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Abstract

Current analyses and predictions of spatially‐explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing, or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently this database contains time series from 7538 temperature sensors from 51 countries across all key biomes. The database will pave the way towards an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
SoilTemp: a global database of near-surface
temperature
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A:%'M&*P#&-Q!#&:!=&
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A4&S:5#,=&!7@8B& ,.
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@'*(D)05'*7.''8B1
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&  B            W(    M
M "\%&!
B(DW(D&1953/3/0D%
$     +K    VJ?2 +V0161;4 F +K
& & "  "  M  +K W(    
#]?J213+V029612 90+K& ' W^B!_
` CLZB!@%W 5/6:+%0W >[W()  >
"*B(&$W(BN + 
W# 2/ 3"B  +$!WB\ ) )24:9 + 2:61;!?  B\  W(
 +$!  $%B $! $   "  &   $(
PZ$["+"W(+P+P+ 
$    Ba    (  Z$B[    +      B    
  (  W(    +  +   B  $    
B  !    B(      BI  Vb  :/31  Vb
K   % W      +      "    $  M  :3/4
  K & !        B(    c
W(  !  c ' B(    +  c  W(  ! c *
!%W(5;32%
    B( %  W( ;1 % ( 
943:26!BW(D& ),)N;9/B0
;/64/D&  D& D& ( + ),)N;92B0;/6 4/
D&  BN B$ B
W(    D  !025;:5  D  D   B &0(0I0
$    W(  D 45/54 D D  D
+$((Z!([IddKKD%$
KW(KD& $.D&
D   W( I0M& I Z[ D ' $ 
&0$,"@DW(+?9643///D*
W( ),N:45" W!"!
D&    W(      +K  &  &  "  W(
#?     B  B(  % W( E
$!"%I)%))DW(0D$M
$("519/!(K% ?
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 NB"  D&T0W(+E0%
 & )  K  D &0 W( +E0%  ' )%)
(( $  B  B(     223/52 ?
)44*DBa &$$"Z$%0+,%$+B[$
> ;;6/       B(  "  %
W("#3; +&B
BW(MM%
M  1611    +  $    +  !    B  
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I(%IW!E?++
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%!%!$%;9669W&!B+W
W(64/6 ,I  W:;411W ' !  $
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+De(E2;:;2/O!?!W(
D2:/52D   !  +    B(
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!BB(+E1//B4//2(
!  BB(   + +
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  ? W( B02/9 32 ?   !  
B(  BI V W( 1 :/31 V K  %
"!W(%VC245//6
G?CRE(+K&& !DB&
W(M W# ' !D @?   W(  f M?
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I  $%& #& D  B +&
  B  D% % W(
+24//1; + & E ? E% ?+  99/2/
 $& %"+"$V()D)?
11  39//2  V(  (? &  B D !  B(  B
W(b&b&D&%+(>YD=
B0;/4  ;/  D&   &&   +    $  B(  
,%$)$)?(W(5?(?96/:1,W?&'
$%"Z$%,[I(::&542///'*
DW( B& #L  B& BI3 4"" W # '
KW(VW'!B(
    W(+? + 3;51/W ' 
W(   $ + C :B56962
W   '   "    $  M  :3/4    K '
B  +"  D&N0 W( 
 CC1 6/4/D&N  '  W# +  B \ I 
B ?  BI19 /c W # '% W(    D
  b  1  45/55  D=  D) '& W(    %  !
!     B( + $(  '' M M
 &$U W( %/:6;/W**$!
 !( VK   *  M $ IK + 
    & ;6 2;;54  D * D
!  I&0W(Y  K    D * +        
D!DW(&51/5/&D
*! MB+(W(" D("41922
W*%B(W("D("41922
W* VE"B%,&(
BB(B+%W(1//1;2+*%
(BD&N0W(`
D&N6/41'D B+,Z B[W(
192/  ME?   *& "          !        
D,W(611//,? *'W(%&
++  * !  D ? W(  +K &  
++W B(   > +
$  ?&    $    %  I    "?  94652
DD"W(!&0B;62;2BD
   & !) D W( V M
23/ :/65 V K   W + B  " $ 
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Abstract
        
 !"
# $"      % "
#&'
"      (        %)#  $"  
*+%"",##%'
!      "-      "+  %    "    
'      !  ,##  "'  %  '    
-#.%""
"'
   % "  "
""'!"
""%#
$("'!"""+
  .$'      +  %        
%"!#"+
)+#
$"+ ! %" ! !% + 
  + "  +! " %+    "
    (      %        
#
Keywords: '  ' "' ' +'
'+'
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(.
    "      !"      
'  !"  +  +     +      +
%+,/")''0)''0)'
-#1" 2  "  
"    "  2        +        
      !"  "
  % "  ,3+"  )' -#
4%'      "  !"      "      
        5    +%    6  !  ! ,
77'8'-# $""
  "   "  '  (
'%" % "
!'  ,77'9''/  )''
0: ;  &+'  -#  <'  !"  "    
      (    '  "    *  
+"""+,9=)''1"
)''3+")'-#='"
!  +        %+'  %
    "      %%        +  
              '    +*  
 "  % ,9 =)''3)''
/)''.)'-#4"!%
'!""+"
"    %% 
,>+;'-#<%
  +    '   !      "      "
  "    '      "    
'     0'    "     ,0:  ;
&+'-#
D:.,.,B
$" * +!  
"' +%"*
+"%'"?+%+!
,@'  '  3+"   )'  -#  $"  *        +"
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A" B  A%B  ,=# -'    % 
  %# = ' 0 ,-  
        %  "    "  %  
*  "    +#  $"  %      "  *  
%  "  ,##      %-'  !  %    
",##''+)-
,3'  '  C"  )'  '  3+"  )'  -#  $"      "
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  '  +      +*  *'  ##  "    %+  
"!""",@''1";
@'-#& """"
" # $" ++) "" 6
( %   (  ,##* 
+D/  )' -'  " !"
      *  +  "+   ,##  "
"'    "  %' !"" 
DE"''1";@'-#
&! "  %(*+! 
        *  !"  "  +'       #  =
'  ' "!%  
  "              '
!""          ""      ""  %%  "
,@' -# &!%'  !' "   "  
+*"% *'%"
'!"+%"
"%%,F)'-#<'"%
G+)"+)"!%'""
+ +%%"  +!
"""""
* "  '  "  "
    "    ,0:  ;  >'  '  3   )'  '
C!)'-#1'" "'
!+!""'!
("22,1"
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"),I)''
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;  >'  '  0  )'  '  L )'  '    /)  )'
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+)      +'    6    +      
!"    +    !"        "
 ,.   )'  -# E"    '  !      )
""""%+
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  !  +     ,>+  )'  '  4  )'  -#
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    !"  (    '    !    "!  
"!!""% ,4'-#
1'      "      "  "  "      "
(                "  '  )
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  !  +      #  $"  +  +    +
''"!"""
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$"   """ "       
!""+"
!'!"'
%+,##&M3.1,0
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,/ )''3+"  )'-'!
!"" " %% 
%  6  O  (  P    ,##    %  -
      *  %  +  "      "
  ,/)'-# $" "'+
      (+  % 
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    " +    % ,/ )' -'
    +"  +"  
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 ,1" )' ' 1"  )' '  )'
'1)' -'"% )(
",##".%,.-'-#3
   +   '  " " "' 
" + " +   +! " % % ,/
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   !   ' !   % 
  )!    "  *      "    %+
+!%    ,1)''3+"
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   ,1"  ;  @'  '  >%  )'  '
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,=#-#
E"  "              %  !  +  (  ,
+!-'  "  +  !  !  +      "  +"
"+"%,@'
-' !"""+)#$"!+ %6
   "   " * "   %
 K  +"
!'!"""#<!!
"!+ %
+            ,-  ,0  )'  '
0)'+')''4)'-#<!!
  +  !    (
+  %+  ,##  !    '  !    "-  
%,0:;&+'-#
S'"?+*"+
!  +      %      "  "  +!  B
 ""' 
      %  +          
  #  <'    "      
%       (   !" " 
%  (        )      "  %  
% !'  "+%
,3+" )' -# 1' " ( + *  + 
      !  "  %      
+%+,/)'-'!
"6!"%
% ,8))'-#$"
    "  +'      !""    %      
              '  !    !  (    
  "  +    " )
+!        "     "    ,3  )'  '
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E   )'  '  /   )'  '  4'  -'  %  
+%"#
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$  "  "  '  !      !    
 + " " ! .$ +# 1 
      "    '  !"      
 "'  "'""+%" 
!'+'+!+#J
"+"!)'! 
 # $" "% "%
        "%      "  !"  
%+,##""%-#
<  !  "%  "      "  "        +!
'  !    *%  +  "          "  
!),E)'-#
E   +" +%   '  
"%+'"
  ,##  "+'  "  %%
-      ,$+  -#  @%      !
'!+"
%("+%#4('
",+"
%-" 
,=# +-' 1'1'1 " 1,=#-#
9  +  !  +  %    "    "    +  
"+,.4
!"-#
/  + ' ! "
%  "  +    +          , )'  '
@)'-'!"++!)2
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,4  ;  M"'  -#  4      %+    "  .$
!+'+%=",9L<10.6084/m9.figshare.12126516-#
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E" +"'".$+%% ,##
+%+- !+  %+ " 
+   '  " + +! 
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4    %  "    
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Acknowledgments
We thank Sylvain Pincebourde and an anonymous reviewer for their critical evaluation of our manuscript. This work was
supported by the Research Foundation Flanders (FWO) through a postdoctoral fellowship to Jonas J. Lembrechts (12P1819N)
and a Research Network Grant (WOG W001919N). We gratefully acknowledge all data contributors, all staff of the author
institutions engaged in field measurements and equipment maintenance (namely Erik Herberg, Iris Hamersveld, Ida Westman,
Fredrik Brounes, Pernille Eidsen, Eleanor Walker and the teachers participating in the Tepåseförsöket 2015) and the ILTER-
network, and thank local peoples for permission to collect data on their lands. Temperature data collection on European
GLORIA summits was funded by European Union FP-5 project GLORIA-Europe (EVK2-CT-2000-0006) and the Swiss MAVA
Foundation project ‘Climate change impacts in protected areas of the Alps and high mountains of Eastern Europe and the
Mediterranean region’, on the Eastern Swiss GLORIA summits by the Swiss Federal Office for the Environment (FOEn), the
Research Commission and staff of the Swiss National Park, and the Foundation Dr. Joachim de Giacomi, on Tenerife in the
framework of the Flexible Pool project (W47014118) of Sylvia Haider funded by the German Centre for Integrative Biodiversity
Research (iDiv) Halle-Jena-Leipzig, on Livingston Island, Antarctica by different research projects of the Gobern of Spain
(PERMAPLANET CTM2009-10165-E; ANTARPERMA CTM2011-15565-E; PERMASNOW CTM2014-52021-R), and the
PERMATHERMAL arrangement between the University of Alcalá and the Spanish Polar Committee and on the Western Swiss
GLORIA summits by Département de la culture et des sports du Valais, Fondation Mariétan, Société académique de Genève,
Swiss Federal Office of Education and Science and Swiss Federal Office for the Environment. Jan Wild, Martin Macek, Martin
Kopecký, Lucia Hederová, Matěj Man and Josef Brůna were supported by the Czech Science Foundation (project 17-13998S)
and the Czech Academy of Sciences (project RVO 67985939), Meelis Pärtel by an Estonian Research Council grant (PRG609)
and by the European Regional Development Fund (Centre of Excellence EcolChange), Lena Muffler, Juergen Kreyling, Robert
Weigel, Mario Trouillier, Martin Wilmking and Jonas Schmeddes by DFG GraKo 2010 Response, Juha M. Alatalo by Qatar
Petroleum (QUEX-CAS-QP-RD-18/19), the authors from Odesa National I. I. Mechnikov University (Sergiy Medinets and
Volodymyr Medinets) by EU FP6 The nitrogen cycle and its influence on the European greenhouse gas balance (NitroEurope),
EU FP7 Effects of Climate Change on Air Pollution Impacts and Response Strategies for European Ecosystems (ÉCLAIRE),
Ukrainian national research projects (No. 505, 550, 574) funded by Ministry of Education and Science of Ukraine and GEF-
UNEP funded ‘Towards INMS’ project, see www.inms.international for more details. Florian Zellweger was supported by the
Swiss National Science Foundation (grant no. 172198), Peter Barančok, Róbert Kanka, Jozef Kollár and Andrej Palaj by the
Slovak Scientific Grant Agency (project VEGA 2/0132/18), Jonas Ardö by a infrastructure grant from faculty of Science, Lund
University, Julia Kempinen by the Doctoral Programme in Geosciences at the University of Helsinki, Jan Altman by the Czech
Science Foundation (projects 17-07378S and 20-05840Y), the Czech Academy of Sciences (project RVO 67985939) and
Ministry of Education, Youth and Sport of the Czech Republic, program Inter-Excellence, subprogram Inter-Action (project
LTAUSA19137), Toke Thomas Høye by the Carlsberg Foundation (grant no. CF16-0896) and the Villum Foundation (grant no.
17523), Jiri Dolezal by the Czech Science Foundation (projects 17-19376S), and Ministry of Education, Youth and Sport of the
Czech Republic, program Inter-Excellence, subprogram Inter-Action (project LTAUSA18007), Nico Eisenhauer, Felix Gottschall
and Simone Cesarz by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the
German Research Foundation (FZT 118), Stuart W. Smith by AfricanBioServices project funded by the EU Horizon 2020 grant
number 641918, Haydn Thomas by a K Natural Environmental Research Council doctoral training partnership grant
NE/L002558/1, Isla H. Myers-Smith by the UK Natural Environmental Research Council ShrubTundra Project NE/M016323/1,
Anibal Pauchard, Rafael Garcia and Eduardo Fuentes-Lillo by the projects Fondecyt 1180205, Fondecyt 11170516 and
CONICYT PIA APOYO CCTE AFB170008, Rafaella Canessa, Maaike Y. Bader, Liesbeth van den Brink, and Katja Tielbörger
by the DFG Priority Programme 1803 EarthShape (projects 1 (BA 3843/6-1) and 11 (TI 338/14-1&2)), Martin Svátek by a grant
from the Ministry of Education, Youth and Sports of the Czech Republic (grant number: INTER-TRANSFER LTT17017), Mihai
Pușcaș by ODYSSEE project (ANR-13-ISV7-0004 France, PN-II-ID-JRP-RO-FR-2012 UEFISCDI Romania), Pavel Dan
Turtureanu by UEFISCDI in Romania, MEMOIRE grant no. PN-III-P1-1.1-PD2016-0925, Jonathan Lenoir by the Agence
Nationale de la Recherche (ANR) within the framework of the IMPRINT project "IMpacts des PRocessus mIcroclimatiques sur
la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat" (grant number: ANR-19-CE32-
0005-01), Radim Matula and Roman Plichta by a grant Inter-Excellence (project: INTER-TRANSFER LTT17033) from the
Ministry of Education, Youth and Sports of the Czech Republic, Lisa Rew by the National Institute of Food and Agriculture, U.S.
Department of Agriculture Hatch MONB00363, Tim Seipel and Christian Larson by a grant from the United States National
Institute of Food and Agriculture grant 2017-70006-27272, Nina Buchmann by the SNF (projects M4P 40FA40_154245, ICOS-
CH 20FI21_148992, 20FI20_173691, InnoFarm 407340_172433) and the EU (SUPER-G contract no. 774124) for the Swiss
FluxNet, Mana Gharun by the SNF project ICOS-CH Phase 2 20Fl20_173691, Sanne Govaert by the Research Foundation
Flanders (FWO) (project G0H1517N Pieter De Frenne, Camille Meeussen. and Pieter Van Gansbeke by the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting Grant
FORMICA 757833), Olivier Roupsard by EU-LEAP-Agri (RAMSES II), Agropolis and Total Foundation (DSCATT), CGIAR
(GLDC) and EU-DESIRA (CASSECS), Zuzana Sitková by the Slovak Research and Development Agency under the project
No. APVV-16-0325 and project ITMS 26220220066 co-funded by the ERDF, Brett Ryan Scheffers by National Geographic
Society (grant no. 9480-14 and WW-240R-17), James D. M. Speed by the Research Council of Norway (262064), William D.
Pearse and the Pearse Lab by National Science Foundation ABId1759965, NSF EFd1802605 and United States Department of
Agriculture Forest Service agreement 18dCSd11046000d041, Isla H. Myers-Smith by the UK Natural Environmental Research
Council ShrubTundra Project NE/M016323/1, Andrew D Thomas by a Leverhulme Trust Research Fellowship under
Government of Botswana permit EWT8/ 36/4 VIII(4), Shengwei Zong by National Natural Science Foundation of China (No.
41971124), Roman Plichta by the post-doc project 7.3 of Institutional plan of Mendel University in Brno 2019−2020, František
Máliš by the Slovak Research and Development Agency project APVV-15-0270, Filip Hrbacek and Kamil Laska by the projects
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LM2015078 and CZ.02.01/0.0/0.0/16_013/0001708 of Ministry of Youth and Sports of the Czech Republic, T-M. Ursu was
supported by the Ministry of Research and Innovation through Projects for Excellence Financing in RDI: Contract no. 22
PFE/2018 and PN2019-2022/19270201 – Ctr. 25N BIODIVERS 3-BIOSERV and Andrej Varlagin by RFBR project number 19-
04-01234-a. Lore T. Verryckt is funded by a PhD fellowship from the Research Foundation Flanders (FWO) and acknowledges
support from the European Research Council Synergy Grant; ERC-2562013-SyG-610028 IMBALANCE-P and Pallieter De
Smedt holds a postdoctoral fellowship of the Research Foundation-Flanders (FWO) and The Kreinitz Experiment is a
cooperative research project initiated by the Helmholtz Centre for Environmental Research - UFZ. We also acknowledge
project 18-74-10048 from the Russian Science Foundation, the Dirección General de Cambio Climático del Gobierno de
Aragón, the Ordesa y Monte Perdido National Park and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y
León, the National Swiss Fund for research (SNSF, project “Lif3web”, n°162604).
4Y(U(l)
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References
18'I"V)&'4M'&0'34,-I%
"#<8
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18'."9'38'@1'34,-/"
""*G!1
#M%I"313'#
1+ 8$'9+!).C'>).1'&!"0,-$'
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+2#.(5'#
1"4/'%"4'M49/'@8I,-$"+
"+
"+")!+, -#8
+"41'#
1"4/'""31'="0L,-$"*
+#
3M23'#
1"4/'""31'="0L,-""
G("!
%#@+"+15'#
1"4/'@8I,-=,-
,-"%
"+,)-%#<8
32'#
1"4/'@8I,-4'"'"
+GS
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176'#
/8'&/'E"1'&4L'/I,-.'
G."
")#M4216'#
/8'EI8'4<49'.18,-."!"
!"+W
@+"+20'#
/<'1/'/8'/1'9=>'&9'&I1'
04I'0:'0?1&'38'4<49'4"9'
449'L"XI'.&9'.18'C!='@"
>0,-1%
%#1%MI"58'#
/"&'98'>")L'38'8Y 1/'&).4'
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M0'4<.'3%>'4"8'$3'@"='@1'
&8'>@'.+8,-@++%#
J;%2'#
1'04'4"J'&.'>E'JJ,-4
"G"
W=/"7'#
".%,-,-.MI13#,
".%-#
N44'/>'//,-3'
6#$;%10'#
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... These models determine a species' environmental niche, by relating abiotic conditions, often climate, to species occurrences in space (Araújo & Guisan, 2006;Guisan & Thuiller, 2005), and thus make inferences on whether a location is suitable for a species under present, past, and/or future climatic conditions (Kearney & Porter, 2004. SDMs are typically constructed using climate data interpolated from measurements obtained by weather stations (Lembrechts et al., 2018(Lembrechts et al., , 2020 and then projected into the future using projections of the same climate variables. However, weather stations record air temperatures inside well-ventilated protective shields placed 2 m above the ground in open habitats, at locations carefully selected to be unaffected by local microclimatic influence (Bramer et al., 2018;. ...
... Near-ground leaf temperature data have typically been unavailable at regional scales. However, recent methodological development in the field of microclimatology, as well as the recent SoilTemp database release (see : Lembrechts et al., 2020), makes modelling of temperature conditions experienced by organisms possible, without the need to deploy temperature loggers (e.g. Bennie et al., 2013;Maclean, 2019;Maclean et al., 2017 . ...
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Aim Species distribution models (SDMs) have been used widely to predict the responses of species to climate change. However, the climate data used to drive these models typically represents ambient air temperatures, derived from measurements taken 1–2 m above the ground. Most plant species live near the ground where temperatures can differ significantly, owing to the effects of solar radiation and reduced wind speed. Here, we investigate differences in spatio‐temporal patterns in near‐ground leaf and ambient air temperatures and the implications this has on projected changes in species richness of a suite of Fynbos plant species. Location Fynbos Biome, South Africa. Methods For each individual plant species (n = 83), we constructed two types of SDMs: one using ambient air temperatures and one using near‐ground leaf temperatures. Each of these models was fitted to species occurrence data for a recent time period and projected backwards into the past. Species richness projections for both time periods were then constructed using binarized projections. Results We found that the impact of climate change on species richness – both the degree of suitable climate lost from the historical range and gained outside of the historical range – was greater using SDMs built with near‐ground leaf temperatures. Independent validation of the hindcast projections revealed near‐ground SDMs to be more accurate. Main Conclusions Our study suggests that SDMs constructed using ambient air temperatures are likely overestimating the breadth of the species’ occupied thermal niche, thus underestimating the climate change‐driven risk to species where near‐ground leaf and ambient air temperatures are particularly decoupled from one another. Additionally, ambient air SDMs may be underestimating the ex‐situ refugial potential of inland mountains. Ambient air temperatures should not be considered an effective surrogate for investigating climate change impacts on species living near the ground.
... In many cases, this has become a very inclusive initiative with open calls for standardized data contribution, participation, co-authorship of subsequent products, as well as data storage and sharing (e.g. Maestre & Eisenhauer, 2019, Smith et al. 2019, Lembrechts et al. 2020, Ochoa-Hueso et al. 2020, Guerra et al. 2021b, Potapov et al. 2022a. With these numerous activities on the way, there is a special obligation to be globally representative and inclusive (Maestre & Eisenhauer 2019). ...
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Understanding global biodiversity change, its drivers, and the ecosystem consequences requires a better appreciation of both the factors that shape soil macrofauna communities and the ecosystem effects of these organisms. The project "sOilFauna" was funded by the synthesis center sDiv (Germany) to address this major gap by forming a community of soil ecologists, identifying the most pressing research questions and hypotheses, as well as conducting a series of workshops to foster the global synthesis and hypothesis testing of soil macrofauna. The overarching goal is to analyze the most comprehensive soil macrofauna database-the MACROFAUNA database-which collates abundance data of 17 soil invertebrate groups assessed with a standardized method at 7180 sites around the world, and seeks to foster the collection of future data. In a recent kick-off workshop in May 2022, the first research priorities and collaboration guidelines were determined. Here, we summarize the main outcomes of this workshop and highlight the benefits of creating an open global community of soil ecologists providing standardized soil macrofauna data for future research, evaluation of ecosystem health, and nature protection. 94 (2) · August 2022
... 10. SoilTemp (Lembrechts et al., 2020). Data are available at https://soiltemp.weebly.com/ ...
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In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.
... A final limitation is that our weather stations were located above the plant canopy, and thus were unable to capture below-canopy microclimatic differences. Future research projects involved in collecting data on meteorological parameters can benefit from the installation of microclimate sensors below the canopy that are now readily available at competitive costs (Lembrechts et al., 2020). ...
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Over the last century, peatlands have undergone severe degradation. Nevertheless, the restoration of drained peatlands has gained much importance over the last decades. Hydrological processes are closely linked to soil properties and as such investigations of both water dynamics and soil properties are vital. The specific objectives are to evaluate (1) how long-term rewetting of drained fens alters the response of the water table to precipitation (2) to what extent rewetting changes how meteorological factors drive water table dynamics (3) whether microtopography controls peat properties.
... Weather stations above the upper forest line in the Central Andes are scarce, limiting understanding of the climate in these vulnerable regions. At the same time, we should also aim to monitor microclimatic variations at local scales, such as initiated by the SoilTemp network using soil temperature sensors (Lembrechts et al., 2020). ...
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Aim: Climate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes.
... EuForPlant can be readily linked to other large vegetation-plot databases such as EVA (Chytrý et al., 2016) and sPlot (Bruelheide et al., 2019;Sabatini et al., 2021), to plant trait data sets such as TRY (Kattge et al., 2020) and ClimPlant (Vangansbeke et al., 2021), as well as to climate databases such as CHELSA (Karger et al., 2017) and SoilTemp (Lembrechts et al., 2020). The links between these da- ...
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When evaluating forests in terms of their biodiversity, distinctiveness and naturalness, the affinity of the constituent species to forests is a crucial parameter. Here we ask to what extent are vascular plant species associated with forests, and does species’ affinity to forests vary between European regions? Temperate and boreal forest biome of Northwestern and Central Europe. We compiled EuForPlant, a new extensive list of forest vascular plant species in 24 regions spread across 13 European countries using vegetation databases and expert knowledge. Species were region‐specifically classified into four categories reflecting the degree of their affinity to forest habitats: 1.1 – species of forest interiors, 1.2 – species of forest edges and forest openings, 2.1 – species which can be found in forest as well as open vegetation, and 2.2 – species which can be found partly in forest, but mainly in open vegetation. An additional “O” category was distinguished, covering species typical for non‐forest vegetation. EuForPlant comprises 1726 species, including 1437 herb layer species, 159 shrubs, 107 trees, 19 lianas and four epiphytic parasites. Across regions, generalist forest species (with 450 and 777 species classified as 2.1 and 2.2, respectively) significantly outnumbered specialist forest species (with 250 and 137 species classified as 1.1 and 1.2, respectively). Even though the degree of shifting between the categories of forest affinity among‐regions was relatively low (on average, 17.5 %), about one third of the forest species (especially 1.2 and 2.2) swapped categories in at least one of the study regions. The proposed list can be widely used in vegetation science and global change ecology related to forests’ biodiversity and community dynamics. Shifting of forest affinity among regions emphasises the importance of a continental‐scale forest plant species list with regional specificity.
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Alien plant species invasion depends on biotic and abiotic conditions that can represent environmental barriers as compared to their native range conditions. Specifically, little is known about how alien plant species distribute along the urban-to-rural gradients based on their native climatic conditions and how environmental conditions along these gradients could influence intraspecific trait variation. We studied the distribution of eight woody alien species from contrasted native range climates along urban-to-rural gradients in European areas with a temperate climate (hereafter termed oceanic Europe). During two consecutive summers and in the Belgian part of oceanic Europe, we then measured their intraspecific trait variation using the nitrogen balance index (NBI), chlorophyll content, flavonols index, specific leaf area (SLA) and internode space. Urban-to-rural gradients were characterized by a system of local climate zones (LCZ), the percentage of artificially sealed surfaces (urbanity) and the sky view factor (SVF). We found that the distribution of studied species in the LCZ classes was highly dependent on the climate of their native range, with species from warm climates occurring more in the most urban areas while the ones from cool climates preferred the more rural or natural areas. However, their intraspecific trait variation was not related to the LCZ class in which they grew, nor to their native climate. Instead, we found a surprisingly consistent effect of shielded environments (low SVF) along the entire urban-to-rural gradient on leaf and development traits. Such environments induced a lower leaf flavonols index and higher NBI and SLA, suggesting a shade response and possibly lower heat and drought stress. Our results show that although woody alien plant species from warmer or cooler native climates distributed differently along the urbanization gradient in oceanic Europe, they did not show contrasted intraspecific trait variation. Nevertheless, our findings highlight that even if the woody alien plant species from cooler native ranges are currently more present in the most natural areas, special attention should be paid to woody alien plant species from warmer native ranges that are yet restricted to the most urban areas and could potentially have severe impacts in the future when the barriers to their spread weaken with climate change.
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In the era of anthropocene, global warming tends to alter the distribution range of the plant species. Highly fragile to such changes are the species that are endemic, inhabit higher elevations and show narrow distribution ranges. Predicting and plotting the appropriate suitable habitats and keeping knowledge of how climate change will affect future distribution become imperative for designing effective conservation strategies. In the current study we have used BIOMOD ensemble forecasting to study the current and predict the future potential distribution of Dactylorhiza hatagirea and Rheum webbianum and describe their niche dynamics in Himalayan biodiversity hotspots under climate change scenarios using ecospat R package. Results reveal sufficient internal evaluation metrics with area under curve (AUC) and true skill statistic (TSS) values greater than 0.8 i.e. 0.93 and 0.98 and 0.82 and 0.90 for D. hatageria and R. webbianum respectively, which signifies robustness of the model. Among different bioclimatic variables, bio_1, bio_3, bio_8, bio_14 and bio_15 were the most influential, showing greater impact on the potential distribution of these plant species. Range change analysis showed that both the studied species will show significant contraction of their suitable habitats under future climatic scenarios. Representative Concentration Pathway (RCP) 8.5 for the year 2070, indicate that the suitable habitats could be reduced by about 51.41% and 70.57% for D. hatagirea and R. webbianum respectively. The results of the niche comparisons between the current and future climatic scenarios showed moderate level of niche overlap for all the pairs with D. hatageria showing 61% overlap for current vs. RCP4.5 2050 and R. webbianum reflects 68% overlap for current vs. RCP4.5 2050. Furthermore, the PCA analysis revealed that climatic conditions for both the species vary significantly between current and future scenarios. The similarity and equivalence test showed that the niche between present and future climate change scenarios is comparable but not identical. From the current study we concluded that the influence of climate change on the habitat distribution of these plant species in the Himalayan biodiversity hotspots can be considered very severe. Drastic reduction in overall habitat suitability poses a high risk of species extinction and thereby threatens to alter the functions and services of these fragile ecosystems. Present results can be used by conservationists for mitigating the biodiversity decline and exploring undocumented populations on one hand and by policymakers in implementing the policy of conservation of species by launching species recovery programmes in future on the other. The outcomes of this study can contribute substantially to understand the consequences of climate change in the Himalayan biodiversity hotspots.
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Forest canopies can buffer seedlings from extreme climate conditions. Yet, how disturbed forest canopies influence microclimate is not well understood, despite the important implications of microclimate for seedling establishment and post-disturbance successional trajectories. Better understanding of the relationship between a forest canopy and sub-canopy temperature and moisture conditions requires easily acquired and continuous forest canopy data, which is increasingly available due to new technology. Here, we measured canopy height using a remotely piloted aircraft (RPA) and monitored microclimate with low-cost temperature and soil moisture sensors in a sub-boreal forest impacted by fires of variable severity. We used regression models to investigate how differences in canopy height influenced microclimate variables. Mean growing season temperatures at -8 cm (soil), 0 cm (surface), and 15 cm (near-surface) relative to the ground surface were higher under shorter more disturbed canopies. Soil temperature was most sensitive to canopy height differences: linear models for the observed data range predicted a 2.0 °C increase in mean growing season soil temperature with every 10 m decrease in canopy height. We observed a weak negative relationship between canopy height and mean growing season soil moisture. We found that canopy height summarized at moderate resolution (15 m) better explained differences in temperature in our disturbed landscape. This work informs future methods to produce gridded microclimate datasets and outlines the impact of disturbed forest structure on microclimate variables. Our results show that the characteristics of the forest canopy remaining after a burn impact microclimates, which has important implications for post-fire ecosystems.
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Soils harbor a substantial fraction of the world’s biodiversity, contributing to many crucial ecosystem functions. It is thus essential to identify general macroecological patterns related to the distribution and functioning of soil organisms to support their conservation and consideration by governance. These macroecological analyses need to represent the diversity of environmental conditions that can be found worldwide. Here we identify and characterize existing environmental gaps in soil taxa and ecosystem functioning data across soil macroecological studies and 17,186 sampling sites across the globe. These data gaps include important spatial, environmental, taxonomic, and functional gaps, and an almost complete absence of temporally explicit data. We also identify the limitations of soil macroecological studies to explore general patterns in soil biodiversity-ecosystem functioning relationships, with only 0.3% of all sampling sites having both information about biodiversity and function, although with different taxonomic groups and functions at each site. Based on this information, we provide clear priorities to support and expand soil macroecological research. Soil organism biodiversity contributes to ecosystem function, but biodiversity and function have not been equivalently studied across the globe. Here the authors identify locations, environment types, and taxonomic groups for which there is currently a lack of biodiversity and ecosystem function data in the existing literature.
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Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait– nvironmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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