Vulnerability of baobab (Adansonia digitata L.) to human disturbances and climate change in western Tigray, Ethiopia: Conservation concerns and priorities

Journal Pre-proof Vulnerability of baobab (Adansonia digitata L.) to human disturbances and climate change in western Tigray, Ethiopia: Conservation concerns and priorities Emiru Birhane, Kidane Tadesse Asgedom, Tewodros Tadesse, Hadgu Hishe, Haftu Abrha, Florent Noulèkoun PII:

S2351-9894(19)30345-2

DOI:

https://doi.org/10.1016/j.gecco.2020.e00943

Reference:

GECCO 943

To appear in:

Global Ecology and Conservation

Received Date: 3 June 2019 Revised Date:

23 January 2020

Accepted Date: 23 January 2020

Please cite this article as: Birhane, E., Asgedom, K.T., Tadesse, T., Hishe, H., Abrha, H., Noulèkoun, F., Vulnerability of baobab (Adansonia digitata L.) to human disturbances and climate change in western Tigray, Ethiopia: Conservation concerns and priorities, Global Ecology and Conservation (2020), doi: https://doi.org/10.1016/j.gecco.2020.e00943. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

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Vulnerability of baobab (Adansonia digitata L.) to human disturbances and climate

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change in western Tigray, Ethiopia: Conservation concerns and priorities

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Emiru Birhane1,2, Kidane Tadesse Asgedom1, Tewodros Tadesse3, Hadgu Hishe1, Haftu Abrha4, Florent

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Noulèkoun5*

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Agriculture and Natural Resources, Mekelle University, P.O. Box 231, Mekelle, Ethiopia

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Department of Land Resources Management and Environmental Protection, College of Dryland

Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life

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Sciences (NMBU), Postboks 5003, INA, 1432 Ås, Norway

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Resources, Mekelle University, P.O. Box 231, Mekelle, Ethiopia

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5

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Biotechnology, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Republic of Korea

Department of Agricultural and Resource Economics, College of Dryland Agriculture and Natural

Institute of Climate and Society (ICS), Mekelle University, P.O. Box 231, Mekelle, Ethiopia Division of Environmental Science and Ecological Engineering, College of Life Science and

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*Corresponding author

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E-mail: [email protected]

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Tel: +82 10 2626 6268

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Abstract

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The increasing rate of land use intensification and the rising evidence of climate change impacts

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raise concerns about the viability of valued non-timber forest product (NTFP)-providing trees.

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This calls for the assessment of the current status and future trajectories of their populations.

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Using population data collected from three land-use types (e.g., grazing lands, riverine areas

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and natural forest) in western Tigray, we evaluated the vulnerability of the multipurpose

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baobab tree (Adansonia digitata L.) to human disturbances and climate change. The study was

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based on the premises that integrating ecological science with modeling tools and local

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knowledge would enhance the overall effectiveness of conservation strategies and community

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support. Therefore, based on field-based inventory, ecological niche modeling and a

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socioeconomic study, we characterized and mapped baobab current and future population

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distribution and documented local knowledge on the uses and management of the species. The

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characterization of the population structure showed that baobab stands were denser with

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larger-sized and taller trees in riverine areas and natural forest compared to grazing lands,

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suggesting adverse effects of human disturbances on its populations. Moreover, positively

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skewed size-class distributions with negative slopes in all land-use types indicated a low

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recruitment of juvenile trees to the adult stage. Climate change simulations using Maximum

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Entropy Algorithm (Maxent) revealed that future temperature increases would lead to

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significant reductions (41-100 %) in baobab suitable habitats due to range contraction. The

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intensive harvesting of baobab leaves, branches and bark and lack of conversation practices as

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indicated by local communities, in combination with the risk of local extinction under future

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climate warming constitute serious threats for the viability of the species in western Tigray. The 2

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results suggest immediate interventions, such as planting baobab at up to 65 m higher in

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altitude, designing appropriate leaf and bark harvest strategies and protecting seedlings from

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livestock, will help guarantee the persistence of the species populations.

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Keywords: Non-timber forest products (NTFPs), size-class distribution, Maximum Entropy

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(Maxent), overgrazing, local ecological knowledge, consensus value

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1. Introduction

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Human activities and climate change are worldwide acknowledged as key drivers of

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biodiversity loss (Mantyka-Pringle et al., 2015; Newbold et al., 2015). Human-driven changes in

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vegetation and land-use have caused habitat losses and the decline of important species,

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leading to reduced species diversity and genetic variability (Banla et al., 2019; Butchart et al.,

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2010; Powers and Jetz, 2019). Notably, the populations of several non-timber forest product

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(NTFP)-providing species are reportedly threatened critically by land-use changes or

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intensifications and unsustainable harvesting as a result of growing demands for NTFPs (Gaoue

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and Ticktin, 2007; Obiri et al., 2002; Schumann et al., 2011, 2010). For example, overgrazing and

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overexploitation of NTFPs such as fruits and foliage can reduce the size of individuals, limit

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recruitment and alter population structure (Gaoue et al., 2017; Gaoue and Ticktin, 2007). At the

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same time, rising temperatures and temperature variability over the last few decades have

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either shifted the latitudinal and altitudinal distribution range of tree species or led to the

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extinction of species for which there were not sufficient habitats at higher altitudes to facilitate

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their migration (IPCC, 2014; You et al., 2018). The latter phenomenon has been called the

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“nowhere to go” hypothesis (Loarie et al., 2009; Nogués-Bravo et al., 2007). “Nowhere to go” is

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particularly relevant for tropical species because it is assumed that they evolved narrow

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climatic tolerances as a consequence of little intra-annual variability in temperature, which

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constitute a strong barrier to range expansion under steep climatic gradients (Blach-Overgaard

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et al., 2010; Colwell et al., 2008). Hence, conservation strategies to safeguard threatened NTFP-

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providing species and thus reduce biodiversity loss could account for both the impacts of

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human disturbances and climate change on their population structure and dynamics. 4

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Previous ecological studies have primarily relied on population structure as a tool to

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investigate the effects of land-use types and human disturbances on the population patterns of

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important tropical NTFP-providing trees (Banla et al., 2019; Noulekoun et al., 2017; Schumann

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et al., 2011, 2010). The approach provides valuable information on the viability of species

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populations based on size-class distributions (SCDs), but is not appropriate to reliably predict

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future population trends because SCD data are a static representation of the population at a

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certain moment in time (Condit et al., 1998; Feeley et al., 2007). However, in the actual context

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of increasing global warming producing rapid shifts in the distribution and abundance of species

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(Lenoir and Svenning, 2013; Vieilledent et al., 2013; You et al., 2018), estimates of the potential

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effect of climate change on the future trajectories of species populations are urgently needed

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to design current conservation and management measures that account for the anticipated

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changes. In this regard, ecological niche modeling (ENM) has been widely used to project

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current species distributions into future climate scenarios, thereby identifying climatic (e.g.,

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rainfall and air temperature) and non-climatic (e.g., topography, soil type) range-limiting factors

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and predicting potential distributional patterns (i.e., range contraction or extension) under the

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assumptions of niche conservatism (Blach-Overgaard et al., 2010; Elith et al., 2006; Noulèkoun

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et al., 2017; Soberon and Peterson, 2005; You et al., 2018). Empirical studies combining both

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population structure and ENM to assess the current and future status of NTFP-providing trees

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remain scarce.

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Practical recommendations for the conservation and management of NTFP-providing

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trees may be more effective with the integration of local ecological knowledge, ecological

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science and modelling (e.g., Mockta et al., 2018). According to Dovie et al. (2008), population 5

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structure analysis may not adequately justify the conservation assessment of the population

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status of NTFP-providing species on its own. Additional information on the knowledge of local

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people regarding the uses, management practices and their impact on plant species are

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required for developing culturally and ecologically effective conservation strategies (Schumann

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et al., 2012). For instance, ethnobotanical studies have improved the understanding of ecology,

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conservation biology and socio-economic values of a number of NTFP-providing species (Dovie

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et al., 2008). Similarly, local perceptions of historical changes in vegetation have provided key

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indicators of long-term population trends of NTFP-providing species populations (Dhillion and

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Gustad, 2004; Dovie et al., 2008). Therefore, the combination of ecological and ethnobotanical

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knowledge has been recommended for effective biodiversity conservation and sustainable

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resource use. There have been only a few studies (Dhillion and Gustad, 2004; Lykke, 1998) that

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integrated both ecological and ethnobotanical knowledge when assessing the viability of NTFP-

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providing species in Africa.

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In this study, we combined ecological assessments, ENM and local knowledge to

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evaluate the vulnerability of Adansonia digitata L. to human disturbances and climate change,

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under the assumption that the integration of these methods may enhance the overall

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effectiveness of conservation and community support. A. digitata L., commonly known as

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baobab is a large and long-living angiosperm tree, largely distributed throughout the savanna

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regions of Africa (Cron et al., 2016; Aida Cuni Sanchez et al., 2011; Wickens, 2008, 1982).

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Baobabs constitute important components of tropical biodiversity and culture. Through the

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provision of a wide range of NTFPs (e.g., barks, fruits, leaves), baobab is one of the most

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important NTFP-providing species with significant ecological and socioeconomic importance in 6

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many African countries (e.g., Assogbadjo et al., 2008; Sidibe and Williams, 2002). The NTFPs of

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baobab are harvested from different land-use types (e.g., croplands, fallows, forests) and even

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in protected areas (Dhillion and Gustad, 2004). However, land-use and harvest intensifications

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(Schumann et al., 2010; Venter and Witkowski, 2010) and climate change (Aida Cuni Sanchez et

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al., 2011) reportedly threaten its populations. The recent demise of some of the oldest and

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largest African baobabs in southern Africa that may be associated with climate change impacts

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(Patrut et al., 2018) and the projected large reduction in the distribution range of baobab under

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future climatic conditions (Aida Cuni Sanchez et al., 2011) may be an indication of what could

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be the fate of this iconic tree under climate change if urgent conservation measures are not

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taken.

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To the best of our knowledge, there has been no published work on baobab which

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assessed the controls of human disturbances and climate change on the structural and

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demographic population characteristics using a merge of approaches including population

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structure, modeling and socio-economic analysis in order to quantify extinction risks and set

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conservation priorities. In this study, we analyzed a unique dataset of 612 baobab trees

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collected from three major land-use types in the western Tigray, Ethiopia, where there had

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been no or little empirical evidence to date regarding the impacts of the ongoing habitat loss

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and fragmentation, increasing human pressure on natural resources and climate change (Aerts,

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2006; Hurni, 1988; Nyssen et al., 2015) on the viability of baobab populations. We hypothesized

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that (i) population demographic and morphological characteristics decrease with increasing

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level of human disturbances, (ii) populations would face the “nowhere to go” predicament

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under future climate warming scenarios, and (iii) local knowledge on the uses, population

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dynamics and management practices will be crucial in setting conservation priorities.

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2. Materials and methods

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2.1.

Species and study area

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A. digitata (Malvaceae) is a gigantic deciduous tree species with a height of up to 25 m

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and a huge trunk that can reach 10-14 m in diameter (Gebauer et al., 2002; Kamatou et al.,

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2011; Wickens, 1982). It is widely distributed through the savanna woodlands of Sub-Saharan

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Africa at altitudes between 450-600 m (Sidibe and Williams, 2002; Wickens, 1982). The tree is

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found at an altitude of 1500 m in Ethiopia (Wilson, 1988). Baobab is commonly restricted to hot

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(up to 42° C) and (semi-) arid areas. However, the species can also thrive in areas receiving as

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low as 90 mm to as much as 1600 mm of annual rainfall (Gebauer et al., 2002; Kamatou et al.,

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2011; Sidibe and Williams, 2002).

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The current study was conducted in two of the largest districts (Welkait and Kafta

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Humera) of the western Tigray, North Ethiopia (Fig. 1). The two districts are located in the semi-

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arid deciduous woodlands, which are characterized by well-drained clayey soils in lowlands and

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a diversity of tree species. The Tekezé River traverses the two districts (Fig. 1). The study area

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experiences hot summers (October to May) with high air temperatures that can reach 42° C in

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Kafta Humera (Niguse and Aleme, 2015). Annual rainfall is highly variable and ranges between

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400–1800 mm. The tree species dominant in the lowlands of the study area include, among

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others, A. digitata, Anogeissus leiocarpus (DC.), Balanites aegyptiaca (L.), Boswellia papyrifera

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(Delile ex Caill.), Terminalia brownii (Fres.) and Zizipus spina-christi (L.). Because the climatic 8

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conditions prevailing in the selected districts match with those of its suitable niche, baobab is

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common and well represented in Welkait and Kafta Humera.

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Subsistence agriculture, livestock rearing, traditional gold mining and resin production

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from B. papyrifera are the main sources of livelihoods for the communities in the study area.

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The 2007 population census estimated a density of approximately 41 and 15 people km-2 in

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Welkait and Kafta Humera, respectively (CSA, 2007). The land use is characterized by a mixed

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farming system dominated by crop cultivation. Farmlands are subject to continuous cultivation

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without proper management. Communal woodlands and the Kafta-Sheraro national park

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located in Kafta Humera are predominantly natural forest. Grazing lands are open for all-year-

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round grazing with low grass and herb biomass, which prevents frequent bush fires. Camels,

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cattle, donkeys, goats, mules and sheep are the common livestock types in the study area.

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Baobab inhabits all the aforementioned land-use types.

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Insert Fig.1

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2.2.

Tree sampling

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Trees were sampled in three land-use types from December 2016 to February 2017:

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natural forest, riverine areas and grazing lands (Supplementary Information, Fig. S1). The land-

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use types were selected according to an increasing gradient of human pressure. The natural

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forest was assumed to experience only slight human disturbances (i.e., minimal cropping,

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livestock grazing and browsing and NTFP harvesting). The grazing lands were the most

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disturbed land-use type due to the occurrence of livestock grazing and browsing and NTFP

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harvesting activities. The riverine areas are exposed to intermediate human disturbances.

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Inventory was conducted on each land-use type using strip transects of 5 ha (i.e., 1 km

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long and 50 m wide)(Venter and Witkowski, 2010). The number of transects installed in each

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land use type was proportional to its size: 15 transects in natural forest, 12 transects in grazing

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lands and 11 transects in riverine areas, which gave 38 transects in total. All the trees

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encountered during the transect walks were counted and measured for their diameter at breast

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height (dbh) and total height using a measuring tape and Sunnto clinometer, respectively. Only

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trees with the intact stem (i.e., without a false cavity) were considered and measured during

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the sampling. The geographic coordinates of individual baobab tree were recorded as decimal

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latitude/longitude using a hand-held Garmin 60 GPS.

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2.3.

Socio-economic data collection

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To collect socio-economic data, four (i.e., two from each of the selected districts) lower–

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level administration units locally called “tabias” were selected purposively based on the

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endowment of baobab trees. From these four tabias, the lists of the farm household population

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were obtained from the tabia administrations and used as sampling frames to select the final

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sample of farmers. In the end, a sample of 120 households was randomly selected from the

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four tabias based on the probability to proportional size (PPS) approach (Hansen and Hurwitz,

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1943).

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The collection of household-level socio-economic data, which included personal and

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private data such as income, needed to be implemented in such a way that as much accurate

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data as possible were collected. The household head from each of the sample households, who

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could be a male or female, was identified as the person with whom interviews needed to be 10

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conducted. Having identified the type of data to be collected, structured questionnaires were

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developed as an instrument to collect the socio-economic data. Given such an instrument, the

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best way to complete the questionnaires and collect the required data was through face-to-

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face interviews with household heads or their spouses when the head was not available. Thus,

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enumerators were hired and trained on the purpose of the study and how to implement the

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interviews to assist in socio-economic data collection through face-to-face interviews. The

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questionnaires were pretested on a small number of farm households to obtain useful feedback

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and implement the face-to-face interview for the main survey in a better way. For this, the

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questionnaire was translated into local language (“Tigrigna”) by the research members, who

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also translated the data back to English. Important feedback on baobab related issues were

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incorporated in the questionnaires after which the final and full survey was conducted where

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enumerators made door-to-door visits to the selected farmers and filled the questionnaires.

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Socio-economic data collected, among others, included (i) the uses of the different parts of the

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baobab tree and the contribution of baobab harvesting to the household income, (ii) the local

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knowledge on the status of baobab population (e.g., decreasing, increasing, or stable) and (iii)

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the existing conservation measures to maintain the species population.

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To obtain more detailed and community-level data related to the importance of baobab,

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four focus group discussions (FGDs; one group in each tabia) were held. Each FGD consisted of

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8 members including both males and females, local elders and model farmers, who were

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purposively selected based on their knowledge of the communities’ socio-economic and

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agricultural conditions and of the baobab tree. The FGDs were moderated by researchers who

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have experience in conducting such discussions and know what data to collect. Each focus 11

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group discussed social, economic, environmental and management aspects of the baobab tree.

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During the discussions, researchers collected mainly qualitative data by writing down important

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notes. The FGDs were used to triangulate pertinent responses given during the interviews with

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the sample households (Caillaud and Flick, 2017; Flick, 1992).

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2.4.

Statistical data analyses

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Following (Venter and Witkowski, 2010), the trees were divided into juveniles (< 99 cm

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dbh) and adult (> 100 cm dbh). The total tree density (stem ha-1), the density of juvenile and

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mature trees and dendrometric parameters including dbh (cm), height (m) and basal area (m²

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ha-1) were compared between the land-use types using one-way ANOVA. Significant effects of

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land-use type on the population structure and demographic parameters were identified at a 5%

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level using the least significant difference (LSD) test.

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The structure of the baobab population was further characterized graphically using size-

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class distributions (SCDs). The size-classes were constructed as 50 cm increments in dbh, i.e. 0-

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49 cm, 50-99 cm, …, > 450 cm (Venter and Witkowski, 2010). Generalized linear models (GLMs)

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using a negative binomial error distribution to account for overdispersion was run to test the

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similarities between SCDs of the land-use types using the number of individuals per size-class

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and the size-class mid-point as dependent and independent variables, respectively. The median

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dbh (Feeley et al., 2007), the coefficient of skewness (g1; Bendel et al., 1989) and the SCD slopes

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(Condit et al., 1998; Lykke, 1998) were used to evaluate and compare the species population

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trends between the land-use types (Noulekoun et al., 2017; Venter and Witkowski, 2010). For a

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given land-use type, the population with a lower median dbh is expected to increase more 12

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rapidly in abundance than the populations at other land-use types. Negative coefficients of

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skewness (g1 < 0) and slopes are indicative of a population with relatively more small-sized

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trees than large-sized trees (i.e., good recruitment), while positive g1 and slopes indicate poor

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recruitment with more individuals in larger size-classes compared to smaller size-classes.

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Moreover, the steepness of the slopes was used to describe recruitment trends: for example,

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steep negative slopes indicate a better recruitment than shallow slopes (Bendel et al., 1989;

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Lykke, 1998; Mwavu and Witkowski, 2009; Obiri et al., 2002).

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The quotients (Qs) between the number of trees in successive size-classes (Shackleton,

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1993; Botha et al., 2004) and the Permutation Index (PI; (Wiegand et al., 2000) provided

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additional information on the stability of the species population. The Qs tend to a constant

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value in a stable population but fluctuate when a population is unstable. Likewise, a population

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showing a discontinuous SCD will have a PI > 0 while a monotonically declining population will

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have a PI = 0.

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To assess the local knowledge on the uses, population development and conservation

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measures, a combination of content analysis through the calculation of descriptive statistics

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and quantitative ethnobotanical analysis was used. The uses were qualitatively categorized into

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different groups based on the type of contribution (e.g., food, medicine and construction). We

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computed the following ethnobotanical indices to determine the distribution of knowledge

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regarding the uses: informant diversity value (ID), informant equitability value (IE), use-diversity

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value (UD) and use equitability value (UE) (Supplementary Information, Table S1; Monteiro et

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al., 2006; Schumann et al., 2012). Furthermore, the degree of agreement among respondents

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regarding the parts of the tree used, the perception on the trend of the species population and 13

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the management activities were assessed by computing the Consensus value (Supplementary

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Information, Table S1; Monteiro et al., 2006; Schumann et al., 2012). The relative contribution

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of the income gained from the harvest of baobab to the households’ overall income was

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analyzed using descriptive statistics.

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2.5.

Climate change impact simulations

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We modelled the effects of climate change on the distribution of the species using the

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Maximum Entropy algorithm (Maxent; v 3.3.3.e) (Phillips et al., 2006). Maxent approximates

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the realized niche of a species by relating geographic locations and environmental data to

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derive the response of species probability of occurrence to environmental gradients. The

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responses are in turn applied to the same data to reconstruct the geographical distribution of

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the species probability of occurrence (Phillips et al., 2006; Trabucco et al., 2010). Maxent has

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been successfully used to assess the impacts of climate change on the distribution of important

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tropical tree species such as Prunus africana Hook.f. (Mbatudde et al., 2012), Tamarindus indica

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L. (Fandohan et al., 2011), Faidherbia albida A. Chev. (Noulèkoun et al., 2017). It has reportedly

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outperformed other SDM algorithms (Elith et al., 2006; Phillips et al., 2006; You et al., 2018),

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especially at small samples sizes, due to its use of lasso regularization (El-Gabbas and Dormann,

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2018). Furthermore, using an ensemble ENM approach, You et al. (2018) reported that Maxent

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had the highest performance when compared to mean ensemble and multi-model mean

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ensemble methods.

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2.5.1. Presence and environmental data 14

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The 612 GPS records of individual baobab tree were inserted into Maxent along with the

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environmental data to predict the current and future potential distributions of the species. The

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environmental dataset consisted of the 19 bioclimatic variables (Hijmans et al., 2005) and three

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non-climatic (habitat) variables: altitude, aspect and a soil (type) (Supplementary Information,

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Table S2). The current (~1950-2000) and future (2050, 2070) bioclimatic data and the altitude

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data were downloaded from the WorldClim database (Hijmans et al., 2005) and clipped down

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to the extension of the map of Tigray in Arc GIS 10.2. The soil types were extracted from the

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FAO/ UNESCO Soil Map of the World (http://www.fao.org/soils-portal/en/). The aspect map

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was derived from the Digital Elevation Model (DEM) map of the study area

299

(http://ned.usgs.gov). All the 22 layers were at a 1 km scale.

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To represent the future climates in 2050 (2041-2060) and 2070 (2061-2080), we used

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the projections of three GCMs (CCSM4, ACCESS1-0 and MIROC5) for two Representative

302

Concentration Pathways (RCPs) scenarios (i.e., 4.5 and 8.5). The RCP scenarios represent

303

climate conditions under which the radioactive forcing is projected to increase by 4.5 and 8.5

304

Watts per square meter (W/m-2) by the year 2100 (van Vuuren et al., 2011). The average output

305

of the three GCMs was used for the prediction of the future probability of occurrence of

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baobab to reduce model uncertainty due to structural dissimilarities among GCMs.

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2.5.2. Model performance, current and future suitability maps

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The ability of Maxent to predict the observed spatial distribution of the species was

310

assessed by splitting the occurrence data into random training (80 %) and test (20 %) datasets

311

and by randomly selecting 10 000 pseudo-absence points from the whole study area, which 15

312

were associated to the presence data to form the presence-absence dataset. Twenty replicates

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and 5 000 iterations were performed for each run to cater for the small number of occurrence

314

points (Noulèkoun et al., 2017). The accuracy of the model predictions was evaluated using the

315

Area Under the receiver operating characteristic Curve (AUC), which is a standard and widely

316

used threshold-independent measure of the prediction accuracy of SDMs (Araújo et al., 2004;

317

Phillips et al., 2006). Perfect discrimination between suitable and unsuitable cells in the model

318

yields the best possible AUC of 1.0, whereas an AUC of 0.5 or less indicates that the model has

319

poor to no predictive ability.

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The suitability maps from the logistic output in Maxent was derived using a minimum

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suitability threshold of 10 % because we assumed that there may exist presence data arising

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from uncontrolled factors such as recording errors, ephemeral populations, presence of

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unusual microclimatic conditions within a cell (e.g., (Mbatudde et al., 2012, 2013; Noulèkoun et

324

al., 2017). The binary absence -presence (0 or 1) map of the current distribution of the species

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was produced using the ten-percentile training presence threshold (0.43) generated by the

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model. Thus, the set of grid cells for which the absence-presence prediction was greater than

327

0.43 delineated the present suitable area (SAp, % of the study area)(Vieilledent et al., 2013).

328

Future suitable areas (SAf) for the species were identified by overlaying and reclassifying the

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binary rasters of current and future potential distributions, thus defining four levels of

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suitability ranging from unsuitable (high-impact areas) to highly suitable (new suitable areas)

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(Scheldeman and Zonneveld, 2010). The results were visualized in DIVA-GIS 7.5.

332

16

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2.5.3. Contribution of environmental variables to the potential distributions of the

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species

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The percent contribution, the internal jackknife test and the partial dependence curves

336

were used to evaluate the importance of each environmental factor for predicting the current

337

distribution of baobab (Phillips et al., 2006; Mbatudde et al., 2013; Noulèkoun et al., 2017).

338

Important variables showed high percent contribution, high permutation importance and high

339

decrease in gain when removed from the model. The curves permitted a graphical

340

interpretation of the dependence of the predictions on the variables (Phillips et al., 2006).

341

Despite the reported high correlations between the bioclimatic layers, we kept all variables in

342

our analyses because (i) they individually describe different environmental properties that we

343

did not want to lose and (ii) multicollinearity does not affect the overall spatial prediction in

344

Maxent (Blach-Overgaard et al., 2010).

345 346

3. Results

347

3.1.

Population structure

348

The total density of baobab population across the three land-use types was 3.2 (SE +

349

0.3) trees ha-1, with a relatively higher number of adult trees compared to juvenile trees (Table

350

1). Between land-use types, riverine areas had the highest total density of trees, mainly due to

351

the significantly higher number of juvenile trees. A similar trend was observed for the basal

352

area across land-use types. In contrast, the biggest and tallest trees were recorded in natural

353

forest (Table 1).

354

Insert Table 1 17

355

The SCDs for the whole baobab population and the different land-use types was

356

positively skewed (g1 < 0 and SCD slopes < 0) and dominated by individuals in the 50-200 cm

357

dbh size-classes (Fig. 2). The upper quartiles of the SCDs were characterized by the dearth of

358

trees with dbh > 400 cm (i.e., mother trees). The best recruitment was observed in riverine

359

areas, which had the lowest median dbh, the steepest slope and the most positively skewed

360

SCDs (Table 2).

361

Insert Table 2 and Fig. 2

362

Overall, the Qs between successive size-classes showed that the baobab trees were not

363

evenly distributed in the study area (Fig. 3). However, the baobab populations tended to be

364

more evenly distributed in the smaller size-classes compared to larger size-classes where large

365

fluctuations indicative of an uneven distribution were observed (Fig. 3). The PIs for the whole

366

population as well as for the populations in the different land-use types were greater than zero

367

(Table 2), indicating that the size distributions are discontinuous (i.e., bigger trees are more

368

frequent than a preceding size class). The PI was higher in the natural forest than in the other

369

land-use types (Table 2).

370

Insert Fig. 3

371 372

3.2.

Current and future distributions

373

Maxent showed a very good performance in modeling the distribution of baobab in the

374

study area. This was evidenced by the AUC value of 0.996, which is higher than would be

375

expected at random. The most important environmental variables associated with the

18

376

distribution of baobab were bioclimatic and related either to extreme temperature (bio 5,

377

maximum temperature of warmest month) or temperature range at shorter (bio 2, mean

378

diurnal range) and longer (bio 7, temperature annual range) temporal scale (Table 3;

379

Supplementary information, Fig. S2). The top-performing variable for predicting the distribution

380

of baobab was temperature annual range. To a lesser extent than temperature, precipitation

381

(bio 17, precipitation of driest quarter) was also identified as a significant factor explaining the

382

distribution of the species.

383

Insert Table 3

384

The SAp of the baobab covered 11.8 % of the study area and spanned the low- and

385

Midland areas in the north- and southwestern zones (Table 4, Fig. 4A). The species suitable

386

niche is found in arid and semi-arid areas with high temperature annual range (> 23 °C), high

387

maximum temperature of warmest month (> 38° C), high mean diurnal range between (> 16)

388

and very low precipitation of driest quarter (< 19 mm) (Table 4; Supplementary information,

389

Fig. S3). Such environmental conditions are characteristic of arid and semi-arid areas, where the

390

species is known to occur.

391

Insert Table 4 and Fig. 4

392

The future predictions showed a substantial decrease in the suitable habitats of the

393

baobab over time due to an increase of high impact areas under the effect of climate change

394

(Figs. 4B-E). The SAp dropped from 11.8 % under the current climate conditions to 7, 2 and 2.5

395

% in 2050-RCP 4.5, 2050-RCP 8.5 and 2070-RCP 4.5, respectively. New suitable areas are

396

predicted under 2050-RCP 4.5, but with a very low area coverage (0.14%; Fig. 4B; 19

397

Supplementary information, Table S3, Fig. S4). The disappearance of the suitable habitats (SAf =

398

0 %) of the species is likely to take place before 2070 under RCP 8.5, leading to its potential

399

extinction in the study area (Fig. 4E). The predicted changes observed in the suitable niche of

400

the species were mainly associated with maximum increases of temperature annual range

401

(+0.4° C) and maximum temperature of warmest month (+2.2° C) in the study area across the

402

emission scenarios and the time slices (Table 4).

403 404

3.3.

Uses, population status and management

405

Interviews and FGDs revealed that baobab is harvested by local people for 7 types of

406

uses, which were grouped into three main categories: food, medicine and construction (Table

407

5; Supplementary information, Table S4). The highest UD and UE were found for construction

408

category, followed by food and medicine, indicating that among study participants,

409

construction was the most important use category and that knowledge about the use of the

410

baobab tree in construction was more widely and homogenously distributed within the

411

community compared to the other categories (Table 5). The total ID and IE values were medium

412

(~0.5), indicating that among study participants, the overall knowledge on the uses of the

413

species was more or less homogenously distributed among respondents. Among participants,

414

the consensus value for the plant part (CPP) was highest for bark, followed by fruit pulp, leaves

415

and seeds (Table 5). The lowest CPP was recorded for roots (0.03). Bark, fruit pulp and leaves

416

were reported to be the most used parts of the species because they serve different purposes

417

including construction of rope and cordages, human food, human and veterinary medicine and

418

fodder (Supplementary information, Table S4). Debarking and pruning of the tree, usually after 20

419

leaf flush, were unanimously mentioned by the participants as the most intensive uses of

420

baobab, which caused severe damages to the baobab trees.

421

The results of the socioeconomic analysis revealed that the harvest and sale of baobab

422

products represented only 2.5% of the mean annual households ‘income. The significant

423

portion of income from baobab came from its sale as human foods and construction ropes. It

424

was also observed that households had limited awareness of the economic importance of

425

baobab. For instance, only 37.5% of the respondents knew that they could generate income

426

from the sale of the tree’s parts for medicinal purpose.

427

All participants perceived that the number of baobab trees decreased in the area

428

(consensus value for population development, CPD = 1) and they did not report practicing any

429

conservation activities to protect baobab trees (consensus value for conservation practices, CCP

430

= 1). The decline was attributed to the excessive cutting of leaves and branches, excessive

431

debarking, free grazing, agricultural expansion, climate change and the absence of conservation

432

and management practices.

433 434

4. Discussion

435

4.1.

Population structure as affected by human disturbances

436

The differences in baobab population characteristics and dynamics between the land

437

use types revealed important patterns and contrasts. Adult and juvenile densities, basal area

438

and recruitment rates (judged by the SCD slopes, g1 and median dbh) were lower in the land-

439

use type experiencing higher human disturbances (grazing lands). This finding corroborates

440

with previous studies, which underlined the adverse effects of human activities on the 21

441

demography and recruitment of baobab in Namibia (Lisao et al., 2018, 2017), Mali (Dhillion and

442

Gustad, 2004) and a semi-arid savanna of Burkina Faso (Schumann et al., 2010). Livestock

443

browsing and trampling are likely the major causal factors for the low baobab population

444

density and recruitment on grazing lands. Similar factors reportedly affected the population

445

structure of baobab stands in human-modified areas (Dhillion and Gustad, 2004; Schumann et

446

al., 2010; Venter and Witkowski, 2013). However, this result supported partially our hypothesis

447

that baobab population demography decreases with increasing level of human pressure

448

because the highest adult and juvenile densities were recorded in riverine areas while similar

449

SCD patterns were observed in both natural forest and grazing lands. Moreover, the baobab

450

population was more stable in riverine areas as evidenced by the best recruitment success

451

(relative to adult populations) and the lowest Dm and PI.

452

The higher population density and better recruitment in riverine areas compared to

453

natural forest suggest that human disturbances may not necessarily be the primary factor that

454

determines baobab demographic patterns as previously reported (Dhillion and Gustad, 2004;

455

Schumann et al., 2011, 2010). Ultimately, the prevailing high moisture in riverine areas

456

contributed to the observed healthy baobab stands in this land-use type, likely due to the

457

importance of moisture for breaking baobab seed dormancy, facilitating the establishment and

458

growth of seedlings and fulfilling the water storage capacity of mature trees (Cuni Sanchez et

459

al., 2010; Lisao et al., 2018). For instance, baobab seedlings exposed to higher moisture

460

conditions established and grew faster than those exposed to lower moisture conditions (A.

461

Cuni Sanchez et al., 2011). Relatively denser baobab stands were likewise observed in wetter

462

areas compared to drier areas (Lisao et al., 2018). 22

463

The similarities in the SCDs between the grazing lands and the natural forest may be

464

attributed to the ecological role of human activities in seed dispersal, seedling establishment

465

and preservation of mature trees. Dhillion and Gustad (2004) reported that some intentional

466

(e.g., protection of trees) and unintentional (e.g., seed dispersal through garbage) human

467

activities are beneficial to baobab populations. On the grazing lands, juvenile and mature trees

468

are preserved by farmers because of the various products they derived from them, as

469

mentioned during the interviews. This has likely resulted in similar population density and

470

recruitment rate in grazing lands and natural forest, despite the contrasting level of human

471

disturbances. However, the low number of baobab trees of smaller sizes in both land-use types

472

indicates an ageing population (Schumann et al., 2010). This is particularly evidenced in the

473

natural forest by the high value of Dm, indicative of a population at a relatively steady state

474

(Feeley et al., 2007).

475

The presence of bigger and taller trees in the natural forest compared to grazing lands

476

and riverine areas supports our hypothesis that the tree morphological characteristics decrease

477

with increasing human disturbances and underlines the effects of NTFPs harvesting on baobab

478

population structure. The interview results revealed that the baobab trees were harvested

479

mainly for their back, fruits and leaves, as reported by several previous studies (Dhillion and

480

Gustad, 2004; Schumann et al., 2010). The higher intensities of pruning and debarking in

481

grazing lands and riverine areas than in natural forest may explain the differences observed in

482

population morphological traits between the land-use types.

483 484

4.2.

Vulnerability of A. digitata to future climate change 23

485

The ecological niche modeling suggested that the baobab would have a more restricted

486

geographic range and hence would decrease in abundance with future climate warming, as

487

evidenced by the significant reduction (41-100%) in its suitable habitats along with no to very

488

low occurrence of new suitable areas. Importantly, the high vulnerability of the species to

489

extreme warming (RCP 8.5) would result in its potential local extinction by the year 2070. These

490

results imply that there is likely to be insufficient suitable habitats to facilitate the latitudinal

491

and altitudinal range shifts or migration of the species under future climate conditions. Thus,

492

the projections of the baobab future distributions in the study area are consistent with the

493

predictions of the “nowhere to go” hypothesis (Loarie et al., 2009; Nogués-Bravo et al., 2007).

494

Sanchez et al. (2011) found that A. digitata exhibited similar patterns of range contraction and

495

extinction under future climate change in West and East Africa. Vieilledent et al. (2013)

496

reported more variable patterns for three endangered Malagasy baobab species, but two of the

497

baobab species Adansonia perrieri (Capuron) and Adansonia suarezensis (H. Perrier) exhibited a

498

decrease in suitable habitats and potential extinction under climate warming, respectively.

499

The observed patterns of range shifting for the baobab may be explained by the

500

projected increase in future temperature (e.g., up to 2°C for the maximum temperature of

501

warmest month), which was the most important factor influencing the distribution of the

502

species in the study area. Colwell et al. (2008) and Blach-Overgaard et al. (2010) reported that

503

little opportunity exists for latitudinal range shifts from warming within the tropics because the

504

mean annual temperature remains approximately constant between -21° and 21° latitude.

505

Consequently, warmer temperatures under future climate conditions would transform the

506

baobab suitable habitats into unsuitable areas, but the modest latitudinal and temperature 24

507

gradients within the study area would constrain range shifts (Blach-Overgaard et al., 2010;

508

Lenoir and Svenning, 2013) and result in the potential extinction of the species. Nonetheless,

509

the commonly reported upward range shifts for tropical species in response to warming

510

climates (e.g., Colwell et al., 2008; Lenoir and Svenning, 2013) was observed for the species

511

under the low emission scenario (2050-RCP 4.5), although with a very small upward rate of

512

range shifting (Supplementary information, Table S3, Fig. S4). Our findings suggest that species

513

restricted to warm extremes such as the African baobab may also have nowhere to go to

514

escape climate warming as previously reported for cold-adapted montane species (e.g.,

515

Nogués-Bravo et al., 2007; You et al., 2018).

516

Given that climate projections are associated with some degree of uncertainty and that

517

greenhouse gas emissions by human societies fluctuate (IPCC, 2014), our findings on the future

518

range shifts of baobab should then be treated with caution. Other sources of uncertainty

519

related to biological mechanisms (e.g., dispersal barriers) and species adaptation to climate

520

change (e.g., phenotypic plasticity, gene flow) may also be associated with the future

521

projections. So far, such mechanisms remain difficult to identify, quantify and include in ENM

522

(Vieilledent et al., 2013). Nonetheless, the agreement of our results with previous findings on

523

the vulnerability of the baobab species to future warming supports the reliability of our

524

projections.

525 526

4.3.

Conservation concerns and priorities for the viability of A. digitata

527

Previous studies aiming to understand the dynamics of baobab populations have shown

528

that low recruitment rates and positively skewed SCDs are not of major concern for the 25

529

maintenance of the populations because long-lived species such as baobab can sustain

530

population levels with low or episodic recruitments (Condit et al., 1998; Dhillion and Gustad,

531

2004; Schumann et al., 2010; Venter and Witkowski, 2010). However, the fluctuating quotients

532

(Qs) in the higher diameter classes (Fig. 3) indicative of the negative impacts of selective NTFP

533

harvesting, the dearth of mother trees, and the potential extinction of the species under future

534

climate warming raise major conservation concerns over the long-term. These findings were

535

supported by the results of the assessment of the local knowledge on the uses and

536

management of the species, which was commonly shared among the respondents as showed

537

by the informant diversity values. The discussions with focus groups, interviews of the

538

households and use-diversity values revealed that intensive and improper debarking and

539

pruning of leaves for fodder, browsing by animals due to free grazing, climate change and lack

540

of conservation practices are major threats to the viability of the species. This reported high-

541

use pressure on the baobab, predicted to increase due to land-use intensifications, will likely

542

negatively affect the overall tree productivity and lead to the degradation of the species

543

populations if adapted management strategies to ensure the persistence of the species are not

544

adopted (Dhillion and Gustad, 2004; Schumann et al., 2012, 2010). Despite the high pressure on

545

baobab trees as a consequence of excessive NTFPs harvesting, the resulting economic benefits

546

appear to remain very low, reflecting the limited awareness of its economic importance and the

547

high use of the harvested NTFPs for domestic purposes. These observations indicate an

548

opportunity for awareness creation on sustainable harvesting and management (e.g., moderate

549

leaf harvesting and debarking) of the species and the associated environmental benefits. The

550

current practice of preserving baobab trees on agricultural lands could be maintained while the 26

551

protection of seedlings and saplings using silvicultural measures such as physical barriers to

552

prevent livestock browsing, irrigation and installation of water collection structures (Dhillion

553

and Gustad, 2004; Venter and Witkowski, 2013) could be promoted to increase recruitment

554

success.

555

Rising temperatures would pose a serious threat to the survival of baobab in the study

556

area under future climatic conditions. The occurrence of new suitable areas and associated

557

upward shifts in altitude under the 2050-RCP 4.5 means that the baobab trees may survive in

558

higher altitude (up to 65 m; Table 4) than currently under a low emission scenario. Therefore, in

559

order to sustain baobab populations in the near future, the new suitable areas could be

560

targeted for the in situ conservation of existing stands through the design of effective protected

561

area networks and the planting of seedlings and saplings (e.g., Sanchez et al., 2011; Vieilledent

562

et al., 2013). In contrast, under the high emission scenario where high to complete mortality is

563

predicted, priority may be given to ex situ conservation in germplasm collections, gene banks or

564

orchards and assisted migration to ensure the long-term viability of the species (Aida Cuni

565

Sanchez et al., 2011). For the latter, experimental planting of baobab in areas experiencing

566

current maximum temperatures greater than 41°C might help to assess the drought tolerance

567

of the species and identify microsites favorable for its occurrence under future climate

568

warming.

569 570

5. Conclusion

571

This study provides novel insights into the much-needed evidence of the impacts of

572

human disturbances and climate change on the populations of the highly used baobab tree in 27

573

the western Tigray. One of the strengths of our study is the integration of ecological and

574

socioeconomic assessments with ENM, which allowed us to examine both current and future

575

population trends and propose potential management options to foster the conservation of the

576

species. Our results revealed that population demography and traits were negatively affected

577

by human-induced activities (e.g., livestock grazing and NTFP harvesting) and microsite

578

environmental conditions, particularly moisture availability. Despite its wide climatic

579

tolerances, future temperature increases are likely to reduce range sizes and lead to the

580

potential extinction of the species under the high emission scenario as predicted by the

581

“nowhere to go” hypothesis, emphasizing how vulnerable species that have narrower climatic

582

tolerances would be to climate warming. Together, the evidence herein underscores an urgent

583

opportunity to support the design of conservation strategies that help anticipate the expected

584

climate change impacts such as assisted migration and the need for awareness creation on the

585

best management practices that secure the maintenance of baobab populations over the long

586

term.

587 588

Declarations of interest

589

None

590 591

Acknowledgments

592

The authors are thankful to the Steps Toward Sustainable Forest Management with the Local

593

Communities in Tigray, Northern Ethiopia (ETH 13/0018) for the provision of a research grant to

594

support this work. We are also grateful to the respondents. 28

595 596

Appendix A. Supplementary Information

597 598

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599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637

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33

814 815 816

Table 1 Demographic (total, adult and juvenile density) and morphological characteristics (basal area, dbh and height) of baobab populations in three land-use types and for the whole population in two districts (Welkait and Kafta Humera) of western Tigray. Land-use types Total density Adult density Juvenile density Basal area (m2 DBH (cm) Height (m) (stem ha-1) (stem ha-1) (stem ha-1) ha-1) Grazing lands 2.4b (+ 0.3) 1.4b (+ 0.2) 1.0b (+ 0.3) 4.0b (+ 0.7) 123.3b (+ 6.5) 15.5b (+ 0.6) b ab b ab a Natural forests* 2.6 (+ 0.4) 1.8 (+ 0.2) 0.8 (+ 0.2) 5.6 (+ 0.9) 142.7 (+ 6.2) 17.0a (+ 0.5) Riverine areas 5.0a (+ 0.8) 2.5a (+ 0.5) 2.5a (+ 0.6) 7.9a (+1.9) 114.1b (+ 5.1) 14.8b (+ 0.5) Whole population* 3.2 (+ 0.3) 1.9 (+ 0.2) 1.4 (+ 0.2) 5.8 (+ 0.7) 125.3 (+ 3.4) 15.7 (+ 0.3)

817 818 819 820

DBH: diameter at breast height. Within the same column, the means with the same superscript are not significantly different at p < 0.05 between the land-use types. *indicates significant differences between adult and juvenile density within a land-use type according to the paired t-tests.

34

821 822 823 824

Table 2 Size-class distribution (SCD) slopes, median dbh (dm), skewness coefficient (g1) and Permutation Index (PI) for baobab populations in three land-use types and for the whole population in two districts (Welkait and Kafta Humera) of western Tigray. Land-use types SCD slope Dm (cm) g1 PI Estimate SEM R² Grazing lands - 1.26* 0.44 0.51 114.8 - 0.59 9 Natural forests - 0.90* 0.33 0.48 134.5 - 0.69 13 Riverine areas - 1.52* 0.47 0.57 98.7 - 0.50 7 Whole population - 1.22*** 0.23 0.50 114.2 - 0.62 7

825 826

SEM: standard error of the mean; R²: coefficient of determination Significance levels: “*” 0.01, “**” 0.001, “***” 0

35

827 828 829

Table 3 Relative contribution (%) of the environmental variables to the Maxent model. More details on the 19 bioclimatic variables are presented in Supplementary information, Table S2. Variables bio7 bio5 bio17 bio2 bio4 bio16 bio6 bio15 bio18 bio19 bio9 bio14 Aspect bio11 Soil Altitude bio10

Description Temperature Annual Range Maximum Temperature of Warmest Month Precipitation of Driest Quarter Mean Diurnal Range Temperature Seasonality Precipitation of Wettest Quarter Min Temperature of Coldest Month Precipitation Seasonality Precipitation of Warmest Quarter Precipitation of Coldest Quarter Mean Temperature of Driest Quarter Precipitation of Driest Month Aspect Mean Temperature of Coldest Quarter Soil type Altitude Mean Temperature of Warmest Quarter

Percent contribution 35.3 28.1 13.8 9.2 5.4 2.3 1.7 1.2 0.9 0.6 0.4 0.4 0.3 0.2 0.2 0.1 0.1

830 831

36

832 833 834

Table 4 Environmental conditions range defined by the most important climatic variables and altitude in the suitable areas under the current climate and future climate scenarios. Climate Current 2050-RCP 4.5 2050-RCP 8.5 2070-RCP 4.5

bio 7 (°C) 23.6-28.2 23.2-28.6 23.3-27.2 22.9-28.5

bio 5 (°C) 38.3-41.2 39.8-43.0 40.1-43.0 40.6-43.4

bio 2 (°C) 1.67-1.88 1.65-1.86 1.61-1.79 1.63-1.86

bio 17 (mm) 0-19 0-18 0-9 0-14

Altitude (m) 573-1217 573-1282 578-1149 573-1153

835

37

836 837 838

Table 5 Summary of the quantitative measurements of the knowledge about baobab, the use categories and the different plant parts used in the study communities. Total number of respondents Number of use-types Number of used plant parts Knowledge diversity indices Informant diversity value (ID) Informant equitability value (IE) Use diversity value (UD) for food Use diversity value (UD) for medicine Use diversity value (UD) for construction Use equitability value (UE) for food Use equitability value (UE) for medicine Use equitability value (UE) for construction Consensus value for plant part (CPP) Leaves Fruit pulps Bark Roots Seeds

120 7 5 Mean 0.50 0.70 0.27 0.20 0.53 0.51 0.38 1.00 Mean 0.22 0.28 0.31 0.03 0.15

839 840 841 842

38

843

Figure captions

844

Fig. 1. Map of the study area, showing the location of Kafta-Humera and Wolkait districts in

845

Tigray, the surveyed baobab occurrence sites and the Tekezé River network.

846 847

Fig.2. Size (dbh)-class distributions (in 50 cm intervals) of baobab populations in three land-use

848

types (A-C) and for the whole population (D) in two districts (Welkait and Kafta Humera) of

849

western Tigray.

850 851

Fig. 3. Quotients between baobab density in successive dbh size-classes (in 50 cm intervals) for

852

three land-use types and the whole population in two districts (Welkait and Kafta Humera) of

853

western Tigray.

854 855

Fig.4. Distribution maps of baobab showing the evolution of the suitable habitats under climate

856

change in Tigray region, Northern Ethiopia. Presented are the map of the present suitable area

857

(SAp; A) and the maps of the future suitable area (SAf) under 2050-RCP 4.5 (B), 2050-RCP 8.5 (C),

858

2070-RCP 4.5 (D) and 2070-RCP 8.5 (E). Note that no habitat would remain suitable by 2070

859

under the high emission scenario (RCP 8.5).

860 861

39

1 2 3

Fig. 1. Map of the study area, showing the location of Kafta-Humera and Wolkait districts in

4

Tigray, the surveyed baobab occurrence sites and the Tekezé River network.

5

1

6

(A) Grazing lands

1.2

1.6 Stem ha-1

Stem ha-1

1.6

0.8

1.2 0.8

0.4

0.4

0.0

0.0

DBH sice-class (cm)

DBH sice-class (cm)

(C) Riverine areas

1.2

1.6 Stem ha-1

Stem ha-1

1.6

0.8 0.4

(B) Natural forest

(D) Whole population

1.2 0.8 0.4 0.0

0.0

DBH sice-class (cm)

DBH size-class (cm)

7 8

Fig.2. Size (dbh)-class distributions (in 50 cm intervals) of baobab populations in three land-use

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types (A-C) and for the whole population (D) in two districts (Welkait and Kafta Humera) of

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western Tigray.

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2

12 8

Quotients

6

4

2

0

DBH size-class (cm) Natural forest

Grazing lands

Riverine areas

Whole population

13 14

Fig. 3. Quotients between baobab density in successive dbh size-classes (in 50 cm intervals) for

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three land-use types and the whole population in two districts (Welkait and Kafta Humera) of

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western Tigray.

17 18

3

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(A) SAp under current climate

(B) SAf under 2050-RCP4.5

(D) SAf under 2070-RCP4.5

(E) SAf under 2070-RCP8.5

(C) SAf under 2050-RCP8.5

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Fig.4. Distribution maps of baobab showing the evolution of the suitable habitats under climate change in Tigray region, Northern

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Ethiopia. Presented are the map of the present suitable area (SAp; A) and the maps of the future suitable area (SAf) under 2050-RCP

4

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4.5 (B), 2050-RCP 8.5 (C), 2070-RCP 4.5 (D) and 2070-RCP 8.5 (E). Note that no habitat would remain suitable by 2070 under the high

23

emission scenario (RCP 8.5).

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Highlights • • • •

This study assessed the current and future status of baobab in the face of human disturbances and climate change. Grazing, excessive harvesting and lack of conservation practices are detrimental to baobab populations. Climate change simulations predicted loss of baobab due to the “nowhere to go” predicament. Innovative strategies (e.g. assisted migration) should be urgently adopted to preserve the species.

Conflict of Interest None.