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Branching Trees from standard Epidemic Aftershock Sequences (ETAS) Model (no time, no space)
<p>All files licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)</p> <p>###############<br> 0. SUMMARY<br> ###############</p> <p>1. DESCRIPTION</p> <p>2. INPUT PARAMETERS</p> <p>3. TYPES OF FILES<br> 3.1. Raw data<br> 3.2. List of trees<br> 3.3. Tree-size frequencies</p> <p>4. LIST OF FILES<br> 4.1. Raw data<br> 4.2. List of trees<br> 4.3. Tree-size frequencies<br> 4.4 Known missing/broken files</p> <p>###############<br> 1. DESCRIPTION<br> ################<br> Simulation results of an standard ETAS model as a branching process. Using a two seed version of the RANDU linear congruential pseudorandom number generator. The offspring number is a Poisson number given the rate n(M) (see below). Details of simulation procedure can be found in reference [1]: 'Topological properties of epidemic aftershock processes', by J. Baró submitted to J. of Geophysical Research - Solid Earth (JGR-B)</p> <p>##############<br> 2. INPUT PARAMETERS<br> ##############<br> The input parameters (see reference for details) for each raw and processes data-file are indicated in the prefix of the file: "ETASbranch_b(b)r(r)N(nb)*"</p> <p>- M0(= 1) = magnitude of completeness (arbitrary for the study of topological properties of trees)<br> - (b) = b-value (arbitrary for the study of topological properties of trees)<br> - (nb) = average branching ratio<br> - (r) = ratio a/b</p> <p>The b-value defines the distribution of event-magnitudes: P(M) = 10^(b*(M-M0)) . The nb and a define the productivity law: n(M) = (nb*(b-a)/b)*10^(a*(M-M0))</p> <p><br> #################<br> 3. TYPES OF FILES<br> #################</p> <p>3.1. Raw data:<br> --------------</p> <p>42 x "*.Seq" files with input b=0.50 and different nb, r values. Raw data from simulation code. (all cases, simulated with 10^5 background events)<br> Each row represents an individual event in the point process, or element of the simulated branching forest.<br> Columns description (9 columns x data point):<br> c0:Time (arbitrary, used here as id.)<br> c1:Magnitude of the event<br> c2:Identification number of the cluster or tree<br> c3:Depth of the event in the tree structure (background events have Depth = 0)<br> c4:Time of the direct parent of the event (set to -1 for background event)<br> c5:Magnitude of the direct parent of the event (set to -10 if background event)<br> c6:Time of the event initiating the tree (set to own time if background)<br> c7:Magnitude of the event initiating the tree (set to own magnitude if background)<br> c8:N or offspring number of the event. (Events are leafs if N=0)</p> <p><br> 3.2. List of trees<br> ------------------</p> <p>53 x "*TopoTrees.dat" files obtained from simulations (after processing of *.Seq files. Files ending with "N0.99", "N0.50" obtained from 10^5 background events from files above. Files ending with "N0.500*" obtained from 10^7 simulations)<br> Each row represents an individual tree constituted by one or several causally connected events of the simulated branching forest. Files used to generate fig. 4 of ref. [1]</p> <p>Columns description (8 columns x data point):<br> c0:Maximum Depth of the tree<br> c1:Number of events in the tree<br> c2:Average depth of leaves<br> c3:total number of leaves<br> c4:Sum of the depth of all leaves (=c2*c3)<br> c5:(=0) not used<br> c6:Magnitude of root<br> c7:Maximum magnitude of an event inside the tree</p> <p><br> 4.3. Tree-size frequencies<br> --------------------------</p> <p>18 x "*TopoTrees.FK" files obtained from "*TopoTrees.dat". Contains the frequencies of tree-sizes. Each raw number correspond to a size. Each value corresponds to number of incidences of that size divided by total number of events (10^5 in all cases). Notice that last point is missing at size = max-length, freq.= 1.0 / total number of events. Files used to generate fig. 3 of ref. [1]</p> <p><br> ################<br> 4. LIST OF FILES<br> ################</p> <p>(copy of this text)<br> readme.txt<br> md5:2073cdbb4a0afd8ab96f8bfaef20579f 13 Kb</p> <p>4.1. Raw data (42 files)<br> ------------------------</p> <p>ETASbranch_b0.50r0.00N0.50.Seq<br> md5:bae21678db4fe575b06b69a5f32253d2 12.5 Mb<br> ETASbranch_b0.50r0.00N0.99.Seq<br> md5:76ea70ba94aeeed7ba3c358305f4b85b 1.3 Gb<br> ETASbranch_b0.50r0.05N0.50.Seq<br> md5:0ccf5af07efb9dd99d63a9675fd3069f 12.4 Mb<br> ETASbranch_b0.50r0.05N0.99.Seq<br> md5:77a7bfd490af54a5a3d15c4183e9e6f7 1.4 Gb<br> ETASbranch_b0.50r0.10N0.50.Seq<br> md5:f274717039f8f69ce57d5054577777af 12.4 Mb<br> ETASbranch_b0.50r0.10N0.99.Seq<br> md5:e18f4c26acfdff8d79a6223aa86867d2 1.3 Gb<br> ETASbranch_b0.50r0.15N0.50.Seq<br> md5:5f93c14a8d0f88fd021bc6d8a5fc7d6e 12.3 Mb<br> ETASbranch_b0.50r0.15N0.99.Seq<br> md5:906e09ecb6c6c18fdbcfdca3d9dbe225 1.3 Gb<br> ETASbranch_b0.50r0.20N0.50.Seq<br> md5:2467c2c7fafeddaa630074e991cb7767 12.4 Mb<br> ETASbranch_b0.50r0.20N0.99.Seq<br> md5:64c45a47b7f953d7088b10d4badd068c 1.3 Gb<br> ETASbranch_b0.50r0.25N0.50.Seq<br> md5:d65abaa4664b2412707b27b6e9143215 12.4 Mb<br> ETASbranch_b0.50r0.25N0.99.Seq<br> md5:8d7467b0bd80ce7e8cbcaf5dfe2725b7 1.2 Gb<br> ETASbranch_b0.50r0.30N0.50.Seq<br> md5:fd3af9802b3b66a623172bb2aafc0a0b 12.4 Mb<br> ETASbranch_b0.50r0.30N0.99.Seq<br> md5:2ad1b9b9c858638067af6068dfd25d59 1.3 Gb<br> ETASbranch_b0.50r0.35N0.50.Seq<br> md5:8cf36cc90a19f1283c838dee4849626b 12.5 Mb<br> ETASbranch_b0.50r0.35N0.99.Seq<br> md5:6b35085100561865ac9463edfb166d7f 1.4 Gb<br> ETASbranch_b0.50r0.40N0.50.Seq<br> md5:317b154c8ed74262b18c41762597d236 12.3 Mb<br> ETASbranch_b0.50r0.40N0.99.Seq<br> md5:c83dc299035928c9ae8a7f6d101ac9b8 1.2 Gb<br> ETASbranch_b0.50r0.45N0.50.Seq<br> md5:d3baccfbf347e555c30f3dfb1db1d9c0 12.4 Mb<br> ETASbranch_b0.50r0.45N0.99.Seq<br> md5:5bf42163acc2eae1eeae8549b850ccc9 1.1 Gb<br> ETASbranch_b0.50r0.50N0.50.Seq<br> md5:3ad7427725dfcf302c2dc2c519dad8c0 12.4 Mb<br> ETASbranch_b0.50r0.50N0.99.Seq<br> md5:7316fb3661b87215a1a7b1c11a54975b 1.3 Gb<br> ETASbranch_b0.50r0.55N0.50.Seq<br> md5:6dbc9846506675c61d110ae918165a6b 12.4 Mb<br> ETASbranch_b0.50r0.55N0.99.Seq<br> md5:5d4d007bf898512185dd2f87b715715e 1.4 Gb<br> ETASbranch_b0.50r0.60N0.50.Seq<br> md5:569c1c409ef618f518fe6fb6f41493a3 12.7 Mb<br> ETASbranch_b0.50r0.60N0.99.Seq<br> md5:e739613598e8d2530d108ff24e8c045a 1.1 Gb<br> ETASbranch_b0.50r0.65N0.50.Seq<br> md5:e95cead475c1eedb0618bebf1b548ec2 12.3 Mb<br> ETASbranch_b0.50r0.65N0.99.Seq<br> md5:8fe8b042085f261971d94bf12f96dc7e 1 Gb<br> ETASbranch_b0.50r0.70N0.50.Seq<br> md5:48824f1efdbc31c5bb4d4f799bc166d5 12.2 Mb<br> ETASbranch_b0.50r0.70N0.99.Seq<br> md5:2ead803394a4c3b04f1e22e9b8c5ba46 872.9 Mb<br> ETASbranch_b0.50r0.75N0.50.Seq<br> md5:8c5cf4753836076c677a74d831354f33 12.2 Mb<br> ETASbranch_b0.50r0.75N0.99.Seq<br> md5:91ed931aedfb365761799e0882fcfb86 1 Gb<br> ETASbranch_b0.50r0.80N0.50.Seq<br> md5:f93e1779bc5344ffd17021bb387bb373 11 Mb<br> ETASbranch_b0.50r0.80N0.99.Seq<br> md5:6b0fd7fcad20ac4467fecda9502e9954 75.6 Mb<br> ETASbranch_b0.50r0.85N0.50.Seq<br> md5:a5debe21de70204a490c75dd7c68657a 11 Mb<br> ETASbranch_b0.50r0.85N0.99.Seq<br> md5:f9ed3ad6db12ad4a612c3945ccf72f18 48.5 Mb<br> ETASbranch_b0.50r0.90N0.50.Seq<br> md5:4493eef2170b68052cf56636c63091d7 9.7 Mb<br> ETASbranch_b0.50r0.90N0.99.Seq<br> md5:70043ed6c84e3896268c46f77eabefd3 26.7 Mb<br> ETASbranch_b0.50r0.95N0.50.Seq<br> md5:874916faa95c63312aa27aecea45de8f 7.5 Mb<br> ETASbranch_b0.50r0.95N0.99.Seq<br> md5:bbc54464c2e3cbac64401978216ae993 11.7 Mb<br> ETASbranch_b0.50r1.00N0.50.Seq<br> md5:3ff467b894f46a4be0b0747c686edad5 5.8 Mb<br> ETASbranch_b0.50r1.00N0.99.Seq<br> md5:d4d5416d56ed3de6eb2702a8eeb0a0b5 5.8 Mb</p> <p><br> 3.2. List of trees (53 files)<br> -----------------------------</p> <p><br> ETASbranch_b1.00r0.00N0.500TopoTrees.dat<br> md5:a5834b8d047fd3446e6d83488422bb5b 1.8 Gb<br> ETASbranch_b1.00r0.00N0.99TopoTrees.dat<br> md5:34eb899782444475151073ed6fcfc6aa 18.6 Mb<br> ETASbranch_b1.00r0.05N0.50TopoTrees.dat<br> md5:cc5f0edfc31a5f15394444243560e0ad 18.6 Mb<br> ETASbranch_b1.00r0.05N0.99TopoTrees.dat<br> md5:58cc7e7e746051f9efbe510c164a2b09 18.6 Mb<br> ETASbranch_b1.00r0.10N0.500TopoTrees.dat<br> md5:52cb3a9a5aeb1e222743bcdad3ea3b3d 1.8 Gb<br> ETASbranch_b1.00r0.10N0.50TopoTrees.dat<br> md5:a68a69280c0129ee3554d5c97dd0fa47 18.6 Mb<br> ETASbranch_b1.00r0.10N0.99TopoTrees.dat<br> md5:307edf1353af5141f11d89cbab6c4d30 18.6 Mb<br> ETASbranch_b1.00r0.15N0.500TopoTrees.dat<br> md5:52807004c32bdc1a999a2aaf1ff91bb9 1.8 Gb<br> ETASbranch_b1.00r0.15N0.50TopoTrees.dat<br> md5:0bdb45b13ed0fd5c0fa1619310d71670 18.6 Mb<br> ETASbranch_b1.00r0.15N0.99TopoTrees.dat<br> md5:f26e351184921ed9a85c8993404a4718 18.6 Mb<br> ETASbranch_b1.00r0.20N0.500TopoTrees.dat<br> md5:5fd930a98e28d551c9c204d22c6f564a 1.8 Gb<br> ETASbranch_b1.00r0.20N0.50TopoTrees.dat<br> md5:4aaeb952e0ecb7d4fc4bd2fb7822c729 18.6 Mb<br> ETASbranch_b1.00r0.20N0.99TopoTrees.dat<br> md5:a30687662b269f447d7e4cc020bd3773 18.6 Mb<br> ETASbranch_b1.00r0.25N0.500TopoTrees.dat<br> md5:4168e6e70b247977213d2278b94d65f3 1.8 Gb<br> ETASbranch_b1.00r0.25N0.50TopoTrees.dat<br> md5:b360877be00088b52676ba4717377761 18.6 Mb<br> ETASbranch_b1.00r0.25N0.99TopoTrees.dat<br> md5:34d11b64c78c852d4f50de7b1265c9b7 18.6 Mb<br> ETASbranch_b1.00r0.30N0.500TopoTrees.dat<br> md5:8c1a8b07c5f2350646d415a687f84492 1.8 Gb<br> ETASbranch_b1.00r0.30N0.50TopoTrees.dat<br> md5:0c1bfccd8cd141090a0bfb0cc7ae1ccd 18.6 Mb<br> ETASbranch_b1.00r0.30N0.99TopoTrees.dat<br> md5:652f975754241fed317acba066cf339b 18.6 Mb<br> ETASbranch_b1.00r0.35N0.500TopoTrees.dat<br> md5:6bc2a3f7b4f7fd8a92595271d2ea425f 1.8 Gb<br> ETASbranch_b1.00r0.35N0.50TopoTrees.dat<br> md5:d1d4e8a467d8d445cd3f6ca27c3c8af3 18.6 Mb<br> ETASbranch_b1.00r0.35N0.99TopoTrees.dat<br> md5:e56eee0119b193898ea729b75c572617 18.6 Mb<br> ETASbranch_b1.00r0.40N0.500TopoTrees.dat<br> md5:370a2f6c670fd7c9b515e43891417b73 1.8 Gb<br> ETASbranch_b1.00r0.40N0.50TopoTrees.dat<br> md5:9f50c23e33cca983df58e2b17f29f8e5 18.6 Mb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.dat<br> md5:d9971930b46838de09a7ccdc7cd9b459 18.6 Mb<br> ETASbranch_b1.00r0.45N0.500TopoTrees.dat<br> md5:cfef99746043d0718ad19e48ffdfeee4 1.8 Gb<br> ETASbranch_b1.00r0.45N0.50TopoTrees.dat<br> md5:44667f56360024e40b11be4f52ccf9c3 18.6 Mb<br> ETASbranch_b1.00r0.45N0.99TopoTrees.dat<br> md5:09b84c6f3c9dd58331c6f9ab4eec8f58 18.6 Mb<br> ETASbranch_b1.00r0.50N0.500TopoTrees.dat<br> md5:e9e880f24ffc9ba40db7c7f78d0f8d89 1.8 Gb<br> ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> md5:ce4835d532513bffd54723f285207898 1.9 Mb<br> ETASbranch_b1.00r0.50N0.99TopoTrees.dat<br> md5:4c935d76aee113c88f4a3348197f75f3 18.6 Mb<br> ETASbranch_b1.00r0.55N0.500TopoTrees.dat<br> md5:46cd9e8d508c15009e90a194a141008f 1.8 Gb<br> ETASbranch_b1.00r0.55N0.50TopoTrees.dat<br> md5:8e1cb9c364c2a275d10373b54dab6239 18.6 Mb<br> ETASbranch_b1.00r0.55N0.99TopoTrees.dat<br> md5:304b947c52e826a9b4cb68c38f007443 18.6 Mb<br> ETASbranch_b1.00r0.60N0.500TopoTrees.dat<br> md5:df8b7889e76694188b29f7fc16f6dce2 1.8 Gb<br> ETASbranch_b1.00r0.60N0.50TopoTrees.dat<br> md5:fc6c2110e139290ae9401765e8aae782 18.6 Mb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.dat<br> md5:6af466cc527b7422f1f958bfc6a87d15 18.6 Mb<br> ETASbranch_b1.00r0.65N0.500TopoTrees.dat<br> md5:129abd6de7821da465265a485217156d 1.8 Gb<br> ETASbranch_b1.00r0.65N0.50TopoTrees.dat<br> md5:469fc2ae3768fe9ae0ab0b8fbfaf3051 18.6 Mb<br> ETASbranch_b1.00r0.65N0.99TopoTrees.dat<br> md5:b7148c414b243b1911de8543d25e3d38 18.6 Mb<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> md5:3a3eed181ae6308c29f274d5e14a97df 1.8 Gb<br> ETASbranch_b1.00r0.70N0.50TopoTrees.dat<br> md5:ca7472f17916e7f8634a97c08ca96c58 18.6 Mb<br> ETASbranch_b1.00r0.70N0.99TopoTrees.dat<br> md5:8c2a4ab3800e8a8178be5d6e856f5b50 18.6 Mb<br> ETASbranch_b1.00r0.75N0.50TopoTrees.dat<br> md5:374796f34a3e25f711601161f8a34ae7 18.6 Mb<br> ETASbranch_b1.00r0.75N0.99TopoTrees.dat<br> md5:591f6ac31545f3e7558b4a5e151c80a5 18.6 Mb<br> ETASbranch_b1.00r0.80N0.50TopoTrees.dat<br> md5:473c6e9ac33cc6e8eabe0ed3c91b40b7 18.6 Mb<br> ETASbranch_b1.00r0.80N0.99TopoTrees.dat<br> md5:81f41fbce89c931f87890133b4c0f9f9 18.6 Mb<br> ETASbranch_b1.00r0.85N0.50TopoTrees.dat<br> md5:2c38db4208898d95c1c5b2ef0a94c930 18.6 Mb<br> ETASbranch_b1.00r0.85N0.99TopoTrees.dat<br> md5:4866f179b52ff5a6191d47e6d8ead5ce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.50TopoTrees.dat<br> md5:f14a7f700c0c73743eb5747399abdcce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.dat<br> md5:da4bd00aeefcbd1e67f0d7fe8fb1d8be 18.6 Mb<br> ETASbranch_b1.00r0.95N0.50TopoTrees.dat<br> md5:57b7f0bd901bb47ba3c253f801301716 18.6 Mb<br> ETASbranch_b1.00r0.95N0.99TopoTrees.dat<br> md5:6381354768603c6d2129c99df2a7798a 18.6 Mb</p> <p>4.3. Tree-size frequencies (18 files)<br> -------------------------------------</p> <p>ETASbranch_b1.00r0.00N0.99TopoTrees.FK<br> md5:5b6ccaf1c1218f101ed24c332bfefec3 612 Kb<br> ETASbranch_b1.00r0.10N0.30TopoTrees.FK<br> md5:f9f07bbd7be5377e1589101e85640149 468 B<br> ETASbranch_b1.00r0.20N0.30TopoTrees.FK<br> md5:6c7e40f8b93c7a3e62f2c105f0e7a89b 558 B<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> md5:158884c9bc4afa10c8ebb2e7b516a421 3.3 Mb<br> ETASbranch_b1.00r0.30N0.30TopoTrees.FK<br> md5:14d837b9763b9a54e64311e832e29f04 846 B<br> ETASbranch_b1.00r0.30N0.99TopoTrees.FK<br> md5:90b8b5a38a83cb4b4c833b605cf6b38e 2.1 Mb<br> ETASbranch_b1.00r0.40N0.30TopoTrees.FK<br> md5:1f198a7c08078066e7fa57ce9ebda8eb 3 Kb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.FK<br> md5:229b837e7950fe85ea1c51be8e3f457e 3.3 Mb<br> ETASbranch_b1.00r0.50N0.30TopoTrees.FK<br> md5:6ead8bff09c3c2687c526648bfec6ab3 16 Kb<br> ETASbranch_b1.00r0.60N0.30TopoTrees.FK<br> md5:38c5cb9f544367ff9bdab766c4bcf03f 112 Kb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.FK<br> md5:2591d4e2dd1c2051c0b7d96f423c526a 106.5 Mb<br> ETASbranch_b1.00r0.70N0.30TopoTrees.FK<br> md5:4f7741314e1a0d0b1c1733532d909277 7.5 Mb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.90N0.30TopoTrees.FK<br> md5:914a782fa2e73c0bc5a6e70bbb2afec9 1.3 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.FK<br> md5:cc165045d4d9f25c0f9c30c0ec71759f 864 Kb<br> ETASbranch_b1.00r0.99N0.30TopoTrees.FK<br> md5:a08cfc27a0e42d54f207ea05b7210714 187 Kb<br> ETASbranch_b1.00r0.99N0.99TopoTrees.FK<br> md5:a15681be56f870a95fe62794b11524df 365 Kb</p> <p><br> 4.4 Known missing/broken files<br> ------------------------------</p> <p>ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> ETASbranch_b1.00r0.10N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.50N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.70N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.05N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.75N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.80N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.85N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.90N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.95N0.500TopoTrees.dat<br> </p>
Data from: Testing main Amazonian rivers as barriers across time and space within widespread taxa
Aim: Present Amazonian diversity patterns can result from many different mechanisms and, consequently, the factors contributing to divergence across regions and/or taxa may differ. Nevertheless, the river-barrier hypothesis (RBH) is still widely invoked as a causal process in divergence of Amazonian species. Here we use model-based phylogeographic analyses to test the extent to which major Amazonian rivers act similarly as barriers across time and space in two broadly distributed Amazonian taxa. Local: Amazon rainforest. Taxon: The lizard Gonatodes humeralis (Sphaerodactylidae) and the tree frog Dendropsophus leucophyllatus (Hylidae). Methods: We obtained RADseq data for samples distributed across main river barriers, representing main Areas of Endemism previously proposed for the region. We conduct model-based phylogeographic and genetic differentiation analyses across each population pair. Results: Measures of genetic differentiation (based on FST calculated from genomic data) show that all rivers are associated with significant genetic differentiation. Parameters estimated under investigated divergence models showed that divergence times for populations separated by each of the 11 bordering rivers were all fairly recent. The degree of differentiation consistently varied between taxa and among rivers, which is not an artifact of any corresponding difference in the genetic diversities of the respective taxa, or to amounts of migration based on analyses of the site-frequency-spectrum. Main conclusions: Taken together, our results support a dispersal (rather than vicariance) history, without strong evidence of congruence between these species and rivers. However, once a species crossed a river, populations separated by each and every river have remained isolated – in this sense, rivers act similarly as barriers to any further gene flow. This result suggests differing degrees of persistence and gives rise to the seeming contradiction that the divergence process indeed varies across time, space, and species, even though major Amazonian rivers have acted as secondary barriers to gene flow in the focal taxa.
Data from: Consistent scaling of inbreeding depression in space and time in a house sparrow metapopulation
<p>Inbreeding may increase the extinction risk of small populations. Yet, studies using modern genomic tools to investigate inbreeding depression in nature have been limited to single populations, and little is known about the dynamics of inbreeding depression in subdivided populations over time. Natural populations often experience different environmental conditions and differ in demographic history and genetic composition; characteristics that can affect the severity of inbreeding depression. We utilised extensive long-term data on more than 3100 individuals from eight islands in an insular house sparrow metapopulation to examine the generality of inbreeding effects. Using genomic<sub> </sub>estimates of realised inbreeding, we discovered that inbred individuals had lower survival probabilities and produced fewer recruiting offspring than non-inbred individuals. Inbreeding depression, measured as the decline in fitness related traits per unit inbreeding, did not vary appreciably among populations or with time. As a consequence, populations with more resident inbreeding (due to their demographic history) paid a higher total fitness cost, evidenced by a larger variance in fitness explained by inbreeding within these populations. Our results are in contrast to the idea that effects of inbreeding generally depend on ecological factors and genetic differences among populations, and expand the understanding of inbreeding depression in natural subdivided populations.</p>
The Life Cycle of Features in Highly-Configurable Software Systems Evolving in Space and Time
<p>Dataset covering the entire development history of four open-source systems from different domains, covering a total of 37500 commits from up to 20 years of development, which can serve as a source of information for new studies on the evolution of systems in space and time.</p>
Data and code for: Time of night and moonlight structure vertical space use by insectivorous bats in a Neotropical rainforest: an acoustic monitoring study
<p>Abstract</p> <p>Previous research has shown diverse vertical space use by various taxa, highlighting the importance of forest canopy. Yet, we often fail to explore how this three-dimensional space use changes over time. Here we use canopy tower systems in French Guiana to monitor neotropical bat activity above and below the forest canopy throughout nine nights in the wet season. We show that different bats use both canopy and understory space differently, and that this can change throughout the night. We find that bats are overall more active in the canopy, but multiple species/acoustic complexes are more active in the understory. We also find that species that do not seem to prefer understory or canopy, when data are aggregated by night, do show temporally changing preferences in hourly activity. This work highlights the need to consider temporal axes in studies of space use, both throughout daily cycles and across seasons.</p>
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Exact Spin-Boson-Model Tunneling Dynamics with Time Dependent Variation Matrix Product States (TDVMPS). Barrier height and temperature parameter space
<p>Spin-Boson tunnelling data acquired using the T-TEDOA method for Time-Dependent-Variational-Matrix-Product-States (TDVMPS) accompanying the paper <a href="https://doi.org/10.3389/fchem.2020.600731">https://doi.org/10.3389/fchem.2020.600731</a>.</p> <p> </p>
Data from: SNPs across time and space: population genomic signatures of founder events and epizootics in the House Finch (Haemorhous mexicanus)
Identifying genomic signatures of natural selection can be challenging against a background of demographic changes such as bottlenecks and population expansions. Here, we disentangle the effects of demography from selection in the House Finch (Haemorhous mexicanus) using samples collected before and after a pathogen-induced selection event. Using ddRADseq, we genotyped over 18,000 SNPs across the genome in native pre-epizootic western US birds, introduced birds from Hawaii and the eastern United States, post-epizootic eastern birds, and western birds sampled across a similar time span. We found 14% and 7% reductions in nucleotide diversity, respectively, in Hawaiian and pre-epizootic eastern birds relative to pre-epizootic western birds, as well as elevated levels of linkage disequilibrium and other signatures of founder events. Despite finding numerous significant frequency shifts (outlier loci) between pre-epizootic native and introduced populations, we found no signal of reduced genetic diversity, elevated linkage disequilibrium, or outlier loci as a result of the epizootic. Simulations demonstrate that the proportion of outliers associated with founder events could be explained by genetic drift. This rare view of genetic evolution across time in an invasive species provides direct evidence that demographic shifts like founder events have genetic consequences more widespread across the genome than natural selection.
Data from: The Bogert effect revisited: salamander regulatory behaviors are differently constrained by time and space
The use of behavior to buffer extreme environmental variation is expected to enable species to a) extend the breadth of environments they inhabit beyond that predicted from climatic data, and b) diminish the negative effects of broad-scale and chronic disturbances such as climate change. The term Bogert effect refers to behavioral compensation entailing microhabitat selection to maintain performance across a gradient of environmental conditions resulting in evolutionary inertia of physiological traits. Here we compare microhabitats used by plethodontid salamanders distributed along an elevational gradient to determine whether there is behavioral compensation that buffers them from deleterious temperatures and moisture levels. Overall, salamanders preferred cooler and more mesic environments and occupied microhabitats that maintained constant moisture conditions at both high and low elevation sites. Our results suggest that salamanders use microhabitats to regulate temperature and moisture levels, which is consistent with the Bogert effect. Maintenance of more moist conditions may help buffer these species from rising temperatures, but only in suitable high-elevation environments that are likely to disappear over the next century. We conclude that behavioral regulation of temperature and moisture is a potential mechanism for the Bogert effect in plethodontid salamanders.
On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models
<p>In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.</p>
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Effects of COVID-19 lockdown restrictions on parents' attitudes towards green space and time spent outside by children in Cambridgeshire and North London, United Kingdom
<p>1. In the United Kingdom, children are spending less time outdoors and are more disconnected from nature than previous generations. However, interaction with nature at a young age can benefit wellbeing and long-term support for conservation. Green space accessibility in the UK varies between rural and urban areas and is lower for children than for adults. It is possible that COVID-19 lockdown restrictions may have influenced these differences.</p> <p>2. In this study, we assessed parents' attitudes towards green space, as well as whether the COVID-19 lockdown restrictions had affected their attitudes or the amount of time spent outside by their children, via an online survey for parents of primary school-aged children in Cambridgeshire and North London, UK (n = 171). We assessed whether responses were affected by local environment (rural, suburban or urban), school type (state-funded or fee-paying) or garden access (with or without private garden access).</p> <p>3. Parents' attitudes towards green space were significantly different between local environments: 76.9% of rural parents reported being happy with the amount of green space to which their children had access, in contrast with only 40.5% of urban parents.</p> <p>4. COVID-19 lockdown restrictions also affected parents' attitudes to the importance of green space, and this differed between local environments: 75.7% of urban parents said their views had changed during lockdown, in contrast with 35.9% of rural parents. The change in amount of time spent outside by children during lockdown was also significantly different between local environments: most urban children spent more time inside during lockdown, whilst most rural children spent more time outside.</p> <p>5. Neither parents' attitudes towards green space nor the amount of time spent outside by their children varied with school type or garden access.</p> <p>6. Our results suggest that lockdown restrictions exacerbated pre-existing differences in access to nature between urban and rural children in our sampled population. We suggest that the current increased public and political awareness of the value of green space should be capitalised on to increase provision and access to green space and to reduce inequalities in accessibility and awareness of nature between children from different backgrounds.</p>
Functional niche constraints on carnivore assemblages (mammalia: carnivora) in the Americas: What facilitates coexistence through space and time?
<p><b>Aim:</b> Mammalian carnivores are among the best studied groups in terms of evolutionary history. However, the effects of species interactions in shaping community assemblages remain poorly understood. We hypothesize that indirect interactions via ecological trait filtering play a key role in structuring carnivoran assemblages, mediate coexistence, and thus should show high functional diversity in space and time at continental scales.</p> <p><b>Location:</b> Americas.</p> <p><b>Taxon:</b> Mammalian carnivores (Mammalia: Carnivora).</p> <p><b>Methods:</b> We followed a macroecological perspective via ecological networks analyses for indirect interactions, and assessed the underlying functional diversity (FD) across space and from the Last Interglacial to the present in the Americas. We analyzed the potential distributions and six ecological traits of 88 species to establish possible mechanisms that enables species to coexist and the underlying diversity patterns. We compared the empirical results with two null models, and two sensitivity analyses.</p> <p><b>Results:</b> Co-occurring carnivore species presented ecological segregation driven mainly by a size ratio (S <sub>R</sub>) relationship, called here the body-size spatial anti-clustering effect. The underlying FD patterns showed low redundancy towards the tropics and the poles during the times evaluated. However, during the LGM, shifts occurred primarily at high latitudes in North America. This shift affected the S <sub>R</sub> relationship and therefore changed functional diversity patterns. These local-to-continental interactions mediated by the S <sub>R</sub> are significant from an ecological and biogeographic perspective, suggesting a robust and consistent trend in which carnivore species of similar size have a lower probability of occupying the same area unless they differentiate in other ecological trait spaces.</p> <p><b>Main conclusions:</b> The S <sub>R</sub> relationship is potentially a primary mechanism limiting carnivore coexistence, reflecting functional filtering. The S <sub>R</sub> tends to be conservative across different ecological trait groups and through time and space. We propose that the body-size spatial anti-clustering effect can directly measure species' coexistence and mediate FD patterns in the Americas.</p>
Quantifying within-species trait variation in space and time reveals limits to trait-mediated drought response
<p>Climate change is stressing many forests around the globe, yet some tree species may be able to persist through acclimation and adaptation to new environmental conditions. The ability of a tree to acclimate during its lifetime through changes in physiology and functional traits, defined here as its acclimation potential, is not well known. We investigated the acclimation potential of trembling aspen (Populus tremuloides) and ponderosa pine (Pinus ponderosa) trees by examining within-species variation in drought response functional traits across both space and time, and how trait variation influences drought-induced tree mortality. We measured xylem tension, morphological traits, and physiological traits on mature trees in southwestern Colorado, USA across a climate gradient that spanned the distribution limits of each species and three years with large differences in climate. Trembling aspen functional traits showed high within-species variation, and osmotic adjustment and carbon isotope discrimination were key determinants for increased drought tolerance in dry sites and in dry years. However, trembling aspen trees at low elevation were pushed past their drought tolerance limit during the severe 2018 drought year, as elevated mortality occurred. Higher specific leaf area during drought was correlated with higher percentages of canopy dieback the following year. Ponderosa pine functional traits showed less within-species variation, though osmotic adjustment was also a key mechanism for increased drought tolerance. Remarkably, almost all traits varied more year-to-year than across elevation in both species. Our results shed light on the scope and limits of intraspecific trait variation for mediating drought responses in key southwestern US tree species and will help improve our ability to model and predict forest responses to climate change. </p>
Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes
<p>Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.</p> <p> </p> <p> </p>
Data for "Neural space-time model for dynamic multi-shot imaging"
<p>Data for "Neural space-time model for dynamic multi-shot imaging"</p> <p>Link to preprint: <a href="https://www.biorxiv.org/content/10.1101/2024.01.16.575950">https://www.biorxiv.org/content/10.1101/2024.01.16.575950</a></p> <p>Code: <a href="https://github.com/rmcao/nstm">https://github.com/rmcao/nstm</a></p> <p> </p> <p>Raw images from structured illumination microscopy (SIM):</p> <ul> <li>beads.tif: a dense microbead sample with vibrating motion (Fig.2)</li> <li>mito.tif: a live RPE-1 cell expressing StayGold-tagged mitochondrial matrix protein (Fig.3)</li> <li>er.tif: a RPE-1 cell expressing StayGold-tagged endoplasmic reticulum (Fig.4)</li> <li>f-actin.tif: a live RPE-1 cell tagged with F-Actin Halo-JF585 (Extended Data Fig.6)</li> <li>f-actin_extradelay.tif: a live RPE-1 cell tagged with F-Actin Halo-JF585 with extra time delay between rotations (Extended Data Fig.6)</li> </ul> <p> </p> <p>Baseline reconstruction:</p> <ul> <li>beads_fairSIM.tif: conventional reconstruction of the dense microbead sample using fairSIM software (https://www.fairsim.org)</li> <li>mito_cudasirecon.tif: conventional reconstruction for a mitochondria-labeled cell (mito.tif) using cuda-accelerated SIM reconstruction software (https://github.com/scopetools/cudasirecon)</li> <li>er_cudasirecon.tif: conventional reconstruction for an endoplasmic reticulum-labeled cell (er.tif) using cuda-accelerated SIM reconstruction software</li> <li>f-actin_cudasirecon.tif: conventional reconstruction for a F-Actin Halo-JF585 labeled cell (f-actin.tif) using cuda-accelerated SIM reconstruction software</li> <li>f-actin_extradelay_cudasirecon.tif: conventional reconstruction for a F-Actin Halo-JF585 labeled cell with extra delay (f-actin_extradelay.tif) using cuda-accelerated SIM reconstruction software</li> </ul>
Figures for "Conformer-specific Photochemistry Imaged in Real Space and Time "
<p>This is data and code to create the figures of the manuscript "Conformer-specific Photochemistry Imaged in Real Space and Time ". Data are in .npy format, code to create the figures is in python.</p>
Dataset and Code for "A case study of space-time performance comparison of wind turbines on a wind farm"
<p>This is the computer code and partial dataset for producing the results in the paper, Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” <em>Renewable Energy</em>, Vol. 171, pp. 735-746 .</p> <p>[<strong>Note 1</strong>: In the Reproducibility Report, The table numbers are off by one, namely that Table 2 should be Table 3, and Table 3 should be Table 4.]</p> <p>[<strong>Note 2:</strong> The results in Table 4 included in the paper were produced by the DSWE version 1.3.4. Those results are still reproducible if using the same version of DSWE. Since then, DSWE went through some changes and updates. When using DSWE 1.5.1 version (the latest version as of May 10, 2022) and setting the optimization method to 'L-BFGS-B' (because version 1.3.4’s default optimization setting was 'L-BFGS-B'), the results corresponding to the second row in Table 4 are somewhat different. For the specific results using DSWE 1.5.1, please see the note section at the end of the <a href="https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2022/05/J77_Reproducibility_Report_v2.pdf">updated Reproducibility Report</a>.]</p>
Resampling alpine herbarium records reveals changes in plant traits over space and time - dataset
<p><strong>Data overview:</strong></p> <p>These data correspond to the analyses conducted for the article "Resampling alpine herbarium records reveals changes in plant traits over space and time" by Francesca Jaroszynska, Christian Rixen, Sarah Woodin, Jonathan Lenoir and Sonja Wipf, in Journal of Ecology</p> <p><strong>Metadata for jaroszynska_herbarium_traits_data.csv:</strong></p> <p>date = date; date of collection</p> <p>time = factor; time of collection (historical or recent)</p> <p>elevation = numerical; elevation in metres above sea level of the sample collection site</p> <p>selevation = numerical; scaled <em>elevation</em></p> <p>selevation2 = numerical; elevation in metres above sea level of sample collection site (elevation/1000).</p> <p>sSlope = numerical; scaled slope (slope/10)</p> <p>slope = numerical; computed slope based on elevation</p> <p>trait = string; name of the measured trait</p> <ul> <li> <p>crFlowerN = numerical; Cardamine resedifolia; number of flowers</p> </li> <li> <p>crHeight = numerical; Cardamine resedifolia; plant height</p> </li> <li> <p>crLeafL = numerical; Cardamine resedifolia; length of longest leaf</p> </li> <li> <p>crRosetteLeafN = numerical; Cardamine resedifolia; number of leaves in rosette</p> </li> <li> <p>paBasalLeafL = numerical; Poa alpina; basal leaf length</p> </li> <li> <p>paInflorescenceL = numerical; Poa alpina; inflorescence length</p> </li> <li> <p>paHeight = numerical; Poa alpina; plant height</p> </li> <li> <p>pvInfL = numerical; Polygonum viviparum; length of inflorescence</p> </li> <li> <p>pvLA = numerical; Polygonum viviparum; leaf area (length x width)</p> </li> <li> <p>pvLeafL = numerical; Polygonum viviparum; leaf length</p> </li> <li> <p>pvRepH= numerical; Polygonum viviparum; plant height</p> </li> <li> <p>rgFlowerStemL = numerical; Ranunculus glacialis; flowering stem length</p> </li> <li> <p>rgLeafStemL = numerical; Ranunculus glacialis; petiole length</p> </li> <li> <p>rgLeafW = numerical; Ranunculus glacialis; leaf width</p> </li> <li> <p>rgFlowerN = integer; Ranunculus glacialis; number of flowers</p> </li> </ul> <p> </p> <p>traitGroup = factor; the group to which each trait belongs (VegHeight = vegetative height, ReprHeight = reproductive height, ReprOut = reproductive output, PhotoCap = photosynthetic capacity)</p> <p>value = numerical; value of the trait measured</p> <p>species = factor; species code (car_res = Cardamine resedifolia, ran_glac = Ranunculus glacialis, pol_viv = Polygonum viviparum, poa_alp = Poa alpina)</p> <p>transect = string; transect along which the herbarium sample was taken</p> <p>confidence = factor; reliability of the metadata associated with the herbarium sample, assigned by the authors Jaroszynska and Wipf (low, medium, high)</p> <p>northness = numerical; northness</p> <p>eastness = numerical; eastness</p> <p>observer = string; botanist who conducted the collection</p> <p>sheet = string; unique identifier for the collection sheet</p> <p> </p> <p><strong>Metadata for jaroszynska_climate_traits_data.csv:</strong></p> <p>year = year; year of sample collection</p> <p>Month = integer; month of sample colection</p> <p>Temperature = numerical; monthly average temperature (ºC)</p> <p>Precipitation = numerical; monthly total precipitation (mm)</p> <p>yearMonth = string; year.month</p> <p>season = factor; season associated to the corresponding month (spring, summer, autumn, winter)</p> <p>timePeriod = factor; climate period referring to the time before, after, or during the baseline reference period (see article for further details)</p> <p>meanAnnTemp = numerical; mean annual temperature (ºC)</p> <p>sumAnnPrecip = numerical; total annual precipitation (mm)</p> <p>meanSeaTemp = numerical; mean seasonal temperature (ªC)</p> <p>sumSeaPrecip = numerical; total seasonal precipitation (mm)</p> <p>meanRefTemp = numerical; mean seasonal temperature for reference period (ªC)</p> <p>temp_anomaly = numerical; temerature anomaly from the reference period (ªC)</p> <p>lagMonths = string; used in seasonal calculation</p> <p>seasonal_precip = numerical; seasonal precipitation (mm)</p> <p>precip_anomaly = numerical; seasonal precipitation anomaly (mm)</p>
Figure S51 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S51. Higher resolution version of Figure 1. See Figure 1 for caption.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.