title: authors: , arne strid , panayotis dimopoulos ...mar 01, 2020 · vulnerable species...

Title: Management implications based on diversity patterns under climate change

scenarios in a continental island biodiversity hotspot

Authors: Konstantinos Kougioumoutzis1,2,3, Ioannis P. Kokkoris2, Maria Panitsa2,

Panayiotis Trigas3, Arne Strid4, Panayotis Dimopoulos2

Konstantinos Kougioumoutzis: [email protected] 1Department of Ecology and Systematics, Faculty of Biology, National and

Kapodistrian University of Athens, Panepistimiopolis, Greece, GR 15784 2Division of Plant Biology, Laboratory of Botany, Department of Biology, University

of Patras, Patras, Greece, GR 26504 3Laboratory of Systematic Botany, Department of Crop Science, Agricultural

University of Athens, Athens, Greece, GR 11855

Ioannis P. Kokkoris: [email protected] 2Division of Plant Biology, Laboratory of Botany, Department of Biology, University

of Patras, Patras, Greece, GR 26504

Maria Panitsa: [email protected] 2Division of Plant Biology, Laboratory of Botany, Department of Biology, University


Panayiotis Trigas: [email protected] 3Laboratory of Systematic Botany, Department of Crop Science, Agricultural

University of Athens, Athens, Greece, GR 11855

Arne Strid: [email protected] 4Bakkevej 6, DK-5853 Ørbæk

Panayotis Dimopoulos: [email protected] 2Division of Plant Biology, Laboratory of Botany, Department of Biology, University


Corresponding author: Konstantinos Kougioumoutzis; [email protected]

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted March 3, 2020. ; https://doi.org/10.1101/2020.03.01.971143doi: bioRxiv preprint

mailto:[email protected]







https://doi.org/10.1101/2020.03.01.971143

Abstract

In the Anthropocene era, climate change poses a great challenge in environmental

management and decision-making for species and habitat conservation. To support

decision-making, many studies exist regarding the expected vegetation changes and the

impacts of climate change on European plants, yet none has investigated how climate

change will affect the extinction risk of the entire endemic flora of an island biodiversity

hotspot, with intense human disturbance. Our aim is to assess, in an integrated manner,

the impact of climate change on the biodiversity and biogeographical patterns of Crete

and to provide a case-study upon which a cost-effective and climate-smart conservation

planning strategy might be set. We employed a variety of macroecological analyses and

estimated the current and future biodiversity, conservation and extinction hotspots in

Crete, as well as the factors that may have shaped these distribution patterns. We also

evaluated the effectiveness of climate refugia and the NATURA 2000 network (PAs)

on protecting the most vulnerable species and identified the taxa that should be of

conservation priority based on the Evolutionary Distinct and Globally Endangered

(EDGE) index, during any environmental management process. The highlands of

Cretan mountain massifs have served as both diversity cradles and museums, due to

their stable climate and high topographical heterogeneity. They are also identified as

biodiversity hotspots, as well as areas of high conservation and evolutionary value, due

their high EDGE scores. Due to the ‘escalator to extinction’ phenomenon and the

subsequent biotic homogenization, these areas are projected to become diversity ‘death-

zones’ in the near future and should thus be prioritized in terms of conservation efforts

and by decision makers. In-situ conservation focusing at micro-reserves and ex-situ

conservation practices should be considered as an insurance policy against such

biodiversity losses, which constitute cost-effective conservation measures. Scientists

and authorities should aim the conservation effort at areas with overlaps among PAs

and climate refugia, characterized by high diversity and EDGE scores. These areas may

constitute Anthropocene refugia. Thus, this climate-smart, cost-effective conservation-

prioritization planning will allow the preservation of evolutionary heritage, trait

diversity and future services for human well-being and acts as a pilot for similar regions

worldwide.

Keywords: CANAPE; conservation; EDGE; environmental management; extinction

risk; Species Distribution Modelling


https://doi.org/10.1101/2020.03.01.971143

Management implications based on diversity patterns under climate change

scenarios in a continental island biodiversity hotspot

1. Introduction

Earth has entered a new geological epoch, the Anthropocene (Zalasiewicz et al.,

2015), characterized by human-induced temperature, thus placing immense impacts

upon natural, atmospheric and hydrological processes. Consequently, global ecosystem

health is severely challenged, with shifts in biotic composition and ecological linkage

disruption being among the major threats of human activity worldwide. Species unable

to survive to such a novel adaptive matrix are on an inevitable path to extinction (Urban

et al., 2016), often causing a deadlock in nature management and protection practices.

Extinction threat might be more prominent on endemic species suffering from habitat

loss and fragmented populations (Warren et al., 2013), since they have narrower

geographical ranges and environmental niches. Climate change is projected to rapidly

change community structure in the near future, likely surpassing the land-use effects by

the 2070s (Newbold, 2018) and significantly altering terrestrial biodiversity (Warren et

al., 2013). On a global analysis of future climate change impacts, ca. 60% of plant

species examined were predicted to lose more than half of their current climatic range

in the coming decades, possibly leading to a substantial global biodiversity decrease

and ecosystem function degradation (Warren et al., 2013). Even though plants might be

more resistant to extinction compared to animals (Wing, 2004), climate change could

elevate the estimated extinction rates (Urban, 2015). Nearly 20-30% of species would

face high extinction risk if global temperature rises beyond 2-3 oC above pre-industrial

levels (Fischlin et al., 2007). In order to avoid a 1.5 oC rise in global temperature until

2052, unprecedented changes should be made at a global scale (IPCC, 2018). In this

context, it is important to follow a ‘climate-smart’ conservation planning strategy to

efficiently conserve as many species/communities as possible (Groves et al., 2012).

This strategy relies on the identification of climate refugia, i.e., relatively climatically

stable regions for many species of high conservation value which are characterised by

high endemic and genetic diversity (Sandel et al., 2011).

The Mediterranean Basin is a biogeographically complex area, with high levels of

endemism and diversification, due to its high topographic and climate heterogeneity

(Médail, 2017). It constitutes the second largest terrestrial biodiversity hotspot in the

world (Médail and Myers, 2004) with nearly 12500 plant species endemic to the region,

most of which have an extremely narrow geographical range (Médail, 2017). The

Mediterranean islands have high overall and endemic species richness (Médail, 2017).

Plant endemism varies between 9-18% on the largest islands and reaches up to 40% in

the high-altitude zone of their mountain ranges (Médail, 2017). Several of these islands

constitute biodiversity hotspots and climate refugia (Médail and Diadema, 2009). Crete

(S Greece) is rendered the hottest endemism hotspot of the Mediterranean Basin, owing

its high species number and endemism levels to long-term geographical isolation and

climate stability, as well as to its high environmental and topographical heterogeneity

(Médail, 2017).

The current global temperature will probably continue to rise (IPCC, 2018) and has

already forced species to migrate into higher altitudes, changed flowering

period/duration and in some cases, leading species to extinction (e.g., Thackeray et al.,

2016). These impacts will most likely accelerate (Urban, 2015) and are expected to be

more severe for species occurring on mountain ranges, due to the ‘escalator to

extinction’ phenomenon (Urban, 2018). The Mediterranean is a global hotspot of


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vulnerable species (Pacifici et al., 2015) and among the regions expected to experience

the largest changes in climate (e.g., Giorgi and Lionello, 2008), possibly leading to a

ca. 545 m upward vegetation shift and a 50-80 km northward shift in latitude, with these

impacts being more prominent on islands and mountain summits (Médail, 2017).

However, the Mediterranean Basin has experienced few plant species extinctions (22

taxa – 0.17% of the total; Domina et al., 2015). Most Mediterranean countries have

their own Red List of threatened plants. Nevertheless, regarding Crete ca. 30% (58 taxa)

of the Cretan single island endemics (SIEC) have been assessed and only five are

considered facing imminent extinction (Phitos et al., 2009). Several SIEC occur on the

island’s mountain ranges and comprise small and isolated populations, thus being prone

to climate change impacts.

New light can be shed upon the mechanisms underlying species’ possible extinction

and their subsequent conservation needs when integrating phylogenetic diversity

metrics into conservation-centred analyses (Morelli and Møller, 2018). Evolutionary

distinct species usually have unusual phenotypes, rare ecological roles and increased

functional importance (e.g., Cadotte et al., 2008). As such, they have great conservation

value, since their loss cannot be readily replaced. Thus, if evolutionary isolated species

are to become extinct, this would result into great evolutionary loss. Therefore, any

conservation plan needs to take into account the advantage of the conservation

prioritization of evolutionary distinct species, as it allows the preservation of

evolutionary heritage, trait diversity and future services for human well-being (Veron

et al., 2018).

Correlative species distribution models (SDMs) are widely used to identify which

species are most vulnerable, as well as to answer how, why, where and when these

species are vulnerable (Foden et al., 2019). There is ample evidence that the

expectations from correlative SDMs have matched recent population trends focusing

on birds, mammals and plants, in decreasing order and they provide appropriate results

when the aim is to estimate a species’ extinction risk (Pacifici et al., 2015). To our

knowledge, even though continental-wide studies exist regarding the expected

vegetation changes and the impacts of climate change on European plants (e.g., Berry

et al., 2017), none has investigated how climate change will affect the extinction risk

of the entire endemic flora of an island biodiversity hotspot, such as Crete. Our aim is

to assess, in an integrated manner, the impact of climate change on the biodiversity and

biogeographical patterns of the endemic plants of Crete and to provide a case-study

upon which a cost-effective and climate-smart conservation planning strategy might be

set.

2. Material and Methods

2.1 Study area

We focus on Crete (Fig. 1), the fifth largest island in the Mediterranean Basin and

the richest island hotspot of Europe in terms of endemic plant species (Médail, 2017).

Crete is the largest (8836 km2) and most topographically complex island in the

Aegean archipelago with more than 50 mountain peaks exceeding 2000m a.s.l. The

island is climatically compartmentalized in a W-E axis and is characterized by a sharp

altitudinal gradient (0-2456 m a.s.l.). There are three main mountain massifs present on

Crete (Lefka Ori, Idi and Dikti), with an entirely different climate than the warm and

dry lowland plains.


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Figure 1. The island of Crete, along with the altitudinal contours at a 100m

increment.

Geologically, Crete comprises mostly limestones, is part of the Hellenic Arc and was

formed in response to the subduction of the African plate beneath the Aegean (Higgins,

2009).

Its palaeogeographical history is rather complex and determined by two main

geological events that created important dispersal barriers (for a thorough review of the

Aegean’s palaeogeographical history see Sakellariou and Galanidou, 2016): i) the

isolation of Crete from Karpathos’ and Cyclades’ island groups 12 Mya and ii) the

isolation of Crete from Peloponnese after the Messinian salinity crisis (Manzi et al.,

2013).

2.2 Environmental data

An initial set of 43 predictors has been constructed (see Appendix A in Supporting

Information), all at a 30 arc-sec resolution, including:

a. sixteen climatic variables based on the 19 bioclimatic variables from

WorldClim for current and future climate conditions.

b. three Global Circulation Models (GCMs) that are rendered more suitable and

realistic for the study area’s future climate.


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c. two different IPCC scenarios from the Representative Concentration Pathways

(RCP) family: the mild RCP2.6 and the severe RCP8.5.

d. seven soil variables providing predicted values for the surface soil layer at

varying depths.

e. elevation data from the CGIAR-CSI data-portal (Jarvis et al., 2008) aggregated

and resampled to match the resolution of the other environmental variables.

From this set of predictors, only seven were not highly correlated (Spearman rank

correlation < 0.7 and VIF < 5 – see Appendix A).

2.3 Climate refugia We located sites in Crete that are climatically stable (sensu Owens and Guralnick, 2019)

for the past 4 My, using paleoclimatic data obtained from Brown et al. (2018) and

Gamisch (2019), the framework of Owens and Guralnick (2019) and the

`climateStability´ 0.1.1 package (Owens, 2019). Climate refugia as designated here

refer to macro-refugia according to Ashcroft (2010).

2.4 Species Occurrence Data

Crete hosts 2240 native plant species, 183 of which are single island endemics (SIEC)

(Dimopoulos et al., 2013, 2016; Strid, 2016). Based on the most extensive and detailed

database [Flora Hellenica Database, Strid (ongoing)], we compiled a dataset of all the

SIEC (8773 occurrences). The final dataset includes 172 SIEC, since we modelled only

those SIEC with at least three locations (following van Proosdij et al., 2016).

Data cleaning and organizing procedure follows Robertson et al. (2016 – see

Appendix A).

2.5 Phylogenetic tree

Since molecular data are not available for many of the SIEC, we used a “supertree”

approach to generate our phylogenetic tree (see Appendix A). We subsequently

calculated evolutionary distinctiveness (ED) and EDGE (Evolutionary Distinct and

Globally Endangered) scores for each taxon, as well as the standardized effect size

scores of phylogenetic diversity (PD) for each grid cell (see Appendix A).

2.6 Species distribution models

2.6.1 Model parameterization and evaluation

The realized climatic niche of each taxon was modelled by combining the available

occurrence data to current environmental predictors in an ensemble modelling scheme

(Araújo and New, 2007 - see Appendix A).

2.6.2 Model projections

Calibrated models with a TSS score > 0.8 (to avoid poorly calibrated ones) were used

to project the suitable area for each taxon under current and future conditions through

an ensemble forecast approach (Araújo and New, 2007 - see Appendix A).

2.6.3 Area range change

We assessed whether the 172 SIEC will experience range contraction or expansion

under future conditions (see Appendix A). Taxa were not assumed to have unlimited

dispersal capability, since this assumption could be overoptimistic.


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2.7 IUCN measures

We followed the Preliminary Automated Conservation Assessment (PACA) framework

(Stévart et al., 2019), calculated the IUCN measures EOO (Extent of Occurrence) and

AOO (Area of Occupancy) and assigned each SIEC to a preliminary IUCN threat

category according to the Criteria A and B under current and future conditions (see

Appendix A). After taxa were assigned to an IUCN and a PACA category, the total

number of taxa recorded and the proportion of taxa assessed under each category were

estimated, by combining Criteria A and/or B.

2.8 Niche breadth

Levins’ inverse concentration measure of niche breadth (Levins, 1968) was computed

for each taxon (see Appendix A). Niche breadth values range from 0 (specialists) to 1

(generalists) being thus comparable among taxa (Levins, 1968). Difference of niche

breadth between the different IUCN threat categories, was investigated via a Kruskal-

Wallis non-parametric test (KWA), as well as the difference of area range change

between SIEC with broad or narrow environmental niches (see Appendix A).

2.9 Hotspot analysis

For each climate scenario, we stacked the final binary maps of all 172 SIEC. From this

overlay, we calculated for each 30-sec grid-cell the number of taxa that would find

suitable environmental conditions there. We defined potential SIEC hotspots as the 20%

of cells that provide suitable environmental conditions to the highest number of taxa.

2.10 Generalized Dissimilarity Modelling analysis

We used Generalised Dissimilarity Modelling (GDM) to model pairwise plant

community compositional dissimilarity (Sorensen’s dissimilarity) within map grid cells

across Crete. We used the same environmental variables as in the SDM analyses, with

the significance of all variables assessed through a Monte Carlo permutation test (see

Appendix A).

2.11 Significant Differences

To identify sites where predictions of hotspot location differed significantly between

current and future environmental conditions, we applied the methodology proposed by

Januchowski et al. (2010 - see Appendix A).

2.12 Biodiversity indices We performed Categorical Analyses of Neo- and Paleo-Endemism (CANAPE), using

the methods and reasoning described in Mishler et al. (2014) and Thornhill et al. (2016

– see Appendix A).

We assessed the impact of climate change on the distribution of the endemism

hotspots by repeating the CANAPE analysis for every GCM/RCP combination

included in our study.

We tested the relationships among phylogenetic endemism (PE) and relative

phylogenetic endemism (RPE) with elevation, pH, mean diurnal range (MDR) and

climate stability by employing spatial autoregressive models with spatially

autocorrelated errors (SARerr - see Appendix A).

2.13 Future diversity and biogeographical patterns We derived species composition in each grid cell under current and future climatic

conditions by stacking the presences from the individual species models. We followed


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the framework outlined in Menéndez-Guerrero et al. (2019) regarding the assessment

of the spatial biodiversity and biogeographical patterns under future climate scenarios

(see Appendix A).

2.14 Protected areas network and climate refugia overlap

We overlapped current and future CANAPE and hotspot results with the areas

recognised as climate refugia, as well as with the protected areas (PA) network retrieved

from the World Database on Protected Areas (see Appendix A). Based on each species’

current and future EOO, we calculated the irreplaceability of each PA and climate

refugium in Crete for the current and future climate conditions. The irreplaceability

index represents biotic uniqueness and quantifies the degree of overlap between each

PA/climate refugium and the range of SIEC (Le Saout et al., 2013). We investigated

whether the degree of overlap differed between the current and future conditions via a

Kruskal-Wallis non-parametric test.

3. Results

3.1 Model performance

The models for most SIEC had sufficient predictive power (TSS ≥ 0.7 – mean TSS:

0.94; Table S1 in Appendix B). The resulting habitat suitability maps were converted

into binary maps and then compared to the binary maps obtained for each GCM, RCP

and time-period for each SIEC. Precipitation-related variables had the highest

contribution among the response variables for most SIEC (53.5%), while temperature-

related variables and soil pH were important for a smaller fraction of SIEC (42.4% and

4.1%, respectively). Full details and analyses of model performance are given in Table

S1.

3.2 Area range change

All SIEC species will experience range contraction, with varying magnitude across taxa,

GCMs and RCPs (11.7% - 100.0%; Table S1), but the median range contraction was

98.3% (see Table S2 for the median values for each GCM and RCP).

3.3 IUCN measures

For the first time, we provide a preliminary assessment of every SIEC occurring in Crete

according to the IUCN Criteria A and B (Fig. 2; Table S1). At present, 58.7% of SIEC

are facing imminent extinction (Fig. 2) and 87.8% are characterised as Likely

Threatened (LT) under the PACA categories (Table S1). This phenomenon could be

greatly deteriorated, since under any GCM and RCP included in our analyses, many of

these species are projected to become extinct (median EX%: 48.85% - Fig. 2, Table

S1). The distribution and the proportion of taxa assessed as Threatened either under the

IUCN or the PACA categories under both Criteria A and B are not uniform across Crete

(Fig. 3). These taxa are mostly concentrated at the Cretan mountain massifs, with the

highest proportion of them occurring in Lefka Ori (Fig. 3). These high-altitude areas

are predicted to become extinction hotspots under any GCM/RCP (Fig. S1).


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Figure 2. Proportion of the Cretan Single Island Endemics included in our analysis

under the IUCN threat categories for the current conditions according to both

Criterion A and B, as well as for every GCM and RCP considered in the present

study.


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Figure 3. (a) Proportion of species and (b) number of species preliminary assessed as

Threatened (either Critically Endangered, Endangered or Vulnerable) following

Criteria A and B.

3.4 Niche breadth

The median niche breadth was 0.41 for the SIEC and 25% of the SIEC are considered to

have a narrow niche breadth (Table S1). Niche breadth differed significantly between

all the IUCN categories (KWA: H = 56.6, d.f. = 3, p < 0.05) and species with narrow

niches have higher extinction probability for every GCM/RCP (Table S1).

3.5 EDGE

Based on both Criteria A and B, the SIEC have EDGE scores in the range 3.57-8.74,

with most species (75%) falling in an EDGE score class of 3.57-6.39. Eleven species

(Table S1) have an EDGE score exceeding 7.0, belonging to eleven different families

and all are considered as CR taxa (Table S1).

3.6 Hotspot analysis

The eastern, central and high-altitude parts of Crete are more likely to lose most of the

SIEC occurring there (Figs. S2-8). The SIEC hotspots are currently located above 1500

m a.s.l. in Crete (Fig. S2), but all of them are expected to shift downwards – even below

1000 m a.s.l. under any GCM/RCP (Fig. S3-8) and these shifts are significantly

different in absolute or relative terms (Figs. S9-20).


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3.7 GDM analysis

GDM helped disentangle the relative contribution of geographic and environmental

drivers of SIEC community composition across time and space. Our model explained

26.6% of deviance in SIEC composition (Figs. S21-22). Under any GCM/RCP, the low-

elevation area in W Crete is predicted to exhibit the greater floristic turnover than any

other region, followed by the high-altitude Cretan mountain ranges (Figs. S23-28). The

most important gradient for determining SIEC turnover was annual potential

evapotranspiration (PET), followed by geographical distance (Fig. S21). Soil pH was

the weakest predictor of SIEC turnover. The fitted functions describing the turnover rate

and magnitude along each gradient were nonlinear, with turnover rate varying with

position along gradients (Fig. S21). An acceleration zone in SIEC turnover was evident

for PET and short geographical distances, while a sharp compositional transition was

apparent along the precipitation of the wettest month gradient (Fig. S21).

3.8 Biodiversity indices

Higher and lower PD than expected was found in 21 and 86 sites, respectively (Fig.

S29). These sites did not differ significantly in terms of elevation or pH preferences

(both occur above 850 m a.s.l.), but the non-significant sites were located in lower

elevation areas with higher pH (KWA: H = 261.93, d.f. = 5, p < 0.05; Table S3).

The CANAPE analyses revealed 208 sites as containing more PE than expected (Fig.

4a), which are mainly concentrated within and at the periphery of the four Cretan

mountain massifs. Areas of mixed-endemism are the most common, followed by paleo-

and neo-endemism areas (156, 26 and 21, respectively). Centres of paleo- and mixed-

endemism occur in significantly higher altitude than not-significant sites (KWA: H =

15.73, d.f. = 4, p < 0.01; Table S4). Most paleo-endemism areas occur in western Crete

and on the mountain massifs of eastern Crete (Fig. 4a). Overall results are congruent,

regardless the grid resolution (Fig. S30).

The occurrence of the centres of endemism is projected to shift downwards (except

for the super-endemism centres) in the future under any GCM/RCP (Figs. S31-36;

Table S5) and these changes are significantly different for the mixed- and paleo-

endemism centres mainly (KWA: H = 344.17, d.f. = 9, p < 0.01).

The SARerr models indicate that elevation is the most important predictor of both PE

and RPE (GR2 = 16.1% and 5.9%, respectively; Table S6), followed by MDR and CS,

respectively.


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Figure 4. (a) Map of significant phylogenetic endemism (PE) identified by the

Categorical Analysis of Neo- And Paleo-Endemism (CANAPE) analysis for 172

Cretan Single Island Endemics. Red squares: centres of mixed-endemism (i.e. mix of

neo- and paleo-endemism). Blue squares: centres of neo-endemism; Purple squares:

centres of paleo-endemism. Orange squares: centres of super-endemism. (b) Map of

Crete showing predicted distribution of areas of biotic homogenization with respect to

Cretan SIE species diversity, according to the CCSM4 26 GCM/RCP. Red areas

indicate a decrease in beta-diversity (biotic homogenization). Blue areas indicate an

increase in beta-diversity (biotic heterogenization). Analyses were performed under

the assumption that a species cannot have unlimited dispersal capability, since this

assumption could be overoptimistic.

3.9 Future diversity and biogeographical patterns

3.9.1 Changes in ΔEDGE

Patterns of change regarding the EDGE index show that the high-altitude areas of Crete

are currently identified as hosting assemblages of great evolutionary distinctiveness

facing immediate extinction risk (Fig. S37). These areas are projected to become

extinction hotspots under any GCM/RCP (Figs. S38-39). In the future, the mid-altitude

areas are predicted to take up their place (Figs. S38-39).


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3.9.2 Changes in ΔBD

Areas with high SIEC βsim are currently concentrated in high altitudes, mainly across

the four Cretan mountain massifs (Fig. 5a). On the other hand, βsim is relatively low

over most of the lowlands and especially in the coastal area of northern Crete (Fig. 5a).

βsim is projected to generally increase in western Crete and in the mid-elevation areas

(1000-1500 m a.s.l. – a trend towards biotic heterogeneity) and this pattern is projected

to be greatest in a small coastal area of SW Crete (Fig. 4b). An entirely different pattern

emerged mainly in the Cretan highlands, where βsim is predicted to decrease (i.e., a trend

towards biotic homogenization – Figs. 4b-5).

Figure 5. Bivariate maps depicting relative changes in biodiversity measures for

Cretan SIE between the present and the CCSM4 26 GCM/RCP. Colours indicate the

relative amount of change. The red and blue end of the spectrum indicate reductions

and increases, respectively. Each transition in colour shading indicates a 10% quantile

shift in the value of the variables. (a) beta-Diversity, (b) species richness (SR), (c)

average level of ecological generalism (EG), (d) phylogenetic diversity (PD). Yellow

areas indicate sites with high current beta-diversity that will continue to have

proportionally high beta-diversity. Blue areas indicate sites that beta-diversity is

predicted to increase in the future. Green areas indicate sites where beta-diversity will

remain largely unchanged. We used a function generated by José Hidasi-Neto to

generate the map (http://rfunctions.blogspot.ca/2015/03/bivariate-maps-bivariatemap-

function.html).


http://rfunctions.blogspot.ca/2015/03/bivariate-maps-bivariatemap-function.html

http://rfunctions.blogspot.ca/2015/03/bivariate-maps-bivariatemap-function.html

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The most parsimonious model for all GCMs/RCPs was the full GAM including

ΔEG, ΔPD, ΔSR and elevation (Table S7, ΔAIC < 2), which explained 71.2-96.6% of

the total variance in βsim (Table S7). Both ΔPD and ΔSR were negatively correlated

with βsim change, while ΔEG was positively correlated with βsim change (Fig. 7; Figs.

S40-44). Here we focus on the results obtained for the CCSM4 2.6 GCM/RCP, as it

shows the highest similarity with the current conditions (see Changes in

biogeographical patterns below) and the patterns and trends do not deviate between the

different GCMs and RCPs. The increasing heterogeneity of western Crete and mid-

elevation areas (Figs. 4b-5) is mainly driven by range expansion and local extinction of

generalist and specialist species, respectively (Fig. 6; see also red areas in Fig. 5b-c).

Range expansions and local extinctions tend to drive species richness decreases,

resulting in phylogenetic clustering (Fig. 6d). Hence, future SIEC assemblages will tend

to be species-poor and comprised of taxa that are more closely related than current

assemblages (Fig. 6d; see also blue areas in Fig. 5d). The same processes are largely

responsible for the predicted biotic homogenization of the higher elevation areas in

Crete, even though these areas are predicted to host more specialist species.

Figure 6. Biplots showing the predicted relationships between βsim change and the

four environmental predictors included in the best generalized additive model (GAM)

a b

c d


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for the CCSM4 26 GCM/RCP. (a) Change in species richness (ΔSR). (b) Change in

phylogenetic diversity (ΔPD). (c) Change in elevation. (d) Change in the average

level of ecological generalism (ΔEG). Fitted lines show the univariate GAMs with

95% confidence interval (dark grey). Rugs on the x-axes show the predictor values

and how they are distributed. Labels on the y-axes indicate the smooth functions for

the term of interest (ΔSR, ΔPD, ΔEG and elevation) and the estimated degrees of

freedom (following the term). Values above and below the horizontal dashed line

indicate heterogenization and homogenization, respectively. Values left and right of

the vertical dashed line indicate in (a) species loss and gain, (b) PD decrease and

increase and (d) assemblages composed by specialists and generalists, respectively.

3.10 Climate refugia and protected areas network overlap

Six areas were identified as climate refugia and are located along a W-E axis in Crete

(Fig. S60). The largest and most species-rich climate refugium is located in the wider

area of Lefka Ori in W Crete, while the smallest and species-poor is near the Asterousia

mountain range in SC Crete (Fig. S60; Table S9). One and two of the climate refugia

are significantly phylogenetically overdispersed and clustered, respectively (Table S9).

In the future, all these areas are predicted to continue to serve as climate refugia, with

different trends: the species-poor and species-rich refugia show a trend towards

phylogenetic dispersion (PD increases) and clustering (PD decreases), respectively

(Table S9).

The overlap between the PA network in Crete and endemism centres detected by

CANAPE is rather high and ranges between 52-65% (Table S10; Fig. S61). The overlap

between the areas recognised as climate refugia and endemism centres detected by

CANAPE is lower than that reported for the PA network and ranges between 0-22%

(Table S10; Fig. S62). Under any GCM/RCP, the predicted overlap for both the PA

network and the climate refugia is lower than the one currently observed for most

GCMs/RCPs and CANAPE categories (Table S10). The overlap between the PA

network and the climate refugia ranges between 13.3-97.0% (Table S11). The percent

overlap based on the recognised climate refugia of at least the neo-endemism centres is

predicted to increase in the future, while the opposite trend is predicted for all the other

CANAPE types (Tables S11-12). The mean irreplaceability index differs significantly

between PAs and climate refugia (KWA: H = 56.6, d.f. = 1, p < 0.01; Fig. S63), as well

as between PAs and any GCM/RCP (KWA: H = 103.3, d.f. = 6, p < 0.01). Regarding

climate refugia, the mean irreplaceability index is significantly different only between

the current and the HadGEM2 RCP 8.5 climate projection (KWA: H = 14.6, d.f. = 6, p

< 0.05; Fig. S63). When taking out the effect of area, the mean irreplaceability index

does not differ significantly between PAs and climate refugia, except for the current

and the HadGEM2 RCP 8.5 climate conditions (KWA: H = 2.79, d.f. = 1, p = 0.09; Fig.

S64).

4. Discussion

Climate change is projected to alter biodiversity and biogeographical patterns all over

the globe (e.g., Enquist et al., 2019). The Mediterranean Basin is expected to face the

largest changes in climate worldwide (e.g., Giorgi and Lionello, 2008), with these

impacts being more prominent on islands and mountain summits (Médail, 2017). Here,

we used the hottest endemic Mediterranean hotspot, Crete, as a case-study for

management scenario building and decision making, based on an integrated assessment

of climate-change impacts on biodiversity, biogeographical and conservation patterns.

Our results highlight an augmented extinction risk for the majority of the SIEC, while


https://doi.org/10.1101/2020.03.01.971143

pinpointing areas of high conservation and evolutionary value; they should alert

conservation practices and management to take measures for biodiversity loss restrain

and halt of further deterioration.

4.1 Diversity hotspots

The high-altitude areas of Crete are identified as species richness hotspots, with Lefka

Ori, the western mountain massif, hosting the most SIEC (Fig. S1). These areas are

predicted to experience a sharp decline in species richness under any GCM/RCP and

will no longer constitute biodiversity hotspots, as a result of the ‘escalator to extinction’

phenomenon (e.g., Urban, 2018). Due to upward species’ range shift and the extinction

of high-altitude SIEC, mid-altitude areas will host an increasing number of species, thus

intensifying the observed mid-domain effect of the SIEC (Trigas et al., 2013). Most of

the upward migrating species will probably be neo-endemic species, since neo-

endemism centres occur at lower altitudes than paleo-endemism centres in Crete (Table

S4).

4.2 Conservation assessment We evaluated the potential conservation status of SIEC using the IUCN Criteria A and

B. Most of the SIEC are potentially threatened by extinction (Table S1; Fig. 2). These

potential extinctions were not randomly distributed among evolutionary groups, when

accounting for mean future area loss (Cmean = 0.12; p < 0.01), a phenomenon observed

in mammals as well (Davies and Yessoufou, 2013). The SIEC will be highly vulnerable

in the future, since up to 154 taxa are projected to become extinct (Table S1; Fig. 2).

The high-altitude areas that now serve as diversity and conservation hotspots (due to

high EDGE scores - Figs. S2 and S37) are projected to become extinction hotspots (due

to very low ΔEDGE scores - Figs. S1 and S38-39) and should thus be prioritized in

terms of conservation efforts. Eleven taxa (Table S1) should also be given high

conservation priority, since they have a very high EDGE score. However, none of them

is included in the IUCN Top-50 Mediterranean Island Plants initiative (de Montmollin

and Strahm, 2005). Identifying conservation priorities and implementing effective

actions are urgently needed and will become increasingly important, since human

activities and land use are exerting unprecedented pressure on natural environments,

leading to severe biodiversity changes (IPCC, 2018; Veron et al., 2016). In this context,

locating areas with high EDGE and low ΔEDGE scores could help prioritize areas of

high conservation and evolutionary importance and high extinction risk.

4.3 CANAPE A fundamental block for understanding biodiversity patterns and consequently for

conservation prioritization is to determine where species diversify (centres of neo-

endemism – cradles) and persist (centres of paleo-endemism – museums) over

evolutionary time. By this, we can decide whether or not PAs are indeed sheltering

different aspects of biodiversity (Tucker and Cadotte, 2013) and improve conservation

management in the unprecedented biodiversity decline setting which is currently

underway (Humphreys et al., 2019). By incorporating evolutionary history and

randomization processes, we were able to identify new areas of high evolutionary and

conservation value. In total, 107 sites had PD higher or lower than expected by chance

(Fig. S29), scattered across a W-E axis. Phylogenetically overdispersed sites of high

conservation importance (Swenson et al., 2007) occur at higher altitudes in Crete and

this may be related to a variety of reasons, such as the competitive exclusion of closely

related taxa with high niche overlap, the colonization of phylogenetically distinct


https://doi.org/10.1101/2020.03.01.971143

lineages (e.g., Campanula/Roucela species) or the high topographical heterogeneity of

the Cretan mountain massifs that harbour distinctly adapted plant lineages (e.g., Edh et

al., 2007).

In Crete, mixed-endemism centres occurring in higher altitude than the other types

of endemism centres, prevail (Fig. 4). It seems that in Crete, montane regions do not

act just as diversity cradles, but also as diversity museums – a pattern observed in other

parts of the world as well (e.g., Dagallier et al., 2019). Most paleo-endemism centres

occur in or near ravines/gorges in western Crete and on the mountain massifs of eastern

Crete (Fig. 4a); these areas may have functioned as biogeographical museums (Chown

and Gaston, 2000). Neo-endemism centres tend to occur in sub-montane altitude in

Crete, rather near mixed-endemism centres (Fig. 4a); their recent diversification may

have hindered their expansion. In the future, the occurrence of almost all types of

endemism centres are projected to shift downwards under any GCM/RCP (Figs. S31-

36; Table S5), suggesting that montane areas that have served as both diversity cradles

and museums for a very long time, will probably become diversity ‘death-zones’ in the

near future.

Climatic stability and high topographical/environmental heterogeneity may act in

conjunction to provide the conditions needed for the simultaneous persistence of paleo-

endemics and the diversification of neo-endemics (e.g., Azevedo et al., 2019). In this

context, altitude – a proxy of environmental heterogeneity (e.g., Cabral et al., 2014)

emerged as the most important predictor of PE and RPE in Crete, followed by MDR,

pH and CS. Niche-related processes thus seem to play a minor role in generating

endemism patterns in Crete, which is in line with previous studies at a coarser scale

(Lazarina et al., 2019). Distinct endemism patterns may be found in sites with

phylogenetically distinct assemblages, even if environmental similarity is high, due to

phylogenetically conserved niche breadth and dispersal ability (Wiens and Graham,

2005). This could be the case in Crete, since SIEc’s niche breadth has a significant,

though weak phylogenetic signal (Cmean = 0.11, p < 0.05). Our results lend weight to

the suggestion that the interplay between topographical heterogeneity and climate may

be linked with the configuration of centres of paleo- and neo-endemism on mountain

massifs (Rangel et al., 2018), a phenomenon also recorded in the Neotropics (Azevedo

et al., 2019). However, the low GR2 for both RE and RPE limits the scope of

conclusions we can draw from these results, and so they should be regarded as

informative rather than conclusive.

4.4 Future diversity and biogeographical patterns

We predict a trend towards biotic homogenization in the Cretan highlands, whereas an

opposite trend towards biotic heterogenization will probably occur in low- and mid-

elevation areas in Crete (Figs. 4b-5). Biotic homogenization is driven by the range

expansion of generalists and the consequent local extinction of specialists, resulting in

overall lower local species richness and phylogenetic diversity (Figs. 6 and S40-44).

The same processes drive biotic heterogenization in Crete, however these assemblages

will probably have higher species richness, due to the higher migration rate from the

lowlands. The prominent homogenization of the Cretan highlands suggests that

climate-driven extinctions of specialists may have greater impact on beta-diversity

patterns than the generalists’ range expansion. After all, the most important gradient for

determining SIEC turnover is PET, followed by geographical distance (Figs. S21-22)

and niche-based processes were found to exert some influence in the distribution of

species occurring in Crete (Lazarina et al., 2019). These regional variation shifts in beta

diversity could disrupt ecosystem functioning and provisioning of ecosystem services


https://doi.org/10.1101/2020.03.01.971143

(Cardinale et al., 2012). To properly assess and predict future projections of landscape

fragmentation, changes in demand, use and supply of ecosystem services should be

taken into account, using a climate-change, management scenario-based approach (e.g.

Kokkoris et al. 2019) and is particularly important for policy and decision-making

related to land and resource use (Geijzendorffer et al., 2017). Our results are in line

with the emergence of the ‘Homogenocene’ era, observed in other regions and

organisms as well (e.g., Menéndez-Guerrero et al., 2019).

Currently Crete, a speciation centre (Panitsa et al., 2018) and a distinct Aegean

phytogeographical region (Kougioumoutzis et al., 2017) is biogeographically

subdivided into 14 different regions (Figs. S45-46), a result probably of

paleogeographical history and local climate differentiation. This unique

biogeographical compartmentalization seems to be at peril, since under any GCM/RCP

it will drastically change (Figs. S47-58; Table S8), showing a trend towards biotic

homogenization in a W-E axis, along Crete’s altitudinal range. This phenomenon seems

to have a greater impact on the SIEC of the Cretan highlands, since most of the habitat

specialists occurring there, will probably be among the first to become extinct in the

next decades.

4.5 Conclusions – Conservation and management implications

Incorporating climate-change projections is critically important for management

strategies. Identifying climate refugia is an integral part of the ‘climate-smart’

conservation planning framework, since they constitute taxonomic and genetic

diversity centres (e.g., Sandel et al., 2011), with macro-refugia – areas that sustain

climatic suitability along broad spatiotemporal gradients (Ashcroft, 2010) – being

generally more easily captured by GCMs. In the Mediterranean basin, where land-use

change and the occurrence of fire events are expected to intensify (e.g., Turco et al.,

2018), having a resilient PA system should be a priority. However, the current

conservation strategy and management agenda for future conservation projects in Crete

is based on the obligations of Dir. 92/43/EEC and especially for habitats and species of

Annex I and II, respectively. These obligations include reporting for the conservation

status and future trends for only a few species identified as under extinction risk by the

present study. It is also evident that hesitancy toward anything but conventional

conservation actions persists (Hagerman and Satterfield, 2013). Considering the worst-

case scenario (i.e. extinction of the highest number of species by climate-change

impact), in-situ conservation focusing at micro-reserves and ex-situ conservation

practices should be considered, as an insurance policy against such losses of plant

biodiversity (Hawkes et al., 2012), which constitute cost-effective conservation

measures (Kadis et al., 2013). By this, our study urges scientists and authorities to aim

the conservation effort at areas with overlaps among PAs and climate refugia,

characterized simultaneously by high diversity and EDGE scores (including the 11 taxa

with the highest EDGE score), as well as serving as mixed-endemism centres. These

areas are qualified as future climate refugia and may actually constitute Anthropocene

refugia (Monsarrat et al., 2019). By doing so, this ‘climate-smart’, cost-effective

conservation prioritization planning will allow the preservation of evolutionary

heritage, trait diversity and future services for human well-being (Veron et al., 2019).

Landscape connectivity as a prerequisite for successful migration is an important

management issue. The current scale of habitat fragmentation is likely to hamper the

potential success of migration that relies heavily upon the connectedness of populations

across suitable environments (Hoffmann and Sgro 2011). Thus, measures to mitigate

landscape fragmentation, as an active conservation strategy, should be included in


https://doi.org/10.1101/2020.03.01.971143

ongoing and future Action Plans for habitats and species, based on climate-change

projections and management scenarios. To properly assess and predict future

projections of landscape fragmentation, changes in demand, use and supply of

ecosystem services should be taken into account, using a climate-change, management

scenario-based approach (e.g. Kokkoris et al. 2019). This procedure is important for

policy and decision-making related to land and resource use (Geijzendorffer et al.,

2017).

Future climatic projections can trigger the interest of the scientific community and

the conservation society. However, to reach decision and policy making, raw scientific

information is not always the appropriate mean of communication. All information

produced by the present study should be transformed into tangible material using the

ecosystem services approach and by this communicate the loss (via plant species

extinction) of various ecosystem services and the related impacts into the socio-

economic environment. In EU the MAES concept encapsulates this idea and acts as a

management- and dissemination- tool among scientists and policy makers.

Acknowledgments: This project has received funding from the Hellenic Foundation

for Research and Innovation (HFRI) and the General Secretariat for Research and

Technology (GSRT), under grant agreement No [2418].

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Appendix A. Extended Materials and Methods

Appendix B. Supplementary Tables and Figures


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title: authors: , arne strid , panayotis dimopoulos ...mar 01, 2020 · vulnerable species...

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