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opencc-by-4.0Jun 2024View details →

Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P. "Replication Data for: Melanin-based color variation in response to changing climates in snakes"

<p>This repository contains the data used to produce the manuscrpit "Melanin-based color variation in response to changing climates in snakes" by Goldenberg, J., Bisschop, K., Bruni, G., Di Nicola, M. R., Banfi, F., Faraone, F. P.</p> <p>Article DOI: 10.1002/ece3.11627</p> <p>Journal: Ecology and Evolution</p>

opencc-by-4.0Aug 2024View details →

Variations in Rainfall Structure of Western North Pacific Landfalling Tropical Cyclones in Warming Climates

<div>This dataset contains the data used in the analyses for the paper titled <em>'Variations in Rainfall Structure of Western North Pacific Landfalling Tropical Cyclones in Warming Climates',</em> published in <em>Earth's Future.</em> The paper is authored by Thao Linh Tran, Elizabeth A. Ritchie, Sarah E. Perkins-Kirkpatrick, Hai Bui, and Thang M. Luong. Descriptions of the variables included in the data files are provided below.</div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_CMIP6_multimodel_mean_SST_7states.nc</div> <div><xarray.Dataset></div> <div>Dimensions:    (state: 7, month: 12, lat: 181, lon: 360)</div> <div>Coordinates:</div> <div>  * state        (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div>  * month      (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</div> <div>  * lat        (lat) int64 -90 -89 -88 -87 -86 -85 -84 ... 84 85 86 87 88 89 90</div> <div>  * lon        (lon) int64 0 1 2 3 4 5 6 7 8 ... 352 353 354 355 356 357 358 359</div> <div>Data variables:</div> <div>    SST_cmip6  (state, month, lat, lon) float64 0.0 0.0 0.0 ... -1.699 -1.699</div> <div> SST_cmip6           (state, month, lat, lon)  CMIP6 multimodel mean of sea surface temperature in each month in 7 climate states (deg Celcius)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_AllTCs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:      (state: 7, radius: 20, nx: 61, ny: 61)</div> <div>Coordinates:</div> <div>  * state        (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div>  * radius       (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>  * nx           (nx) int64 0 18 36 54 72 90 ... 990 1008 1026 1044 1062 1080</div> <div>  * ny           (ny) int64 0 18 36 54 72 90 ... 990 1008 1026 1044 1062 1080</div> <div>Data variables:</div> <div>    Rain_circle  (state, radius) float64 22.66 24.94 26.54 ... 8.147 7.561 7.041</div> <div>    Rain_ring    (state, radius) float64 22.66 25.8 27.49 ... 2.689 2.436 2.231</div> <div>    PW_ring      (state, radius) float64 72.64 73.04 72.37 ... 69.81 68.99 68.27</div> <div>    MFC_ring     (state, radius) float64 0.01343 0.01539 ... 0.004405 0.004258</div> <div>    MFC_hoz      (state, nx, ny) float64 0.0002184 0.0003068 ... 0.000167</div> <div> Rain_circle         (state, radius)           Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring           (state, radius)           Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring             (state, radius)           Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring            (state, radius)           Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> MFC_hoz             (state, nx, ny)           Integrated moisture flux convergence in the boundary layer in 7 climate states (kg m-2 s-1), TC center is located at nx=ny=540 km</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_TSs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:      (state: 7, radius: 20)</div> <div>Coordinates:</div> <div>  * state        (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div>  * radius       (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div>    Rain_circle  (state, radius) float64 17.86 16.98 15.74 ... 5.884 5.533 5.215</div> <div>    Rain_ring    (state, radius) float64 17.86 16.66 14.92 ... 2.704 2.461 2.28</div> <div>    PW_ring      (state, radius) float64 71.08 70.79 70.14 ... 70.67 69.9 69.22</div> <div>    MFC_ring     (state, radius) float64 0.00885 0.009635 ... 0.004318 0.004183</div> <div> Rain_circle         (state, radius)           Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring           (state, radius)           Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring             (state, radius)           Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring            (state, radius)           Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_TYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:      (state: 7, radius: 20)</div> <div>Coordinates:</div> <div>  * state        (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div>  * radius       (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div>    Rain_circle  (state, radius) float64 21.55 23.26 24.55 ... 8.029 7.459 6.954</div> <div>    Rain_ring    (state, radius) float64 21.55 23.89 25.27 ... 2.73 2.473 2.273</div> <div>    PW_ring      (state, radius) float64 72.7 72.73 71.91 ... 69.23 68.42 67.71</div> <div>    MFC_ring     (state, radius) float64 0.01288 0.01458 ... 0.004363 0.004229</div> <div> Rain_circle         (state, radius)           Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring           (state, radius)           Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring             (state, radius)           Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring            (state, radius)           Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_rain_PW_MFC_2methods_7states_STYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:      (state: 7, radius: 20)</div> <div>Coordinates:</div> <div>  * state        (state) <U9 'CTR' 'med2030s' 'med2050s''med2090s' 'high2030s' 'high2050s' 'high2090s'</div> <div>  * radius       (radius) int64 25 50 75 100 125 150 ... 375 400 425 450 475 500</div> <div>Data variables:</div> <div>    Rain_circle  (state, radius) float64 31.83 39.28 44.65 ... 9.905 9.124 8.436</div> <div>    Rain_ring    (state, radius) float64 31.83 42.18 48.07 ... 2.552 2.304 2.071</div> <div>    PW_ring      (state, radius) float64 73.54 76.26 76.33 ... 71.1 70.22 69.44</div> <div>    MFC_ring     (state, radius) float64 0.01956 0.02383 ... 0.004592 0.004396</div> <div> Rain_circle         (state, radius)           Areal averaged TC rainfall within radii in 7 climate states calculated by the circle-average method (mm)</div> <div> Rain_ring           (state, radius)           Average TC rainfall within radial bands in 7 climate states calculated by the ring-average method (mm)</div> <div> PW_ring             (state, radius)           Average precipitable water within radial bands in 7 climate states calculated by the ring-average method (kg m-2)</div> <div> MFC_ring            (state, radius)           Average moisture flux convergence within radial bands in 7 climate states calculated by the ring-average method (g m-2 s-1)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_AllCats.nc</div> <div><xarray.Dataset></div> <div>Dimensions:            (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div>  * state              (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div>  * level              (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div>  * radius             (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div>  * tc                 (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/16)</div> <div>    cape_azimean       (state, level, radius) object 1890.2525300742138 ... nan</div> <div>    Ta_azimean         (state, level, radius) object 27.644140005330815 ... -...</div> <div>    Qv_azimean         (state, level, radius) object 0.020813525216797777 ......</div> <div>    Qtti_azimean       (state, level, radius) object 5.566690902988369e-08 .....</div> <div>    ttip               (state, tc) object 7997552.5 20351432.0 ... 8139392.0</div> <div>    SST_levmean        (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div>    ...                 ...</div> <div>    etheta_azimean     (state, level, radius) object 367.2280704092775 ... 40...</div> <div>    updraft_azimean    (state, level, radius) object 0.08083254853909848 ... ...</div> <div>    downdraft_azimean  (state, level, radius) object -0.019081951314966872 .....</div> <div>    mfc_azimean        (state, level, radius) object 0.00029923238185347746 ....</div> <div>    ss_azimean         (state, radius) object 48.3689839795465 ... 47.3942474...</div> <div>    vms_azimean        (state, radius) object 371.7967310109822 ... 364.55313...</div> <div> cape_azimean        (state, level, radius)    Azimuthally averaged convective available potential energy (CAPE) at radii up to 500 km from the TC center (J kg-1)</div> <div> Ta_azimean          (state, level, radius)    Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean          (state, level, radius)    Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean        (state, level, radius)    Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> ttip                (state, tc)               Total cloud ice path in 3 future states (kg m-2)</div> <div> SST_levmean         (state, tc)               Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean          (state, tc, level)        Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean          (state, tc, level)        Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean          (state, level, radius)    Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean          (state, level, radius)    Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean      (state, level, radius)    Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean     (state, level, radius)    Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean   (state, level, radius)    Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean         (state, level, radius)    Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean          (state, radius)           Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean         (state, radius)           Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_TSs.nc</div> <div>Data variables: (12/14)</div> <div>    Ta_azimean         (state, level, radius) object 27.457257781178235 ... -...</div> <div>    Qv_azimean         (state, level, radius) object 0.020375530778800895 ......</div> <div>    Qtti_azimean       (state, level, radius) object 5.639538301516413e-08 .....</div> <div>    SST_levmean        (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div>    Ta_levmean         (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div>    Qv_levmean         (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div>    ...                 ...</div> <div>    etheta_azimean     (state, level, radius) object 360.9125096202158 ... 40...</div> <div>    updraft_azimean    (state, level, radius) object 0.07054015061042855 ... ...</div> <div>    downdraft_azimean  (state, level, radius) object -0.018065747224214473 .....</div> <div>    mfc_azimean        (state, level, radius) object 0.00020737332807555921 ....</div> <div>    ss_azimean         (state, radius) object 46.39805195710858 ... 47.228965...</div> <div>    vms_azimean        (state, radius) object 361.6881448325401 ... 365.20869...</div> <div> Ta_azimean          (state, level, radius)    Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean          (state, level, radius)    Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean        (state, level, radius)    Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean         (state, tc)               Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean          (state, tc, level)        Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean          (state, tc, level)        Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean          (state, level, radius)    Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean          (state, level, radius)    Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean      (state, level, radius)    Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean     (state, level, radius)    Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean   (state, level, radius)    Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean         (state, level, radius)    Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean          (state, radius)           Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean         (state, radius)           Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_TYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:            (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div>  * state              (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div>  * level              (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div>  * radius             (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div>  * tc                 (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/14)</div> <div>    Ta_azimean         (state, level, radius) object 27.515624558698093 ... -...</div> <div>    Qv_azimean         (state, level, radius) object 0.020579266032935868 ......</div> <div>    Qtti_azimean       (state, level, radius) object 5.815300849925888e-08 .....</div> <div>    SST_levmean        (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div>    Ta_levmean         (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div>    Qv_levmean         (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div>    ...                 ...</div> <div>    etheta_azimean     (state, level, radius) object 366.45252777008426 ... 4...</div> <div>    updraft_azimean    (state, level, radius) object 0.08167928852011325 ... ...</div> <div>    downdraft_azimean  (state, level, radius) object -0.017788485333689888 .....</div> <div>    mfc_azimean        (state, level, radius) object 0.00028844761894039205 ....</div> <div>    ss_azimean         (state, radius) object 47.93880505618239 ... 47.189455...</div> <div>    vms_azimean        (state, radius) object 369.8002440991976 ... 364.41387...</div> <div> Ta_azimean          (state, level, radius)    Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean          (state, level, radius)    Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean        (state, level, radius)    Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean         (state, tc)               Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean          (state, tc, level)        Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean          (state, tc, level)        Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean          (state, level, radius)    Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean          (state, level, radius)    Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean      (state, level, radius)    Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean     (state, level, radius)    Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean   (state, level, radius)    Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean         (state, level, radius)    Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean          (state, radius)           Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean         (state, radius)           Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------</div> <div>Data_Azimean_levelmean_thermodynamic_dynamic_variables_3states_STYs.nc</div> <div><xarray.Dataset></div> <div>Dimensions:            (state: 3, level: 38, radius: 252, tc: 117)</div> <div>Coordinates:</div> <div>  * state              (state) <U9 'CTR' 'med2090s' 'high2090s'</div> <div>  * level              (level) int64 25 83 156 250 ... 16300 17000 17550 18100</div> <div>  * radius             (radius) int64 0 2 4 6 8 10 ... 492 494 496 498 500 502</div> <div>  * tc                 (tc) int64 0 1 2 3 4 5 6 ... 110 111 112 113 114 115 116</div> <div>Data variables: (12/14)</div> <div>    Ta_azimean         (state, level, radius) object 28.677196671837233 ... -...</div> <div>    Qv_azimean         (state, level, radius) object 0.022842606379269957 ......</div> <div>    Qtti_azimean       (state, level, radius) object 4.0012758302765466e-08 ....</div> <div>    SST_levmean        (state, tc) float64 29.44 29.49 30.23 ... 31.55 31.01</div> <div>    Ta_levmean         (state, tc, level) float32 28.13 27.51 ... -83.1 -83.41</div> <div>    Qv_levmean         (state, tc, level) float32 20.09 19.83 ... 0.00155</div> <div>    ...                 ...</div> <div>    etheta_azimean     (state, level, radius) object 376.0869964585824 ... 40...</div> <div>    updraft_azimean    (state, level, radius) object 0.0844076324643325 ... 0...</div> <div>    downdraft_azimean  (state, level, radius) object -0.02603523353916364 ......</div> <div>    mfc_azimean        (state, level, radius) object 0.0004255960070462955 .....</div> <div>    ss_azimean         (state, radius) object 52.07136400532476 ... 48.142035...</div> <div>    vms_azimean        (state, radius) object 389.7398573564309 ... 364.59206...</div> <div> Ta_azimean          (state, level, radius)    Azimuthally averaged air temperature at radii up to 500 km from the TC center (deg Celcius)</div> <div> Qv_azimean          (state, level, radius)    Azimuthally averaged water vapor mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> Qtti_azimean        (state, level, radius)    Azimuthally averaged total ice-related (ice|graupel|snow) mixing ratio at radii up to 500 km from the TC center (g kg-1)</div> <div> SST_levmean         (state, tc)               Mean SST within 500 km of the TC center (deg Celcius)</div> <div> Ta_levmean          (state, tc, level)        Level averaged Ta within 500 km of the TC center at each level (deg Celcius)</div> <div> Qv_levmean          (state, tc, level)        Level averaged Qv within 500 km of the TC center at each level (g kg-1)</div> <div> vt_azimean          (state, level, radius)    Azimuthally averaged tangential wind at radii up to 500 km from the TC center (kt)</div> <div> vr_azimean          (state, level, radius)    Azimuthally averaged radial wind at radii up to 500 km from the TC center (kt)</div> <div> etheta_azimean      (state, level, radius)    Azimuthally averaged equivalent potential temperature at radii up to 500 km from the TC center (deg Kelvin)</div> <div> updraft_azimean     (state, level, radius)    Azimuthally averaged updraft at radii up to 500 km from the TC center (kt)</div> <div> downdraft_azimean   (state, level, radius)    Azimuthally averaged downdraft at radii up to 500 km from the TC center (kt)</div> <div> mfc_azimean         (state, level, radius)    Azimuthally averaged moisture flux convergence at radii up to 500 km from the TC center (kg m-2 s-1)</div> <div> ss_azimean          (state, radius)           Azimuthally averaged static stability at radii up to 500 km from the TC center (deg Kelvin)</div> <div> vms_azimean         (state, radius)           Azimuthally averaged convective stability at radii up to 500 km from the TC center (MJ m-1 s-2)</div> <div> </div>

opencc-by-4.0Sep 2024View details →

Data for the publication Climate and nutrition drive gut microbiome variation in a fruit-specialist primate

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2021View details →

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

<p>We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the 21<sup>st</sup> century in Europe.</p> <p>Here we present a set of raster files that contain discrete zones of climatic stability and instability, also referred to as hotspots and coldspots. Our analysis of future climatic stability was based on projected changes of nine climate variables selected to proxy key aspects of terrestrial ecosystem dynamics. The underlying climate projection, including different indices, are available at <a href="https://zenodo.org/record/3952159#.YNWN_OgzaUl">https://zenodo.org/record/3952159#.YNWN_OgzaUl</a>. The cumulative change of all nine climate variables, termed Aggregate Climate Change (ACC), is used to identify regions of significantly higher or lower climatic stability (Getis-Ord Gi* p-value < 0.05) across Europe. We carry out this assessment at the continental and regional scales. While the continental assessment reports the large-scale latitudinal and orographic patterns of climatic stability, the regional assessment explores the residual ACC variation that remained after extracting the large-scale continental trend. The raster files present climatic features that were identified to persist in their location throughout the 21<sup>st</sup> century (i.e., in periods 2021-2040, 2041-2060, and 2061-2100) and were supported by a majority of climate projections considered in this study. Specifically, we considered five GCM-RCM pairs driven by two RCP scenarios, RCP4.5 and RCP8.5. The targeted areas of climatic stability were required to be confirmed by four out of five climate models nested within each RCP, at least.</p> <p>The used naming convention is as follows:</p> <ul> <li>Scale – Global, Regional</li> <li>Type of the zone – HotSpot (zone of significantly low climatic stability), ColdSpot (zone of significantly high climatic stability)</li> <li>Driving RCP scenario – RCP45, RCP85</li> </ul> <p>Raster files with RCP indicated in their name refer to either RCP4.5 or RCP8.5. These files contain two values referring to the presence (1) or absence (0) of climatic stability areas:</p> <p>Global_HotSpots _RCP45</p> <p>Global_ColdSpots _RCP45</p> <p>Regional_HotSpots _RCP45</p> <p>Regional_ColdSpots _RCP45</p> <p>Global_HotSpots _RCP85</p> <p>Global_ColdSpots _RCP85</p> <p>Regional_HotSpots _RCP85</p> <p>Regional_ColdSpots _RCP85</p> <p>Raster files without RCP code in their name contain combinations of both RCPs. The cell values indicate absence (0), presence in RCP4.5 OR RCP8.5 (1), or presence in RCP4.5 AND RCP8.5 (2) of climatic stability areas:</p> <p>Global_HotSpots</p> <p>Global_ColdSpots</p> <p>Regional_HotSpots</p> <p>Regional_ColdSpots</p> <p>The format of raster files is tiff. All data are in the GCS_WGS_1984 geographic coordinate system.</p>

opencc-zeroDec 2018View details →

Data from: Regional variation in interior Alaskan boreal forests is driven by fire disturbance, topography, and climate

High latitude regions are warming rapidly with important ecological and societal consequences. Utilizing two landscape-scale datasets from interior Alaska, we compared patterns in forest structure in two regions with sharply differing fire disturbance, topography, and climate. Our goal was to evaluate a set of hypotheses concerning possible warming-driven changes in forest structure suggested by recent literature. We found essentially consistent habitat associations for the tree flora across two disparate study areas concomitant with considerable differences in observed patterns of forest structure and composition. Our results confirmed expected increases in broadleaved species occupancy and abundance in the warmer, more fire-affected study region along with considerably higher tree occupancy and abundance in high elevation areas there. However, contrary to our predictions, we found no evidence of expected reductions in conifer occupancy or increases in non-fire related tree mortality. Instead, both individual and combined tree species occupancy, density, abundance, and richness were considerably higher in the warmer, more fire-influenced region, except in the warmest, driest areas (steep and south-facing slopes at low elevation). Our comparison of two landscape-scale datasets suggests that changes in tree distribution and forest structure in interior Alaska will proceed unevenly, governed by a mosaic of site-dependent influences wherein forest community composition and species dominance will shift along different trajectories and at different rates according to variation in underlying landscape attributes. Although there were clear differences in forest structure between the two areas that were likely attributable to differences in growing season warmth and fire disturbance, we found scant support for the concept of an incipient, ongoing biome shift in interior Alaska resulting from impending diminution of boreal forest cover over the short to medium term. Indeed, we suggest that (depending on severity of disturbance dynamics and the rapidity of future warming) cooler areas of interior Alaska's forest may reasonably be expected to sustain marginal increases in forest cover with additional warming, at least in certain topographic positions (such as poorly drained basins and cool treeline sites) and/or geographic regions, prior to any landscape-scale diminution of forest cover due to warming.

FIGURE 2 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients

FIGURE 2. Plots of PC1 × PC2 and PC1 × PC3 from principal components analysis (PCA) of all specimens in the study. ANFA: P. anfamensis; JUJ: P. jujuyensis; LILL: P. lilloi; PARV: P. parviceps; SCAB: P. scaberula.

FIGURE 1 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients

FIGURE 1. DIVA-GIS map of environmental variables and 150 collection sites of Poa specimens from the Andes in South American. A. Elevation. B. Annual mean precipitation. C. Annual mean temperature. D. Annual maximum temperature. E. Annual minimum temperature. Symbols for the Poa species are described in E.

FIGURE 4 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients

FIGURE 4. Box plots representing the mean, median, interquartile range, adjacent values (lines), and outliers (dots) of quantitative characters in P. lilloi and P. scaberula.

FIGURE 3 in Phenotypical variation and taxonomic correlates of five closely related Andean species of Poa (Poaceae) along geographic and climatic gradients

FIGURE 3. Plot of discriminant analysis (DA) along the first two discriminant axes obtained from all specimens pertaining to a priori defined species. ANFA: P. anfamensis; JUJ: P. jujuyensis; LILL: P. lilloi; PARV: P. parviceps; SCAB: P. scaberula.

opencc-zeroAug 2021View details →

Interactive effects of tree species mixture and climate on foliar and woody trait variation in a widely distributed deciduous tree

<p><span>Despite increasing reports of severe drought and heat impacts on forest ecosystems, c</span>ommunity-level processes, which could potentially modulate tree responses to climatic stress, are rarely accounted for. While numerous studies<span> indicate a positive effect of species diversity on a wide range of ecosystem functions and services, little is known about how species interactions influence tree responses to climatic variability. We quantified the intraspecific variation in 16 leaf and wood physiological, morphological, and anatomical traits in mature beech trees (<i>Fagus sylvatica</i> L.) at six sites located along a climatic gradient in the French Alps. At each site, we studied pure beech and mixed stands with silver fir (<i>Abies alba </i>Mill.) or downy oak (<i>Quercus pubescens </i>Willd.). We tested how functional traits differed between the two species mixtures (pure <i>vs</i>. mixed stands) within each site and along the climatic gradient. We found significant changes in many traits along the climatic gradient </span>as conditions progressively got drier and warmer<span>. Independent of the mixture, reduced leaf-level CO<sub>2</sub> assimilation, stomatal size, and thicker leaf cuticles, consistent with a more conservative resource use strategy, were found. At the drier sites, higher foliar stable carbon isotopic composition (</span><span>d</span><sup><span>13</span></sup><span>C), thicker mesophyll tissues, and lower specific leaf area (SLA) in pure stands suggests that beech had more acquisitive traits there compared to mixed stands. At the wetter sites, trees in beech-silver fir mixtures had higher chlorophyll concentration, lower </span><span>d</span><sup><span>13</span></sup><span>C, larger xylem vessels, and higher SLA, suggesting a more acquisitive resource use strategy in mixed stands than in pure stands. </span>Our work revealed that species interactions are significant modulators of functional traits, and that they can be just as important drivers of intraspecific trait variation as climatic conditions. <span>We show that downy oak mixtures lead to an adaptive drought response by common beech in dry environments. In contrast, in milder climates, interactions with silver fir seem to increase beech' resource acquisition and productivity. These findings highlight a strong context-dependency and imply that incorporating local interspecific interactions in research on climate impacts could improve our understanding and predictions of forest dynamics.</span></p>

opencc-zeroNov 2022View details →

Bark content variations and the production of Populus deltoides as a source of bioenergy under temperate climate conditions

<div> <div> <p>Bark biomass as an energy source has a high economic value. Bark content variations and production helps recognize the potential of this bioenergy source spatially before harvesting. The percentage of fresh and dry bark in Populus deltoides grown under a monoculture system was examined in the temperate region of northern Iran. Diameter at breast height (DBH) and total height data were analyzed based on an initial inventory. Ten sample trees were felled, separated into 2 m-segments, and weighted in the field. A 5-cm-thick disc from each segment was extracted for determining fresh and dry bark percentages. These were statistically significantly different in disc diameter classes and decreased with increasing disc diameters. Bark percentage of the disc classes ranged from 21.8 to 24.4% in small-sized diameters to 8.1‒9.3% in large-sized diameters. The differences between fresh and dry bark percentages depended on water content variations. Allometric power equations were fitted to data of fresh and dry bark percentages and disc diameters as well as DBH. The values of R2 ranged from 0.89 to 0.90. In addition, allometric power equations provided the best fits for relationships between total stem dry biomass, dry bark biomass, and DBH, R2 = 0.986 and 0.979 for the total stem dry biomass and stem dry bark biomass, respectively. The allometric models can be used to estimate bark percentage and bark production of P. deltoides in segments and for the whole stem for a wide range of segment diameters (8‒44 cm) and DBH (15‒45 cm).</p> </div> </div>

opencc-zeroMay 2023View details →

Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition

<ol> <li>Landscape patterns of phenotypic coevolution are determined by variation in the outcome of predator-prey interactions. These outcomes may depend not only on the functional phenotypes that mediate species interactions but also on aspects of the environment that enable encounters between coevolutionary partners.</li> <li>Exploring the relationship between coevolutionary traits and the environment requires extensive sampling across the range of the interaction to determine the relationship between local ecological variation and coevolution.</li> <li>In this study, we synthesized >30 years of data on predator-prey interactions between toxic newts (<em>Taricha</em> <em>granulosa</em>) and their snake predators (<em>Thamnophis</em> <em>sirtalis</em>) to explore the environmental predictors of arms race escalation.</li> <li>We found that geographic variation in phenotypes at the interface of coevolution was best predicted by a combination of community and climatic variation. Coevolutionary phenotypes were greatest in environments with climate favorable for newt-snake overlap. We found prey toxicity was elevated in regions with more predator species, and predator resistance was higher in regions with more prey species.</li> <li>Our results suggest specific environmental conditions reinforce the process of coevolution, signifying the phenotypic outcomes of coevolutionary arms races are sensitive to local ecological contexts that vary across the landscape. </li> </ol>

ExtendedData Fig. 9 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

ExtendedData Fig. 9 | Correlationbetweenseasonalityanddisturbance. At thelocallandscapelevel (a), seasonalityiscalculatedasthestandarddeviationof meanmonthlytemperaturevaluesthroughouttheyearatthelandscapecentroid (n = 31). Highdisturbancemeans 50% of thestudylandscapeareaoverlaps areasofhighnatural (forexamplestorms,glaciers,fires) orAnthropogenic (for exampleforestloss).Boxplotsshowmedian,interquartile range,andwhiskers toextremevalues (outliersaredatapoints>1.5x quartiles).Statisticsarefrom atwo-sided Wilcoxon test.Atthespecieslevel (b), communitymeanvalues (n = 31), arecalculatedusingspecies' distributionalseasonalityanddisturbance scores.Disturbanceiscalculatedastheproportionof thespeciesbreedingrange whichoverlapsareasofhighnatural (forexamplestorms,glaciers,fires) or anthropogenic (forexampleforestloss) disturbance.Seasonalityiscalculated asthestandarddeviationof meanmonthlytemperaturevaluesthroughoutthe year,averagedacrossallgridcellsinthespecies' breedingrange.Statisticsare fromalinearregressionwith Gaussianerrors;purplelineshowsmodelfit;shaded areais 95% confidenceintervals.

ExtendedData Fig. 8 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

ExtendedData Fig. 8 | Relationshipbetweendispersallimitation (nHWI) anddiet. Datashownfor (a) 276 birdspeciessampledacross 18 temperate studylandscapes,and (b) 817 birdspeciessampledacross 13 tropicalstudy landscapes.Dietaryclasseswith <5 specieswereremovedfromtheanalysis.Diet classificationsarefrom Tobiasand Pigot110. F-statisticand P-valuearecalculated withatwo-way ANOVA.Boxplotsshowmedian,interquartile range,andwhiskers toextremevalues (outliersaredatapoints>1.5x quartiles).

ExtendedData Fig. 7 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

ExtendedData Fig. 7 | Predictorsofdispersallimitationinbirds. Results shownareoutputsof phylogeneticleastsquaresmodelpredictingdispersal limitation (nHWI) acrossallbirdspeciessampled,includinglong-distance migrants (swallowimage,dark bars;n = 1034); onlyresidentspeciesandshort distance/partialmigrants (thrushimage,medium bars;n = 921); orresident speciesonly (pittaimage,palebars;n = 858). Panelspresentthreesetsofmodels withincreasingcomplexity:aunivariatemodelwithsinglepredictor (a,d), and multivariatemodelswithtwo (b,e) andthree (c,f) predictors.Eachpredictor iscalculatedatthespecieslevelbyaveragingacross landscapeswhereeach speciesispresent.Disturbance (red) iscalculatedastheproportionofspecies breedingrangewhichoverlapsareasofhigh natural (e.g. storms,glaciers,fires) oranthropogenic (e.g. forestloss) disturbance.Absolutelatitude (yellow) is calculatedasthecentroidlatitudeof thespeciesbreedingrange.Seasonality (blue) iscalculatedasthestandarddeviationof meanmonthlytemperature valuesthroughouttheyear,averagedacrossallgridcellsinthebreedingrange. a–c, EffectsiZeestimatesaregivenwith 95% confidenceintervals;anegative effectindicatesreduceddispersallimitation (thatisincreased dispersalability). R2 and AICvaluesarecalculatedforfullsamplemodelsonly.d–f, Proportion of independentvariationexplainedbyeachmodelcovariate,calculatedusing hierarchicalpartitioning.

ExtendedData Fig. 6 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

ExtendedData Fig. 6 | Driversoffragmentationsensitivitywithnatural disturbances. Resultsof Bayesianphylogeneticmixedeffectmodelspredicting fragmentationsensitivityfor 1564 birdpopulations (n = 1034 species). Populationswereclassifiedasfragmentationsensitiveiftheywereidentifiedas 'Forest-core' by BIOFRAG. Restrictedanalysisassignedfragmentationsensitivity onlyto 'Forestspecialists' (a); Expandedanalysisassignedfragmentation sensitivitytoboth ' Forestspecialist' and ' Forestassociated' species (b; see Methods).Bayesianposteriordistributionisshownabovetheline;effectsiZe estimateswithcredibleintervals (CI) belowtheline (68%: thickerrorbars; 95%: thinerrorbars).HigheffectsiZesindicateapositiveassociationwith fragmentationsensitivity;loweffectsiZesindicateanegativeassociation. Finchandhawksilhouettesindicatethatbothmodelswererunonacomplete sample. Historicaldisturbanceisabinaryvariable (1/0) calculatedusingnatural disturbance (forexamplefires,storms & glaciation) layersonly.

ExtendedData Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

ExtendedData Fig. 2 | Thelatitudinalgradientinaveragedispersallimitation ofbirdassemblages.Datapoints (colouredbylevelofhistoricaldisturbance) showthecommunitymeanvaluesforavianassemblagessampledat 31 study landscapesmappedin Fig.1. Theoverallgradientisnotexplainedbylandscape disturbancehistory.Absolutelatitudeisthecentroidlatitudeofallsampling pointsineachstudylandscape.Mean dispersallimitationisthenegative (thatis inverse) hand-wingindex (nHWI) averagedacrossallspeciesintheassemblage; nHWIislogarithmicallyscaled (log(1/HWI)) forvisualiZation.Statisticsarefroma linearmodelwith Gaussianerrors;purplelineshowsmodelfit (R2 = 0.44); shaded regionshowsthestandarderrorof theregressioncoefficient.

Fig. 3 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

Fig. 3 | Fragmentationsensitivityincreaseswithdispersallimitationinbird assemblages.a, Variationin fragmentationsensitivity anddispersalability plottedonaconsensusphylogenetictree.Eachbranchrepresentsagenus (n = 441), withdataattipsaveragedacrossfamilies (n = 115) forvisualiZation. Branchcoloursindicatedispersallimitation (leastdispersivespeciesin red); tipcoloursshowtheproportionoffragmentation-sensitivespeciesineach family (expandedanalysis;mostsensitivein yellow).b, Datapoints (coloured bylevelofhistoricaldisturbance) aremeansfor 31 studylandscapes.Foreach assemblage,fragmentationsensitivityisassignedtoforest-corespecies withhighforestdependency (Restrictedanalysis),andmeandispersal limitationisthenHWIaveragedacrossallspecies;nHWIislogarithmically scaled (log(1/HWI)) forvisualiZation.StatisticsarefromageneraliZedlinear modelwithquasi-binomialerrors;purplelineshowsmodelfit (R2 = 0.180); shadedregionshows 95% confidenceintervals.Boxplotsin b showthesame distributionswithmedianvalue,interquartilerangeandwhiskerstoextreme values (outliersaredatapoints>1.5× quartiles).Resultsfortheexpandedsample areshownin ExtendedData Fig. 4.

Fig. 2 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

Fig. 2 | Globalpatternsoflandscapedisturbanceanddispersallimitation. a, Thepresenceofnatural oranthropogenichistoricaldisturbancesrecorded ineachgridcell.Naturaldisturbancepressures (bright red) includemajorfires, stormsandglaciation;theseeventshavetypicallypersisted forlongerperiods oftimeandmaycausecompleteremovalofforestbiota.Anthropogenicforest loss (palered) representsmore recentdisturbancethatoftenalterscomposition oflocalassemblageswithoutcompleteeradication.b, VariationinnHWI averagedacross speciesoccurringineachgridcell,rangingfromlow (blue) to high (red) dispersallimitation.Dispersallimitationdataarecalculatedfrom measurementsof 10,562 birdspecies,logarithmicallyscaledforvisualiZation (log(1/HWI)).Yellowdotsshowstudylandscapes (21 from BIOFRAG;10 from additionalsampling).Gridcellsin a and b are 2.5 arcminutes. c,d, Hypothetical relationships:extinctionfilterspredictthatfragmentationsensitivityis negativelyassociatedwithhistoricaldisturbance (c), whiledispersal-related mechanismspredictthatfragmentationsensitivityispositivelyassociated with dispersallimitation (d).

Fig. 1 in Climate-driven variation in dispersal ability predicts responses to forest fragmentation in birds

Fig. 1 | Hypothesespredictingthedistributionoffragmentation-sensitive species. Thetoppathway (a) illustrateshow ' extinctionfilters' linkedto historicaldisturbances (forexample, fireandanthropogenicforestloss) canbenon-random,removingspeciestraitsassociatedwithsensitivityto disturbanceandretainingmoreresilientsurvivors.Tropicalbirdcommunities thathavelargelyavoidedseverehistoricaldisturbancetheoreticallycontain morespecieswithdisturbance-sensitivetraits (suchaspoordispersaland ecologicalspecialiZation),accentuatingtheimpactsofforestfragmentation (b). Backgroundturnoverofspecies,shownin (b) butpresentinallpathways, israndomwithrespecttodisturbance-sensitivetraits.Adifferentmechanism involvestheevolutionofflightadaptationstocopewithseasonalfluctuations intemperatureandresources (including vegetation,insects,flowersand fruits).In birds, thepredominantadaptationtoseasonalityinvolvesincreased mobility (fromlocaldispersaltolong-distancemigration),sohighly seasonal communitieslackdispersal-limitedspecies,potentiallyincreasingtheir resiliencetoforestfragmentation (c) incomparisonwithclimaticallystable regions (b). Relativespeciesrichnessisshownbythenumberofbirdsilhouettes inthecommunity.