee areas in the state was used to prioritise specific tree species for each geographical region. This procedure assumed that coffee farms in the same region are more similar to each other than those in different areas. In a country like Mexico, where environmental conditions tend to be more influenced by altitude than by geographical distance or political boundaries, this approach is highly recommended.

Our similarity analysis of the arboreous flora, based on the checklist of native tree species and the number of records for each taxon in each area, supports the assumption that floristic composition is more similar among geographically related regions, such as Altos and Norte in northern Chiapas, and Sierra, Fraylesca, and Soconusco in the south of the state (Figure 3). The marked dissimilarity of the Istmo-Costa region compared to the other eight areas may be attributed to its lower mean altitude (75 m a.s.l.) and land use patterns, as it hosts only 655 reported coffee farms, in contrast to the over 50,000 farms documented in the Altos and Norte regions (Table 1) [26]. Reported differences in the environmental, altitudinal, and edaphic variables in Chiapas and their influence on the distribution of tree species support the need to generate specific lists for each of the coffee areas and thus suggest trees that are in line with the characteristics of the study area [83]. The similarity of the species in the top 20 was not assessed because the criteria used in the prioritisation method aim to influence the relevance of the species, and this would no longer reflect the biogeographic relationships of the coffee regions. This distinction is important when interpreting prioritisation outputs for management purposes.

4.5. Relevance of Regional Prioritisations and Future Perspectives

Preserving biodiversity, including arboreal diversity, is one of the most urgent priorities of our time [27]. Since deforestation in Chiapas has been increasing in recent decades, the change in land use or the intensification of management in coffee farms are serious concerns [9,84]. The decrease in shaded coffee farms has direct implications for the loss of biodiversity, highlighting the urgency of finding alternative methods to ensure its permanence [85,86].

Planting a new tree represents a significant commitment for producers, particularly when farm sizes are limited, and the farm already provides substantial shade. In Mexico, most rustic coffee farms are smaller than 2 hectares, which is an important consideration in the species selection process [87]. Filtering out native species that are not reported in the area makes the process more precise and targeted while also aiding in the restoration of the original composition of these disturbed ecosystems.

This methodology can be applied to all coffee regions in Mexico or any other region if a list of tree species is available for the area of study. The values assigned to each criterion could also be modified to fulfil the goals of specific projects. For example, the benefit for growers could be maximised by giving the improving livelihoods criterion a higher weighting. Similar preferences could be given to conservation or carbon capture/storage, and the shift of the prioritised list should be explored in future studies. Considering priority shifts, the ability to shifts weights assigned to a set of criteria, should be useful for decision makers in distinct contexts, such as social programs and environmental legislation. Such flexibility enhances the relevance of the framework for diverse policy and funding contexts.

4.6. Limitations of This Study

Additional field work and feedback from coffee producers are required to improve the prioritisation lists. Multicriteria analyses with input from local producers have proven successful for diversification efforts in coffee farms in the coffee-producing state of Veracruz [25]. Further traits and information, such as nursery availability, establishment difficulty, and shade architecture compatibility, could be considered in future prioritisation efforts. However, this information could be scattered in the literature or simply not available for most taxa, which may reduce suitability for prioritisation efforts in regions with large numbers of species.

Since the main contribution of this study promoting a regional approach, additional fieldwork and flora documentation are recommended to reduce sampling biases and to ensure that the recommended trees are native and have an original distribution in the region, particularly in states as diverse and heterogeneous as Chiapas.

5. Conclusions

This study demonstrates that region-specific, evidence-based prioritisation is an effective tool for guiding shade-tree selection in coffee agroforestry systems. By integrating biodiversity data, expert knowledge, and multi-criteria evaluation, our approach provides tailored species lists that balance biodiversity conservation, carbon storage, and livelihood improvement across the diverse regions of Chiapas. Study results highlight the role of shade-grown coffee landscapes as multifunctional systems that support national and international environmental and development commitments and that can strengthen certification, restoration, and incentive schemes.

Regionally adapted species recommendations improve the effectiveness and adoption of agroforestry interventions by aligning ecological suitability with farmer needs, particularly in environmentally heterogeneous areas. The flexible structure of the framework allows decision-makers to adjust priorities according to policy objectives and local contexts. To maximise impact, future programmes should combine these tools with participatory validation, extension services, and long-term monitoring. Overall, the study provides a scalable decision-support approach to promote climate-resilient, biodiversity-friendly, and socially inclusive coffee landscapes in Mexico and beyond.