Extending Janzen’s hypothesis to temperate regions: A test using subterranean ecosystems Stefano Mammola, Elena Piano, Florian Malard, Philippe Vernon, Marco Isaia

To cite this version:

Stefano Mammola, Elena Piano, Florian Malard, Philippe Vernon, Marco Isaia. Extending Janzen’s hypothesis to temperate regions: A test using subterranean ecosystems. Functional Ecology, Wiley, 2019, 33 (9), pp.1638-1650. ￿10.1111/1365-2435.13382￿. ￿hal-02181396￿

HAL Id: hal-02181396 https://hal-univ-rennes1.archives-ouvertes.fr/hal-02181396 Submitted on 17 Sep 2019

HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Accepted Article Article type : Research Article Article :Research type Article FLORIAN MALARD (Orcid :0000-0001-8037-4464)ID d’Ecologie des Hydrosystèmes Naturels et Anthropisés, Villeurbanne, France .LIBRe – Laboratory for BiodiversityIntegrative Finnish Research, Museum of Na 2. * Paimpont, France 4. ecosystems subterranean using test a regions: totemperate hypothesis Janzen’s Extending Rezende Enrico Editor: Dr Ecology Physiological Section: 1. ** corresponding author: [email protected], tel. 0116704544; OrcidID:0000-0001-5434-2127 0116704544; tel. [email protected], author: corresponding ** 3. History, University ofHelsinki, Helsinki, Finland Stefano Mammola co Univ Univ Depar rresponding author: [email protected],tel.0116704544; OrcidID:0000-0002-4471-9055 Rennes, Université Rennes 1, CNRS UMR 6553, ECOBIO, Station Biologique de StationBiologiqueECOBIO, RennesRennes,Université UMR6553, 1,CNRS Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5023, ENTPE, Laboratoire LyonENTPE, Lyon, UMR5023, Bernard ClaudeCNRS 1, Université tment of Life Sciencesand Systems Life of tment Turin, Biology, University of Turin, Italy. 1,2,* DR STEFANO MAMMOLA (Orcid : 0000-0002-4471-9055) ID , Elena Piano Functional Ecology Ecology Functional

1 , FlorianMalard 3 ,

Philippe Vernon 4 , MarcoIsaia 1,** tural

Accepted Article Running title: title: Running increase. At last,we Atincrease. demonstratedathatextent species’ variation and range elevational the whereastemperature, shallow specieswithstood larger atwo-fold subterranean temperature Deep subterranean species reached their critical temperature at 1 to 4 °C above their habitat We thermalthat found tolerancedecreased with increasingsubterranean specialization. 4. subterraneanspecialization to eleven life of species. measured thermal andtolerance used morphological toquantify traits degree the of Then,in weshallowcaves habitats. considerably across were than cave lower indeep nichebreadths. Initially, we thermal seasonalityin andtheoverlaptemperature showed that species would both narrower exhibit andsmaller elevational rangeextents realized thermal seasonality would narrow select for Also,thermal tolerance. specialized subterranean we FollowingJansen’s assumptions, predicted habitatscave thatlow with thermal 3. andshort-variability long-term climatictolerance thermalelevational and on rangeextent. region rather than distinct from biogeographic regions, avoids confounding the effects of seasonalitythermal increasing cave depth.with Using species same thefrom temperate (Araneae: : differences in thermal tolerance and elevational rangeamong congenericspiders alpine provide We theextension first Janzen’sof hypothesis to temperateby habitats, testing for 2. thermal seasonality outside the tropics. communities, leavingunresolved the questionof their generalizationto habitats with low These predictions have beentested byso far and confronting tropical temperate mountain limiting dispersalamong different sitesof elevations showing overlapinlittle temperature. reduced thermal seasonalitywouldselect for species with narrow thermal tolerance, thereby (1967; Janzen’s hypothesis 1. ABSTRACT tolerance, Climate change, Rapoport’s Subterraneanrule, biology, ItalianWestern Alps Keywords: . Climate VariabilityClimate Janzen’sHypothesis, hypothesis, Range size, Thermal

ExtendingJanzen’s caves to hypothesis Troglohyphantes American Naturalist

) occurring alonga gradientsteep decreasing of ) predicts that withtropical habitats

Accepted Article Janzen concluded that mountain passes were figuratively “higher” in the tropics. passes, because theywould be likely tomore encounter Consequently, unsuitable climates. species would have greater difficulty than temperate species to disperse across mountain tolerance over time. By linking together these two assumptions, he predicted that tropical temperature, like in tropical wouldmountains, select speciesfor with narrow thermal gradient across that barrier. Then, he assumed that a greater seasonal stability of thethat degree which to a barriertopographic limited dispersaldepends on the temperature developed explainbiodiversity in to tropicalpatterns Janzen beganmountains. by assuming and, through its this, dispersal and elevational range. This hypothesis was originally species a tolerance variability understandcontribution influences thermal howto climatic Bates, & Dulvy, 2011).Janzen’s passes mountain hypothesis (Janzen, 1967) iscore a Mittelbach2008; al., et Muñoz2007; & Bodensteiner, 2019; Shah et al., 2017;Sunday, research topic in biogeography, andmacroecology change climate biology et (Deutsch al., Understanding indetermining climate role of the species geographicalpivotaldistributionis a INTRODUCTION change.climate the ecologicalspecialization-dispersal tradeoff may constrain the species of response to Extending Janzen’s thoughts ato broader range ecosystems of is key tounderstanding how disentangle the effect long-termof variabilityclimate onsubterraneanbiodiversity patterns. first generalization of Janzen’s framework caves to and provides a conceptual framework to Our work, integrative being groundedin organismaland habitat measures, represents the 5. deep subterranean life. temperatureof across encounteredits rangedecreased with increasingspecialization to o aheig mcaitc nesadn o dvriy atrs cos pc and time . space across patterns diversity of understanding mechanistic a achieving for species, therebyphysiology,conceptual foundationathe dispersalcapacityprovidingof and Janzen’s hypothesis is compelling becausebrings it together the habitat features, the

Accepted Article rangesize higher (Morueta-Holme latitudes at 2013; 1996; Rohde, etal., whenbeen debated it comes to explainingthe inmechanisms causing an species increase and dispersal (Jansson & Dynesius 2002; Stevens 1989). This confounding hasfactor long risk of confounding the influence of short- and long-term climatevariability onspecialization communities bearstheand tropical as temperate such communities geographically-distant communitiesanimal comparing ataverycontinental, scale.Indeed, than rather a local, test relationsamongfor specialization, habitatthermal tolerance dispersal capacity and in few tenths of degrees; Badino, 2010; Cigna, 2002). This provides a unique opportunity to López, Oromí, Polak, & Culver, 2011) to the innersectors(annual variations inthe order of outside (annualtemperature thecomparable variations outer to the seasonalityfrom stability 2010).climatic (Badino, caves, temperate In thereare steep gradientsin thermal and inaccessible voids to humans—are among the terrestrial habitats with greatest the hypothesis.terrestrial Deep habitats—deep subterranean caves and the mazeof fissures with reduced climatic seasonality. testing whether itcan be usedtoexplain biodiversity in patterns non-tropical ecosystems result, it should be theoreticallygeneralizepossible to Janzen’s hypothesis broadly,more by passes hypothesishold might true riverine for communities. Building uponthis milestone temperate mountain rivers,Polato etal. (2018) recently demonstratedthat the mountain (Polato et al., 2018; Smith, By2018). studying invertebrate assemblages in tropicalversus empirical assumptions—was proposed only decades after itsfive from original formulation Janzen’s ofcomprehensive simultaneously its test aresearch tackling allof hypothesis—i.e. systemsemphasizes the difficulty of rigorously testing this framework. Indeed, a communitiesgeographically-distant animal occurring intropical andtemperate mountain 2018). Yet,the challenge of comparing thermal tolerance, range size anddispersal ability of (Ghalambor, Huey, Martin, Tewksbury, & Wang, 2006; Sheldon,Huey, Kaspari, & Sanders, . i.e. theRapoport’sas rule describedby (1989).Stevens Caves represent unique arenas experimental for theassumptionstesting Janzen’s of

Veter etVeter al.,2013), ; Pipan, Accepted Article seasonality. biogeographicsharinga common history inhabitats contrasted of butoccurring thermal 2018), hence evaluating differences in thermal tolerance and dispersal among species 2017;Mammola,&Alps Isaia,biogeographic thearea of Goodacre, al., (Isaia et Western Mammola et al., 2018a). eleventested We congeneric species distributed in the restricted thermally variable to deep and thermally stable subterranean habitats (Isaia et 2017;al., representing replicatesmultiple of the ecological transition alonga gradient shallowfrom and Troglohyphantes Troglohyphantes specialization and theto extent of the elevation range. used spidersWe of the date,organisms to and relating thermal tolerance tothedegree subterranean of by thermalthe measuring tolerance of widest the sample of congeneric subterranean . thermal specialization to its dispersal potential across elevation gradients.dispersalspecializationthermal elevation across its potential to mountains(Polato al.,2018), et byand species’ asubterranean morphological linking Janzen’s hypothesis to habitats with reduced seasonalitythermal than tropical to other thermal niche breadths. Corroborating the two predictions would provide an extension of subterranean specieswould exhibit bothnarrower elevationalranges and smaller realized (hereafter ‘realized nichebreadth’). thermal predicted We that more specialized range temperature across that variation in the wellas to extent as elevationalrange the deeper, seasonally cavehabitats.Then,westable thermal related toleranceto the ranges of thermal tolerance, because the more specialized subterranean species colonize predicted that increasing subterranean levels of specializationwould in narrowerresult (Mammola et 2018a), al., and related this specialization to their thermal tolerance. We specialization eachspecies of usingaquantitative approachbased on traitsmorphological experiments ina climatic Subsequently, chamber. we estimated thesubterranean We initiallyWe explored theupper thermal toleranceofthese Here, we provide the first explicit test of Janzen’s hypothesis in subterranean habitats,

spidersshowdifferentof subterranean levels habitat specialization, thus (Araneae: Linyphiidae)organisms as experimental (Fig. 1).model

species via thermal test Accepted Article background represent mean annual temperature, as derived from Bioclim 2 (Fick & Hijmans, 2017). & Hijmans, 2 (Fick Bioclim from derived as temperature, annual mean represent background Polygon (MCP) encompassing all localitiesof each species is drawn. Shadesof gray in the Convex Minimum the presentation, visual For reported. are distribution the of centroid and localities 1.DistributionFigure of . Troglohyphantes in the Western Italian Alps. For each species, species, each For Alps. Italian the in Western spiders Accepted Article Sampling protocol period in which temperatures were recorded. etal.,2018b).The reported temperaturepatterns remain roughlythesame for the whole dataloggers were properlyinall (seedetailssimultaneouslyandworking caves inMammola showedoverlap during only was thermal timeframea 2.5 because this which months, for all deep subterranean temperature acrossregimes caves opening at different elevations. We range of thermal regimes and determined daily overlap values between thesuperficial and (full details in Mammola et al., 2018b). Following Scheffers et al. (2017), we plotted the caves acrossour study covering region, anelevational range 400from to 2,000over a.s.l. m ininwere Data takenlength). 100 when m than multiple point deepest caves wereless the cave entrance (1–10from m the and surface) deeperinside cave the (>100 or atm the whole period, sampling using Hygrochron between2014. 2012and Temperature measurements weretakenevery through 3h the overlap in temperature at shallow anddeep cave sectors. usedWe thermal collecteddata sectors, we used best available records thermal in the ItalianWestern Alps compare to the whetherTo thermal test gradients inhabiting areless overlapping spiders deep cave for Thermaloverlapdeep for shallow and cave habitats & METHODS MATERIALS in . where period the to corresponds We We livecollected eleven of specimens species of to late winter, which inour experience &(Isaia Mammola, Troglohyphantes of habitats accessdifficult ( Alps (Table 1). theWith exception of a very rare inhabitingspecies interstitial subterranean (caves, subterraneanmines, bunkers;military hereinafter ‘caves’) across the ItalianWestern species occurring in region. Samplingsthis were conducted early from fall

T. giachinoi spiders are generally mostly available mostly generally are spiders Troglohyphantes TM dataloggers (accuracy: dataloggers ±0.5°C) placed at both Isaia & Mammola),Isaia we sampled all known

Troglohyphantes

fromsubterranean sites personal observations ) Accepted Article appendage weremovements visible (Le Lann al., et 2011).observed thatWe after the totalparalysis, isthat temperature the at whichindividual the was and immobileno temperature ofrate 1°C/day. evaluated We the individual critical temperature (CT) as the chamber,climatic starting the from annualmean cave temperature, with an increasingslow cave temperature (Badino, 2010; Sánchez-Fernández etal., 2018). we the calculated annualmean temperature and used valuethis as a direct standard environmental lapse rate (0.57°C/100m;Rubel, Brugger, Haslinger,& Auer, 2017), the nearest thermo-hygro-pluviometricweather After correcting station. withthe data the asterisk in Table we 1), downloaded annualtemperature series period same the from from ForMaissa). temperature field-collected two(indicated records caves lacking with an 2018b), or unpublished temperature records taken via samethe methodology (Grotte della continuous temperature takenmeasurements between 2012and2013 (Mammola al., et throughout theexperiments. starvation of subterranean spiders (Mammola & Isaia, 2017), specimens were not fed offered ad were sampling libitum the climatic chamber. During smallacclimation, collected insects on the wallscave during acclimation and during the experiments, by two placing Petri dishes filled with water inside one day at the temperature of the cave. Air humidity was kept at 100% during the Memmert climatic chamber,eachin specimen individual dish,andacclimated Petri for them we conductedtheexperiments. laboratory,In the inwe a 30placed specimens IPP Peltier bagand transported itthesameday thelaboratory to the University at of Turin(Italy),where hand Tubes® inindividual andplacedthem stored of5ml. Eppendorf allvials inacool- We specimens11 (from to 36) analysis for the (Table 1). each In we cave, collected spiders by species itwasnecessarysample obtain caves number to sufficient ato multiple of caves. Due to the reduced detectability and generally low individuals,density of a for few . We conductedWe experimentstodetermine the uppercriticaltemperaturewith the For eachsampled we cave, internal derived the annual temperaturemean from

to each specimen to tested. Owing the to high resistance to

proxy of the the of Accepted Article stress). No mortality was recorded in controls during the experiments. effectconfounding of causes of thanmortality temperature other alteration (e.g., starvation, approximately10%the of specimens ascontrols instable climatic conditions, excludeto the Elevationalrange and extent realized thermal niche breadth LASLeicaEZ software 3.0 (Leica Microsystems, Switzerland). installed on a Leica M80 stereoscopic microscope (up to 60x magnification), and with the measures in (mm) from millimeters digital pictures with made a Leica EC3 digital camera estimation,thereby problems avoiding related tosexualsize dimorphism. acquired We measurements andobtaincomparable estimations, we considered only adultfemalesin the Troglohyphantes these to representmeasures specialization to deep subterranean inhabitats reductionprofile (cephalothoraxdivided height by cephalothoraxwidth).The reliability of body size), eye regression (sum of eye diameter divided by length of the ocular area) and measured three traits,morphological namely legelongation (sum legof I articles dividedby estimating thedegreeweFor eachspecies specializationsubterranean tested, of of Morphological measures quantify thedegree of subterranean specialization. species identity underastereomicroscope, lethal temperature (Wu & Wright 2015). We expressed the CT the expressed 2015). We & Wright (Wutemperature lethal achamber, typicalpattern when thetemperature whichat heatstupor occursisclose to the process,heating spidersthe never recovered after being removed thefrom climatic . until allthe specimens had stopped (CTmoving temperature at which 50% individuals experienced total paralysis. The experiment continued After theAfter experimentssexed each we specimen (male, or female confirmedjuvenile), species isprovided in Mammola etal.(2018a). To standardize and acquired to and measures morphological

100 ).

For each species tested, we kept

50

as the measure of Accepted Article (response variable). whereby each species ofsurvival asa of the percentagewe rate function modeled We constructed standard survivalcurves with binomial Generalized Linear Models (GLM), into account the sex, via sex, into account theformula: the we also used separate ANOVA test to for differences in species caves in ( Forfour the collected multiple with ANOVA, separate notation): viathe (R formula specimen). testeddifferences for We in difference between the CT and the temperaturemean of the natural of habitat each species criticaltemperatures ( internal annual mean temperaturecaves the of as withthe calculate baselineto the the delta R software(R Core analyses 2017). were conducted usingAll used Team, statistical the We Statistical analyses across itslocalities. species each difference asthe breadth and between temperaturesbymaximum minimum experienced betweendifference and maximum elevationsand nicheminimum realized the thermal the occurrence recordsspecies.eachof calculatedelevational theWe as the range annual temperature rasters at 30 arcsec resolution (1970–2000; Fick & 2017) Hijmans, to Arnedo, 2015; Mammola etal., 2018a, 2018b) (Fig. 1). overlaid elevation We and mean species, We adatabaseassembled eachof occurrence of alpine localities Western .

using recent literature geo-referencedrecords al., (Isaiaet2017; Mammola, Isaia, & ∆

∆

T, aT, of thermal measure tolerance expressed as the T(species) ~ cave +sex ~ cave T(species)

∆

T ~ sex * species sex * T ~ ∆ Ts betweenTs or juveniles) sexes (males, females T. konradi

∆ Ts across populations and taking

, T. lucifer T. , T. lucifuga, Troglohyphantes T. vignai ∆ ), T Accepted Article in . seasons across substantially vary can temperature air because species specialized species belonging to different classes of differenttospecies belonging of classes subterranean ANOVA. specialization with tested differencesfor in extent rangeelevation and realized niche thermal between breadth this analysis, ∆ via a Pagel correlation structure, with the R package ‘nlme’ (Pinheiro et al., 2017). Prior to latest the introduced hence accountingmodels, phylogenetic for non-independence amongspecies. We thermal niche breadthof each specieswith Phylogenetic Generalized LeastSquares (PGLS) testedWe relationshipsbetweenthe the inRpackage(Bates, Mächler, ‘lme4’models &2015), Bolker, the with Walker, formula: designedto account dependence, for usingdata speciesidentity random as factor. fitted We binomial Generalized LinearMixed Model (GLMM) The partmixed of the model was as tested a of function subterranean specialization of each species, we modeled survivalthe rate of all individuals afor kequal of numberto the maximum andperforming speciestested 500bootstraps. determining the optimal number clusters of k (Tibshirani, & Hastie,Walther, 2001), allowing 2014), with the R package ‘fastcluster’ (Müllner, 2013). We relied on the Gap statistic for Euclidean and the distances hierarchicalWard2’s clustering (Murtaghmethod &Legendre, measured on adult females. Using standardized values of traits, we estimated clusters via homogeneous classes subterranean of specialization, using the morphological traits Survival rate ~ The useof annual temperaturemean to calculate To the shape verify the if survivalof curvesvaried inresponse the to degree of We We performed a cluster analysis grouping for T wasT log-transformed account to one outlyingfor observation ( ∆ T * Class ofT subterraneanClass * specialization factor+ random (Species) Troglohyphantes ∆ T in interaction with subterranean specialization,interaction class of the T a with in ∆ phylogeny (Mammola et al., 2018a) into the PGLSs T andboth elevational the andrange the realized Troglohyphantes ∆ T may bias results for low forT results bias may species into T. lucifer T.

). We We ).

Brignoli konradi T. Isaia & Isaia Pantini T. bornensis Gozo pedemontanus T. & Isaia Pantini T. lanai Species Species

Accepted Article

which 50% or 100% individuals experienced total paralysis; paralysis; total experienced individuals 100% or 50% which the specimens were sampled. which at temperature cave annual mean the to relative paralysis total experienced individuals 100% analysis; n= number of specimens tested (controls are included in the count); CT count); in the included are (controls tested specimens of number n= analysis; temperature of the sampling locality; Spec.= subterranean specialization according to thecluster sites) and approximate geographiccoordinates in WGS84 decimal degrees; CaveT=mean annual artificial denotes ("Art." brackets square in code cadastre locality, sampling (Coordinates)= code] 1. Table (Appendix S1in Supporting Information). specimens were collected in the caves, rather than the annualmean cave temperaturte would changeif we ∆ computed sectors cavesuperficial (Fig. 2). Therefore, we explored whether resultsthe ofanalysis our .

Overview of the species of of species the of Overview

(44.26 N, (44.26 N, E) 7.40 1219] 1215, 1214, [Pi Maissa della Grotte (45.27 N, (45.27 N, E) 7.14 1501–1504] [Pi GrottePugnetto di (44.24 N, (44.24 N, E) 7.84 108] [Pi Bossea di Grotta (45.71 N, (45.71 N, E) 8.31 2509] [Pi Arenarie delle Grotta (44.25 N, (44.25 N, E) 7.52 [Art.] di Vernante Sotterranei [cadastre code] code] [cadastre (Coordinates) Cave

T using the cave temperature values at the time when the Troglohyphantes CaveT Spec. n Spec. CaveT . nem 11 Interm. 8.7 8.9 High8.9 15 . Hg 14 High 9.2 8.5 9* 9* ih 32 High tested and summary statistics summary and tested ∆ T 50/100 (10.5–13.5) (10.5–13.5) 11.88±0.96 10.02±0.75 10.69±1.76 (9.2–11.2) (9.2–11.2) 9.44±0.73 mean±sd (range) (9–11) (8–15) (8–15) = temperature at which50% or CT CT CT 13 14 11 12 11 9 11 50/100 50

CT . 15 15 = temperature at at temperature = Cave [cadastre [cadastre Cave 100 0.94±0.73 3.38±0.96 1.82±0.75 1.92±1.23 mean±sd (0.5–2.5) (0.5–2.5) (range) (2–5) (1–3) (1–5) ∆ T ∆ T 1 3 4 5 2 3 1 50 ∆ T 5 100 Brignoli iulianae T. Brignoli vignai T. Brignoli nigraerosae T. Isaia T. lucifer (Simon) T. lucifuga Di Caporiacco pluto T.

Accepted al. et Article * .

Internal mean annual temperature estimated from the closest weather station

(44.27 N, (44.27 N, E) 8.08 309] [Li Baraccone del Grotta (45.02 N, (45.02 N, E) 7.12 1591] [Pi Tana del Diavolo (45.51 N, E) 7.79 1609] [Pi Bucadel Ghiaccio della Cavallaria (45.19 N, (45.19 N, E) 7.04 1580] [Pi Grotta delGhiaccio Bosconero di (44.90 N, (44.90 N, E) 7.19 Tornini mine [Art.] (45.51 N, (45.51 N, E) 7.79 1609] [Pi Bucadel Ghiaccio della Cavallaria (45.27 N, E) 7.14 1501–1504] [Pi GrottePugnetto di (45.02 N, E) 7.12 1591] [Pi Tana del Diavolo (44.29 N, (44.29 N, E) 7.79 121–122] [Pi Grotta delCaudano (45.51 N, E) 7.79 Bornadel Servais B [Art.] 6.7 6.7 0 nem 24 Interm. 10* . nem 22 Interm. 6.6 7.6 7.6 8.7 8.7 6.7 6.7 6.3 6.6 9 Low Low 9 18 4 nem 36 Interm. Low 31 Low Low 21 Low (10.5–17.5) (10.5–17.5) 11.76±0.89 13.11±2.35 21.18±7.04 12.43±4.11 12.50±2.29 (9.5–17.5) (9.5–17.5) 7.89±2.40 (11–14) (11–14) (11–30) (11–30) (5–13) (5–13) (6–19) (6–19) 12 14 13 18 19 30 11 19 13 18 8 13 16.70±8.89 2.42±1.54 1.76±0.89 5.18±2.42 7.68±3.18 3.50±2.29 (0.5–5.5) (0.5–5.5) (0.5–8.5) (0.5–8.5) (2–10) (2–10) (2–27) (2–27) (2–15) (2–15) (1–4) 19 27 27 19 2 6 2 4 4 10 7 15 4 9 Accepted Article tolerant species, reaching CT its its CT its no . factor. the of levels the in imbalance significant a to due ANOVA robust vignai majority of speciesreachingmajoritytheir CT of Narrow tolerancethermal was observed for most test. All remaining species were successfully in tested climaticthe chamber (Table 1). mortality individuals of occurred during the acclimation phase before the beginning of the Alps.Western TheItalian species ofA 247 total belongingspecimens species 11 to in werecollectedcaves 13across the tolerance thermal on test Experimental at differentelevations 2A). (Fig. between wassubstantialsectors andthere overlap a regimes habitats among thermal these elevations (Fig. 2B). Bythermalcontrast, wereregimes more variable inthesuperficial cave We observed no overlap inthermal regimes between deep habitats caves differentat no detectable daily and variations seasonalvariations in the order of one tenth of degree. Variation inambient temperature was extremely low indeep of sectors caves, with virtually Thermaloverlapdeep for shallow and cave habitats RESULTS collected inone the outof three caves delGhiaccio(GrottadiBosconero), preventing to fit a vignai populations (ANOVA: T. konradi specimens (ANOVA: and T. konradi T. , : For allspecies tested, 100 F T. konradi at at 2,24 = 1.65, p=0.21) were detected. In casethe of ∆ T 3 and from ranging 5°C, respectively. proved to be the least tolerant species, reaching their CT species, reaching leasttolerantproved their bethe to ) for which we tested two populations, no differences in F 18,152 = 0.31, p= 0.99). In the case of three species ( species three In the of p= 0.99).0.31, case = ∆ T did not significantly varied depending on the sex of the

50 : at∆ T. bolognai F 2,21 50 T 19 °C and itsCT = 0.81, p= 0.46; at∆

Tranging 1to 4°C (Table from 1). was excluded from the analysis, because high Troglohyphantes

Conversely, T. lucifuga: 100 at 27 °C. °C. 27 at T. lucifer F T. lucifer lucifer T. , individuals weremost

2,10 (Fig. 3A), with the 50 = 3.45, p= 0.09;= 3.45, at ∆ Also, we found we Also, T. lucifuga T of 1 °C, and 1°C, T of was the most ∆ T. bornensis T between , T. T. T. T.

Accepted Article class curves were significantly different from the low one (GLMM: High: estimated subterranean habitat specialization (Fig. 3B; see alsoFig. S1). The high and intermediate indicating the that shape of survivalthe curve according varied degreeto the of GLMM between interaction a significant revealed Relationship between thermal tolerance, specialization and elevational range withhomogeneous, six4. of the individualshighlightedinFig. misplaced exception abdominal pigmentation. absence of andappendage elongation included individualsof most thespecies degreewith a moderate of specialization inmorphological (“Intermediate” andclass Fig.4), (“Low” classof subterraneanspecialization inFig. 4). The second cluster comprised species entrance cave or inshallow in the andoften habitatsvicinity the found of subterranean specialization tosubterranean(eyes life normallydeveloped andabdominal present) pattern T.lucifer belonging to morphologicalsubterranean specialization(Fig. 4).The includedindividuals first cluster all optimal 3,k of corresponding three to discrete classes species of similar with levels of adistinctsubterraneanspecies to of class specialization. The revealedan Gapstatistic Using morphological traits and a Ward2 hierarchical clustering method, we assignedeach specialization habitat Subterranean valuestime atthewhen thespecimens werecollectedcaves inthe(Appendix S1). effects of sex and population on . 1.43±0.19, p-value<0.01;Intermediate: estimated T. konradi vignai . The third cluster comprised highly specialized species (“High” class in Fig. 4), namely4),highlycluster (“High” class The inFig. comprised third species . specialized , T. lanai and T. pedemontanus , T. lucifuga T. and ∆ Twhen we estimated T. pluto, namely with species minor morphological T. bornensis , with pronouncedeye andprofilereduction, β ±SE=–0.68±0.07, p< indicating a 0.01), ∆ , T and theclassof specialization, T. iulianae, ∆ T using the cave temperature temperature cave the T using

T. nigraerosae Clusters were mostly

β ±SE= – ±SE= and T. T. Accepted Article 2013) across caves at different elevations in the Western Italian Alps, using Hygrochron using Alps, Italian Western inthe elevations different at caves across 2013) Feb 04 – 2012 Nov (20 inwinter taken were graph inthe shown records temperature experimental (B gradient elevational across regimes thermal in overlap no and seasonality thermal ( habitats cave shallow to Figure Compared 2. (Fig. 3C; PGLS: estimated ( tolerance thermal specialized steep decline in survival at rate increasing . specialization to deepspecialization tosubterranean life (Fig. 3F; F 0.31). Furthermore, realized niche breadththermal decreased significantly with increasing increasingvaluesat of niche breadth, with increasing variation in temperature among localitiesof a species’ range 5.47, p<0.05). recoveredWe significantsame the trend with respectthe to realized thermal significantly withincreasing specialization deep subterranean to life (Fig. 3D; ANOVA: the residuals (Pagel We We afound significant positive relationship between a species’ elevational range and Troglohyphantes ∆ T), T), with increasing elevational range extents atincreasing values of λ ∆ = –0.71). The extent of a species’ elevational range decreased T (Fig.3E;PGLS:estimated β β declined smoothlymore (Low:estimated ±SE= 930.06±184.66, p< 0.01) and no phylogenetic signal in A ∆ ), deep cave habitats show extremely reduced reduced extremely show habitats cave deep ), T. Conversely, the survival curve for low 2,8 = 4.61, p< 0.05). ±SE=4.02±0.92, Pagel p< 0.01; β ±SE 2.60±0.84). ). Seasonal F λ 2,8 = – ∆ TM T =

Accepted Article GLMM. from derived as conditions, subterranean the to adaptation morphological of class the with interaction to GLMs. breadth. of subterranean specialization, and relations with elevational range extent and realized thermal niche Figure 3. cave. same to corresponds color Same isshown. (max–min) range temperature Daily surface). from dataloggersinstalled cavethe at entrance (1–10surface)m from deeperand insidethecave (>100 m . in the inset inset the in classes of morphologicalspecialization to subterranean life.Color codingin life. subterranean in the extent of elevational range among the three classesof morphologicalspecialization to elevational rangeand C A) A) ) Relationship) between elevational range extent and ∆ B) Thermal tolerance ( B Survival rate as a function of afunction as rate Survival . Predicted relationship between the survival rate of of rate survival the between relationship Predicted

E ) Relationship between the thermal breadth experienced by species across their their across species by experienced breadth thermal the between Relationship ) ∆ T, according to PGLS. PGLS. to according T, ∆ T) of Western alpine Troglohyphantes alpine Western of T) ∆ T. Survivalcurves represent best fits to the data, according F ) Differences in realized thermalbreadth among T, according to PGLS. Troglohyphantes Troglohyphantes species according to the class class to the according species C–F C–F refers to the legend legend the to refers species and ∆ D ) Differences T, in

Accepted Article by Elena Pelizzoli. Misplaced individuals are highlighted with the color of their putative cluster. Morphological drawings , nigraerosae of subterranean specialization: low ( low specialization: of subterranean classes three the to correspond clusters distinct Three clustering. hierarchical and Ward2’s distances analyses.Clustering is based on three morphologicaltraits (bottom panel) and on Euclidean 4.OverallFigure morphological similarity among the . T. vignai T. ), and high ( T. konradi T. lucifuga , , T. lucifer T. lanai Troglohyphantes Troglohyphantes , , T. pedemontanus T. T. pluto ), intermediate ( species considered in the ) specialized species. T. bornensis , T. Accepted Article ee, ht loe u t caatrz te te gains f therm of gradients steep the characterize to us allowed that level, to date (Fig. 3A). also used annualWe temperature data series measured at habitatthe via experimental tests on the widest simultaneous set of congeneric subterranean organisms (seespecies distributions McAlister, Cardillo,Dinnage,& 2019), thermal wequantified traits andorganismal habitat Rather measures. than inferring thermal tolerance breadth from ecosystems is based on integrationthe of lines evidence multiple andof is grounded in (Fig. . Sánchez-Fernández,Fresneda,Cieslak, &Ribera, 2015). Mermillod-Blondinet 2013; al., Pallaréset al., 2019; al., etRaschmanová 2018; Rizzo, 2018) and hypotheses pertaining to thermal niche breadth in particular (Eme et al., 2014; explore eco-evolutionary questions in general (Mammola, 2018; Sánchez-Fernández et al., environmental stability, habitats emerge subterranean as an ideal system model inwhich to Scheffers 2018;ettheir Polato Due 2009; et al.,2017). al., ecologicalsimplicity and to Janzen’sof hypothesis andtransferring it non-tropical to 2016; settings(Gillet McCain,al., have largely prevented ecologists from comprehensively underlying the testing assumptions and challenge the posed by thegeographicscale which at hypothesisthis canbe tested, 2007), anddeep sea (Rex etal., 1993). Yet, ecologicalthe complexity of most ecosystems Ková (Bahrndorff, Loeschcke, Pertoldi, Beier, & Holmstrup, 2009; Raschmanová, Miklisová, displayinghabitats gradients steep of thermal seasonality, includingfor example soils Sheldon al, 2018), et alsobecause but it can potentially betransferred to alarge variety of biodiversity in patterns tropical versustemperate systemsmountain (Ghalambor et al., 2006; mountain passes hypothesis isseminal not only in providing a mechanistic explanation for among the100 most influential in papers ecology&(Courchamp Bradshaw,2018). The Five decades after its publication, Janzen’s (1967) milestone contribution has been listed Extending Janzen’shypothesis to temperate subterranean ecosystems DISCUSSION č Our extension of Janzen’s passes hypothesismountain to temperate subterranean , & Šustr,, & 2015), polar waters (Peck, & Webb, Bailey, 2004;Pörtner, Peck, &Somero,

l aiblt i caves in variability al

Accepted Article habitats is habitats greater much deep that of than subterranean (Pipan habitats 2011). et al., This result isconsistent with the fact that the seasonal variability ofshallow subterranean increase than species morphologically life in for specialized deep subterranean habitats. shallow subterranean habitats proved to be able to withstand a two-fold larger temperature class (Fig.notingpopulationsof 4).Inthisregard,isworthclass that it behaved similarlyto particular,species the specialization,of reaching CT low specialized species The thermal limits. tolerance its and, consequently,about species this of status taxonomic structuringgenetic (Mammola et al.,2015), which casts some doubtsabout current the specialized concomitanta reduction inthermalintermediate tolerance. Overall, andhighly subterranean demonstrate an that increased specializationto subterranean habitats was accompaniedby By integrating morphological measures and physiological experiments, we were first able to Thermal tolerance and subterranean specialization (Fig. 3B). species (Fig. 4), and toincorporate this information in the evaluation of the thermal tolerance statistical inference to estimate the level subterranean of specialization of differentthe 2). An additional novelty this study of was use a to quantitative method supported by result . two than closely related natural ambientnatural temperature,CT and CT 100 at 15 and27at other In °C. words,speciesthat are life morphologically specializedfor in PGLS analyses revealedweak a phylogenetic signalinour dataset, implying that two

pce slce a rno fo te hlgntc re This tree. phylogenetic the from random at selected species Troglohyphantes Troglohyphantes 50 at 7 andabove 19°C theirnaturalambient temperature, respectively, and Troglohyphantes T.vignai T. lucifuga andT.lucifer,T. lucifuga was the only species to exhibit a greater thermal tolerance. thermal agreater tolerance. In exhibit to only species was the species reachedCT species are necessarily not physiologically more similar 100 between 3and10°C. theintermediate classWithin T. pluto, which in clustered the low specialized 50 already between 1and 4 °C abovetheir showed thewidest thermal tolerances,

T. vignai T. display a high

Accepted Article and specialization thermal likelymost actsynergistically Habitat tolerance. thermal thegradient and fragmentationspecies’ rangeacross elevation its this study isthe first to a establish relationship between theextent a of subterranean influence of potential specialization propensitythermal ondispersal (Eme Yet, etal.,2014). Christman& Culver,2001; Moldovan, Meleg, & Per subterranean species has longbeen solely attributed to habitat fragmentation (e.g. cave habitats atdifferent elevations(Fig. 2B; Rubel etal., 2017). The small-range size of variation intemperature regimesignificant andreducedoverlapinthermal between deep subterranean dispersal over elevationalgradients would beunlikely, in account for the (Ghalamboret alpine In 2006). al., in the context which because they wouldlikelymoreencounteroutside be temperatures to their thermal tolerance less specialized topographic barriersthan difficulty species, would incrossing have more Havingadapted to narrow conditions thermal (Fig. 2B), specialized subterranean organisms Subterranean specialization and elevational range extent phylogenetic inour signalobserved study. Thisyields sisterspecies exhibiting contrasted thermal tolerance, hence theweak climatically-variable habitatevolved species ada shallow habitats—there subterranean is agreater chance that some species the from most species samefrom the specializedregion but habitats—indifferent to ourcase, deep and studies (Gutiérrez-Pesquera et al., 2016). Conversely, when using multiple congeneric physiological hence traits; strong the phylogenetic signal typically in anumber observed of withinthese tworegionsclosely are each and more other to they related displaysimilar testing Janzen’s hypothesis. Using species from temperate and tropical regions, species probably owed to our study'smuch whichconsisted inusing specificity a regional scale for .

pted toone.climatically-stable more the pted ş oiu, 2012), without considering the Troglohyphantes

to constrain dispersal. Habitatto

species are found, Accepted Article subterranean specialized Furthermore, 2015). al., et T. widely species lucifuga like ,distributed low alsoother but limited gene withflow, many populations being strongly isolated eachfrom other (Mammola mitochondrial andnuclear specialized speciesof indeed dependsondegree the subterranean of specialization. In several lines evidence of which dispersal suggest that within genus the surrogate for dispersalcapacity (Lester,Ruttenberg, Gaines, &Kinlan, 2007). Yet, arethere thermal specialization, which itself creates physiological barriers to dispersal. fragmentation restrictsgene maladaptive between flow populations, thereby facilitating in . radiation adaptive high to leading isolation, reproductive promote to contributed have may case of our model organisms, specializationthe to a habitat with limited seasonal variability during climate shifts (Jetz &Rahbek,2002; et Ohlemüller al., 2008; Eme al., 2014).et In the imposing barriers to dispersal, and decrease the extinction rate by local promoting survival topographic heterogeneity speciation bothinflate rate could by specialization and increasing Njunji packing that are typically indisparate observed subterranean systems2015Hedin, (e.g. potential explaining mechanism for local highrate the endemism and of speciesincreased subterranean habitats (Isaia etal., 2017). incollected caves Conversely,specialized andshort-rangedspecies of shallow habitats and would explain their larger distribution ranges (Mammola et al., 2015). Mammola et al., 2018a, 2018b). This ecological plasticity would enhance dispersal across strata, voids in rocky debris, and other moist and shaded retreats et(Isaia al., 2017; are typically both incaves andfound superficial includingleafdeep habitats, more litter ć Our extensionJanzen’s ofto subterranean also habitat hypothesis a provides A potentially important thecaveat of present study use isthe of range size as a et al., et Ribera 2018; et al.,2010; et al.,2013).Wessel is acknowledged It strong that , empirically suggestinga reduced capacity todisperse through non- Troglohyphantes Troglohyphantes

genetic data at theindicated interface population/species very for whichfor apopulation genetic studywas carried out,

species in central distributed andeasternAlps, Troglohyphantes are almost exclusively T. vignai Troglohyphantes

, the only only the , ;

Accepted Article 3,000 km 3,000 closelyas six relatedspecies are coexistinknown to thedistrict of Alpi Liguri, i.e. aboutjust geographic extents (Isaia al., et 2017; Mammola et al., 2018a). In the AlpsWestern as much the genusanda to high ofsmall-rangeproportion endemics,even co-occurring withinlimited . (Mammola et al., 2018b: p. 237, fig. 2), which is consistent with a scenario of restricted Troglohyphantes Troglohyphantes variability climate scales of have acted shape inconcert might the currentto distribution of and to recolonize vacant areas once climate becomes favorable again. The two temporal dispersal capacity, then these speciesare also less likely tosurvive climate change major seasonality drives subterranean species narrowtowards thermal specialization andlow seasonal scale. as If, pointed out by the results of this study, the ofabsence thermal latitudes isprimarily drivenby climatic variabilityat acting longermuch time-scales than the doeshabitats notnecessarily imply patternthe observed that of increasing rangeat higher Europe. Yet, the lack latitudinal of variation in temperature seasonality in subterranean accounted afor substantial variation in range sizegroundwater of crustaceans across Zagmajster et(2014) al. and Eme et(2018) al. showedthat long-term temperature variability ecosystems(Scheffers & 2018; ScheffersWilliams, et al.,2017).correlative Using methods, (Zagmajster etal., 2014), a topic that hasrecently been alsoindiscussed surface ofdistinct scales variability climate in shaping subterranean of patterns biodiversity Our contributefindings to reconcile contrasted views aboutthe relative importance of two Climaticcontrols onsubterraneanbiodiversity patterns may local favor survival through short distance dispersal during climate shifts. specialized subterranean question species idea the elevational sharp that gradients climatic subterranean habitats at different elevations and the very narrow thermal tolerance of 2 (Fig. 1).Yet,(Fig. the occurrence of non-overlappingregimes thermal among deep speciesin theAlps. one On Western hand,allspecializedthe subterranean species occur in areas that were free of ice during the Pleistoceneduring the ice of were that inareas free speciesoccur

Accepted Article note, this variability that suggests tolerantmore individuals possiblyundergo selectionmay greater temperature increases in respect to the population average (Table 1). On a positive temperature among populations,the tested with individuals some being able towithstand However,dispersal. we observed variability in individual the response increasing to species covered by theprimary ice shield during the Pleistocene, which could be explained by the range of low specialized species, especially dispersal offollowing retreat the Pleistoceneglaciers.On theotherhand, the distribution . subterranean species in light the these of habitatchangefuture projections, we canpredict that specializedmost climate change scenarios (Mammola et 2018b). al., By consideringthe results of this study of quality habitat forecasts for regions with uncertain climates, a proposal which is entirely consistent with previous SDM specialization-dispersalspecialized prevents tradeoff exploiting species subterranean from Fernández et 2016).al., Results of the present study suggest that the ecological greater thermal tolerance than reflectedthose bytheir current geographic ranges (Sánchez- (Mammola & Leroy, 2018). However, such predictions may be unreliable species if display species distribution models (SDM) projected onto climatefuture change scenarios response of subterranean species alteredto climatic conditions were based on correlative habitats.subterraneanthe attempting fewTo available studies to predictthe date, future evolutionary responses will depend on magnitudethe and rates warming of within dueto the inertia therockof thermal et2019). (Mammolaal., context, likelihood this In the of increasing ambient temperature and reducing moisture content, deep habitats subterranean (Mammola al., et 2019; Taylor 2013), especially etal., by subterranean species to climateanthropogenic change. Climate change is expected affect to al., 2018b). Our study implications hasalsoimportant vulnerability for determining the of

having colonized the areain recent time, after glaciers the retreated (Mammola et

willunlikelybe ableto overcome climatic alterations by of means Troglohyphantes T. lucifuga, spp. both Pleistocene in under spp. the and future stretches in areas that were entirely entirely were that areas in stretches

although with a certain delayalthougha certain with

Accepted Article CSTO162355). FM was supported by grants from the French programs STYGOMICS Thiswork was by funded UniversityTurin andof di Compagnia San Paolo (GrantAward: ACKNOWLEDGEMENTS change.climate into theirdispersal exercises, fitting modelling whenspecies response predictingthe to Therefore,off. we encourage scientists to take into consideration physiological barriers to (Nogués-Bravo et al., 2018) may inpart already beset by thespecialization/dispersal trade- contribution shows that the “move or adapt” indilemma response to climate change our Ultimately, ecosystems. of range abroad across instability climate to species ecological specialization/dispersal tradeoff into understanding the differential responses of we waythan climatically-stablehabitatsintegrating other pavethe the tropical for mountains, research into Janzen’s thoughts(Sheldon al., et 2018). By extending Janzen’s hypothesis to methodology; . constructive comments. for proof-readingour comments; English and useful and two anonymous reviewers fortheir Zerbetto assistance; andRaffaella Yael fieldwork for Filippo Lubin Milano, NicolòChiappetta in the laboratory and to Andrea John Dejanaz for carrying out morphological measures; to H2O’Lyon (ANR-17-EURE-0018). A special goesthanks Alexandra to Jones assistance for andEUR ANR-15-CE32-0005) (no. CONVERGENOMICS Enviromics), Défi (CNRS andadaptation a of change climate in key physiological in traits scenario, resulting phenomena local of SM Author con , MIandEPconceivedthe ideaanddesigned Concerns over the fate biodiversityof inachanging climate have revived biodiversity tributions statement: in situ persistence. situ in

Accepted Article doi:10.3986/ac.v31i1.402 Cigna, A. A. (2002). Modern trend(s) in cave monitoring. habitat. available and biodiversity cave between relationship The 2001. C. D. & Culver, C., M. Christman, Journal of lme4. using models mixed-effects linear Fitting S. (2015). B., Walker, Bolker, M., Mächler, D., Bates, Ecology habitat. the of variability thermal by influenced is Collembola of response hardening Bahrndorff, S., Loeschcke,V., Pertoldi, C.,Beier, C., &Holmstrup,(2009). M. The rapidcold 39, 427–448.doi:10.3986/ac.v39i3.74 underground? weather the What’s meteorology. Underground (2010). G. Badino, REFERENCES (doi:10.6084/m9.figshare.8058302) [the link will available become upon acceptance] Data accessibility statement: Disclosure statement: All authors contributed critically draftsthe to and gave approvalfinal for publication. SM led thewriting of the manuscript; SMand EP analyzed the data; SM performedand MI fieldwork; PVprovided ecophysiological arguments; FM hypothesis provided the andframework eco-evolutionaryarguments; . doi:10.1111/jbi.13477 breadth and geographic range size across plant species. species. plant across size range geographic and breadth Cardillo, M.,Dinnage, R., & McAlister, (2019). W. The relationship betweenenvironmental niche Journal of Biogeography doi:10.1111/j.1365-2435.2008.01503.x 340–347. , 23,

Statistical Software Statistical

The authors report no conflicts of interest. , 67 , , 1–48. doi:10.18637/jss.v067.i01 1–48. ,

28, 367–380. doi:10.1046/j.1365-2699.2001.00549.x

Data studysupporting this areavailable inFigshare

Journal ofBiogeography

Acta Carsologica

Acta Carsologica Acta

, , 46 31, 35–54.

Functional , 97–109.

, Accepted Article smaller ranges but does notchallenge biodiversity determinants. crypticspecies mattermacroecology? in Sequencing European groundwatercrustaceans yields Eme, D., Zagmajster, M.,Deli ofSciences Academy National latitude. across ectotherms terrestrial on warming climate of Impacts P. (2008). R. Deutsch, C. A., Tewksbury, J. J., Huey, R. B., Sheldon, K.S., Ghalambor,K., C. Haak, D. C., & Martin & Evolution read. should ecologist every articles 100 (2018). A. J. C. Bradshaw, & F., Courchamp, . passes are higher inthe tropics’ hypothesis. ‘mountain the for support biogeographic reveals diversity species Cryptic A.(2016). S. Thomas, ... G., D. Gannon, S., A. Flecker, C., A. Encalada, L., K. Casner, C., B. Kondratieff, A., B. Gill, doi:10.1093/icb/icj003 17. passes higher in the tropics? mountain Are (2006). G. & J., Wang, J. Tewksbury, P. R., Martin, B., R. Huey, K., C. Ghalambor, of Climatology Journal International areas. land global 2: Worldclim J.(2017). R. & Hijmans, E., S. Fick, doi:10.1111/ecog.02683 Araneae) from central Texas. Texas. central from Araneae) Hedin,M. (2015). High-stakes speciesdelimitation ineyeless cave spiders( tadpoles. temperate and tropical of limits tolerance thermal in hypothesis variability climate the Testing (2016). Solé M. & A., Nicieza, H., Duarte, Á., M. Olalla-Tárraga, M., Tejedo, L. M., Gutiérrez-Pesquera, 283, determinants. determinants. range species explore to modelling habitat and physiology phylogeography, Integrating (2014). J. C. Douady, ... L., Konecny-Dupré, S., Calvignac, P., Jean, C., Colson-Proch, F., Malard, D., Eme, Biodiversity genus spider the of systematics Isaia,M., Mammola, S.,Mazzuca, P., Arnedo, M. A.,& Pantini, P. (2017). Advancesin the 20160553. doi:10.1098/rspb.2016.0553 Journal of Journal , 2, , , 307–326. doi 10.1080/14772000.2016.1254304 doi 307–326. 15, Journal of Biogeography of Journal 395-401. doi:10.1038/nature.2016.20987

Biogeography Molecular Ecology Molecular ć Janzen's hypothesis revisited. revisited. hypothesis Janzen's , , T.,C., Fišer,, Flot, F., J. Konecny-Dupré, ...Malard, L., F.(2018). Do 105(18), , 43 , 1166–1178. doi:10.1111/jbi.12700 Troglohyphantes , 41 6668-–6672. doi:10.1073/pnas.0709472105 , 687–699. doi:10.1111/jbi.12237 687–699. , Proceeding of The Royal Society B: Biological Sciences , 24, 346-361. doi:10.1111/mec.13036 New 1-km spatial resolution climate surfaces for for surfaces climate resolution spatial 1-km New , 4302–4315. doi:10.1002/joc.5086 doi:10.1002/joc.5086 4302–4315. , 37 (Araneae, Linyphiidae). Integrative Comparative Biology

Ecography

Cicurina Proceedings of the the of Proceedings Systematics and

, Nature Ecology Ecology Nature 41, 424–436. , Dictynidae, , 46, 5– , Accepted Article environment. environment. constant thermally a in living crustaceans groundwater among breadths tolerance Thermal (2013). Mermillod-Blondin, F., Lefour, C.,Lalouette, L., Renault, D.,Malard, F.,Simon,L., & Douady, C.J. ecosystems. cave on alterations climatic global of effects the deep: going change Climate M. (2019). Isaia, Mammola,S., Piano, E., P.,Cardoso, Vernon, P., Domínguez-Villar, D., Culver, D. C., Pipan,T.,& extinction. to spiders cave drive may change Climate (2018b). M. & Isaia, S. L., S., Goodacre, Mammola, Linyphiidae: (Araneae: spiders subterranean insibling partitioning niche Spatial in darkness? speciation Ecological (2018a). M. & Isaia, N., Chiappetta, E., PIano, P., Pantini, A., M. Arnedo, S., Mammola, evolutionsubterranean of species. and origin on the light shed spiders endemic Alpine A.(2015). M. Arnedo, &M., Isaia, S., Mammola, size. range geographic and ability dispersal between relationship The (2007). B. P. &Kinlan, D., S. Gaines, I., B. Ruttenberg, E., S. Lester, Physiological Entomology tolerance of sympatric hymenopteran parasitoid species: does it match seasonal activity?. Le Lann, C.,Roux, O., Serain, N.,VanAlphen, J.J., Vernon, P.,Van Baaren, J. (2011).Thermal Science richness. species avian of determinants and size range Geographic (2002). C. & Rahbek, Jetz, W., 233–249. Janzen, D.H. (1967).mountain Why passes arehigher inthe tropics. doi:10.1146/annurev.ecolsys.33.010802.150520 evolution. and oscillations Milankovitch Jansson, R., &Dynesius,M.(2002). The of clades fate ain world of recurrent climatic change: . 0248.2007.01070.x subterranean habitats. habitats. subterranean other and caves to models distribution species Applying B. (2018). S. &Leroy, Mammola, and White. Poulson after years fifty in ecology models as caves dark: the in answers Finding (2018) S. Mammola, , 297

Ecography, 41 Troglohyphantes , 1548–51. doi:10.1126/science.1072779 The Anthropocene Review Anthropocene The Journal of Experimental Biology Experimental of Journal

Ecography Ecography, 41 , 233–243. doi:10.1111/ecog.02902 233–243. , , , 21–28. doi:10.1111/j.1365-3032.2010.00758.x 21–28. 36, ). Invertebrate Systematics Invertebrate , 41 , 1–21. doi: 10.1111/ecog.03905 PeerJ, 3, PeerJ, , 1194–1208. doi:10.1111/ecog.03464 , AnnualReview Ecology of and Systematics,33 00, 1–19. doi:10.1177/2053019619851594 e1384. doi:10.7717/peerj.1384 , 216 Ecology Letters, 10 Letters, Ecology , 1683–1694. doi:10.1242/jeb.081232 , 32 , 1069–1082. doi:10.1071/IS17090. 1069–1082. ,

, 745–758. doi:10.1111/j.1461- 745–758. , The American Naturalist, 101 Naturalist, American The

, 741–, 777.

, Accepted Article Leptodirini). Leptodirini). climate change. from species small-range to risk high rarity: species and climatic of coincidence The (2008). C. D. Ohlemüller, R., Anderson, B.J., Araújo, M.B.,Butchart, S. H., Kudrna, O., Ridgely, Thomas,R. S., & of the analysis evolutionary Comprehensive (2018). Njunji Evolution & Ecology change. climate past to responses biodiversity of code the Cracking T. (2018). S. Jackson, L Orsini, F., Rodríguez-Sánchez, D., Nogués-Bravo, 014-9161-z algorithms implement Ward’s criterion?. which method: clustering agglomerative hierarchical Ward’s P. (2014). & Legendre, F., Murtagh, 1 Biology, Organismal evolution. physiological of rates and behavior, thermoregulatory variation, climate connecting effect: Bogert the meets hypothesis Janzen’s L. (2019). B. & M., Bodensteiner, M. Muñoz, Journal of Python. R and for routines clustering agglomerative hierarchical, fast fastcluster: (2013). D. Müllner, sizes. range species plant in variation geographical determine stability climate and area Habitat (2013). J.-C. Svenning, Morueta-Holme, N., Enquist, B.J., McGill, B.J., Boyle,B., Jørgensen, P.M., Jeffrey, E. O., ... groundwater populations Moldovan, O. T., Meleg,I. N., Per & Ecology Letters, 10 biogeography. and extinction speciation, gradient: diversity latitudinal the and Evolution (2007). M. C. Mittelbach, G. G., Schemske, D. Cornell, W., H. V., Al Ecology Letters, 12, McCain,M. C. (2009). Vertebrate range sizesindicate thatmountains maybe ‘higher’ the in tropics. . ć, I., Perrard, A., Hendriks, Schilthuizen,K., M., Perreau, M., Merckx, V., ... Deharveng, L.

Statistical Software, 53 Software, Statistical PloS ONE, Biology Letters , 315–331. doi:10.1111/j.1461-0248.2007.01020.x 550–560. doi:10.1111/j.1461-0248.2009.01308.x , Ecology Letters, 16, Letters, Ecology 33 , oby002. doi:10.1093/iob/oby002 oby002. , , 765–776. doi:10.1016/j.tree.2018.07.005 13(6), e0198367. doi:10.1371/journal.pone.0198367 e0198367. 13(6), . Ecohydrology, 5 . Ecohydrology, , 4 , 1–18. doi:10.18637/jss.v053.i09 1–18. , , 568–572. doi:10.1098/rsbl.2008.0097 , 568–572. oiu, A. (2012). Habitat fragmentation and its effects on on effects its and fragmentation Habitat (2012). A. şoiu, 1446–1454. doi:10.1111/ele.12184 Journal of Classification, 31 of Classification, Journal , 445–452. doi:10.1002/eco.237 len, A. P., Brown, M.,J. Bush, M. ...B., McCain, Anthroherpon ., De Boer, E.,Jansson, R., Morlon, H., ... &

radiation (Coleoptera, Leiodidae,

, 274–295. doi:10.1007/s00357- , 274–295.

Integrative Trends in Accepted Article presence of troglobionts in terrestrial shallow subterranean habitats. habitats. subterranean shallow terrestrial in troglobionts of presence the and variation Temperature (2011). C. D. & Culver, S., Polak, P., Oromí, H., López, T., Pipan, doi:10.1038/365636a0 scale latitudinal patternsof species diversityin the deep-sea benthos. Rex, M. A., Stuart, C. T.,Hessler, R. R., Allen, J. A., Sanders, H. L., & Wilson, G. D. F. (1993) Global- 78 Biology, Thermal climatic variability hypothesisinedaphic and subterranean Collembola (Hexapoda). Ková V., Šustr, N., Raschmanová, 802–811. doi:10.1515/biolog-2015-0095 inversion. microclimate strong with doline karst collapse in a Collembola soil of tolerance Raschmanová, N., Miklisová, D.,Ková Vienna, Austria:FoundationR Statistical for Computing. Computing. Statistical for Environment and ALanguage R: (2017). Team Core R Development Sciences, 362(1488) view. integrative an ectotherms: Antarctic marine in adaptation and limits Thermal (2007). G. Somero, & L., Peck, O., H. Pörtner, doi:10.1073/pnas.1809326115 Proceedingsof theNationalAcademy of Sciences,115(49), mountains. tropical in speciation higher drive dispersal low and tolerance thermal Narrow (2018). C. B. Kondratief, ... A., Barthelet, K. L., M., Casner, M. Gray, A.A., B. Shah, A., Gill, N. R., Polato, 8463.2004.00903.x species. marine Antarctic in temperature Peck, L. S., Webb, K. E., & Bailey, D. M. (2004). Extreme sensitivity of biological function to common and stablethermal conditions. under living subterranean unrelated in capacity acclimation and tolerance Heat (2019). Pallarés, S., Colado, R.,Pérez-Fernández, T., T., Wesener, Ribera, I., Sánchez-Fernández,& D. . 257– 273. doi:10.1080/ 273. 257– Mixed Effects Models. R package version 3.1-131. version R package Models. Effects Mixed Pinheiro,J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team, (2017). nlme: Linear and Nonlinear

, 391–400. doi:10.1016/j.jtherbio.2018.11.004 , 2233-2258. doi:10.1098/rstb.2006.1947

00222933.2010.523797

č , L., Parimuchová, A., & Miloslav Devetter, M. (2018) Testing the the Testing (2018) M. Devetter, & Miloslav A., Parimuchová, L., , Philosophical Transactions of the RoyalSociety B: Biological č , Ľ BioRxiv ., & Šustr, V. (2015). Community composition and cold cold and composition Community (2015). V. &Šustr, ., Functional Ecology

, 598458.doi:10.1101/598458

, 18, 625–630. doi:10.1111/j.0269- 625–630. 18, ,

Journal of Natural History, 45 History, Natural of Journal Nature , 636–639. , 165 636–639. ,

12471–12476. 12471–12476. Journal of of Journal Biologia , , 70, Accepted Article doi:10.1086/697046 perspective on Dan Janzen’s classic article. article. classic Janzen’s on Dan perspective a passes: mountain years of Fifty J. (2018). N. &Sanders M., Kaspari, B., R. Huey, S., K. Sheldon, arboreal species. for not but – reach of out are passes mountain Tropical E. (2018). S. & Williams, B. R., Scheffers, doi:10.1111/geb.12585 instability. climatic to vertebrates of resilience the increase movement (dispersal) horizontal and (arboreality) Vertical E. (2017). S. J., ... Williams, VanDerWal, A., Anderson, L., S. Macdonald, B., Phillips, L., Shoo, B. R., Scheffers, and latitude. elevation across insects of aquatic limits thermal predicts variability Climate C. K. (2017). Ghalambor, Shah, A.A.,Gill,B.Encalada,A., C., A. Flecke doi:10.3897/subtbiol.25.23530 research. evolutionary and biogeographical ecological, in system model a potential as environment subterranean deep The (2018), I. Ribera, Sánchez-Fernández, D.,Rizzo, V., Bourdeau, C.,Cieslak, A., Comas,J., Faille, A., Fresneda,J., ... 6 Reports, change. climate with cope to species dispersal poor of capability the and estimators niche Sánchez-Fernández, D.,Rizzo, V., Cieslak, A., Faille,A., Fresneda, J., & Ribera, I. (2016).Thermal doi:10.1127/metz/2016/0816 1800–2100. zones climate Köppen-Geiger high resolution Rubel, F., Brugger, K., Haslinger, K., & Auer, I. (2017). The climate of the European Alps: Shift of very 15(1) adjustment to ambient temperature in highlyspecialized cave beetles. Rizzo, V., Sánchez-Fernández, D., Fresneda, J., Cieslak, A., & Ribera, I. (2015). Lack of evolutionary species diversity. in gradients latitudinal explain cannot and phenomenon local a is rule Rapoport’s (1996). K. Rohde, Biology Ancient origin of a Western Mediterranean radiation of subterranean beetles. Ribera, I., Fresneda, J.,Bucur, R., Izquierdo, A., Vogler, A. P.,Salgado, J.M.,& Cieslak, A. (2010). . , 10.doi:10.1186/s12862-015-0288-2 ,10, 29. doi:10.1186/1471-2148-10-29 , 23381. doi:10.1038/srep23381 Functional Ecology, Frontiers in Ecology and Environment, 16, Biodiversity Letters,3,

, 2118–2127. doi:10.1111/1365-2435.12906 doi:10.1111/1365-2435.12906 2118–2127. 31, 10–13. doi:10.2307/2999704 10–13. GlobalEcology Biogeography, and 26 Subterranean Biology,25 The American Naturalist, 191, Naturalist, American The

r, A.S., C., Funk, Guayasamin,W. J. M.,... Meteorologische Zeitschrift, 26 101–108. doi:10.1002/fee.1764 , 1–7.,

BMC Evolutionary Biology, Biology, Evolutionary BMC 553–565. 553–565. BMC Evolutionary Evolutionary BMC , 787–798. Scientific Scientific , 115–125. Accepted Article gap statistic. gap statistic. the via set data in a clusters of number the Estimating (2001). T. & Hastie, G., Walther, R., Tibshirani, doi:10.1073/pnas.1817774115 doi:10.1111/1467-9868.00293 Appendix S1. Alternative results using seasonal cave temperature values to estimate mechanisms. mechanisms. causal potential on perspective time deep A phenomenon? a recent rule Rapoport’s Is (2013). S. M. Veter, N. M., DeSantis,L. R. G.,Yann, L.T., Donohue, S. L.,Haupt, R. J., Corapi,S.E., ... Velkovsky, change. climate and water Ground Taylor, R.G.,Scanlon, B., Döll, P.,Rodell, Beek, VanM., R.,Y., Wada, ...& Konikow, L. (2013). doi:10.1098/rspb.2010.1295 1830. ectotherms. Sunday,J. M., Bates, A. E., & Dulvy, N.K. (2011). Global analysisof thermal tolerance and latitude in decade. Smith,M.A. (2018). Janzen’s mountain passes hypothesisis comprehensively tested in its fifth . doi:10.1111/geb.12200 habitats with low thermalseasonality. from insights crustaceans: of groundwater diversity beta and size range in variation Geographic Zagmajster, M., D.,Eme, Fišer, C., Galassi, D., Marmonier,P., Stoch, F., ...& Malard, F. (2014). in the tropics. The American Naturalist, 133 Stevens,(1989). G.C. latitudinal The gradientsgeographical in range: how so manyspecies co-exist limits. limits. Paratarsotomus macropalpis mite the in resistance water and tolerance thermal Exceptional (2015). C. J. & Wright, C., G. Wu, 110(23), Sciences, of Academy National Founder effects initiated rapid species radiation in Hawaiian cave planthoppers. Wessel,A., Hoch,Asche, H., M.,von Rintelen,T., Stelbrink, B., Heck,V., ... Howarth, F.G.(2013). Additional supporting informationAdditional in supporting may befound onlinethe version this of article. INFORMATION SUPPORTING Journal of Insect Physiology Insect of Journal Proceedings of the National Academy of Sciences, 115(49), Sciences, of Academy National the of Proceedings Proceedings of the Royal Society of London B: Biological Sciences Journal of the Royal StatisticalSociety: Series B (Statistical Methodology), 63 Biology Letters, 9, Letters, Biology

(Erythracaridae) challenge prevailing explanations of physiological physiological of explanations prevailing challenge (Erythracaridae)

20130398. doi:10.1098/rsbl.2013.0398

,

Nature Climate Change 82 , 1–7. doi:10.1016/j.jinsphys.2015.08.002 doi:10.1016/j.jinsphys.2015.08.002 1–7. , 9391–9396. doi:10.1073/pnas.1301657110 , 240–256. doi:10.1086/284913 GlobalEcology Biogeography, and 23 , 3 , 322–329.doi:10.1038/nclimate1744

, Proceedings of the 278(1713)

12337–12339. 12337–12339. , 1135–1145.

, 411–423. , 1823– , ∆ T. T.