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zenodo40/100

Fig. 1. The modified time calibrated Bayesian tree and a in Fig. 3 in Fig. 4 in Fig. 4 in Responses of Phyllostomid Bats to Traditional Agriculture in Neotropical Montane Forests of Southern Mexico.

Fig. 1. The modified time calibrated Bayesian tree and a plot of four major avian developmental modes (Prum et al. 2015). The complete tree is divided into parts A and B. Scale in the Y-axis: millions of years ago.

opencc-by-4.0Sep 2019View details →
zenodo40/100

Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario

<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales.&nbsp;</p> <p>&nbsp;</p> <p><strong>Dataset details:</strong></p> <p>See paper.&nbsp;</p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p>&nbsp;</p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>

opencc-by-4.0Oct 2024View details →
dryad40/100

Exotics are more complementary over time in tree biodiversity-ecosystem functioning experiments

<p><strong>Background and aims</strong></p> <p>The Biodiversity – Ecosystem Functioning (BEF) literature proposes that ecosystem functioning increases with biodiversity because of complementarity in resource use among species, associated with functional diversity. In this study, we challenge the trait-based ecology framework by comparing congeneric exotic (European) and native (North American) tree species showing similar resource-use functional trait values. The trait-based framework suggests that two functionally equivalent species should play similar roles in a community, resulting in similar interactions and performances. However, several studies showed that when growing in mixtures, exotic species that are functionally equivalent to native species benefitted from enemy-release, resulting in a reduced apparent competition. We hypothesize that exotic species should be more productive than native species because the exotic species benefit from reduced apparent competition due to enemy-release rather than from possessing more competitive resource-use functional traits.</p> <p><strong>Methods</strong></p> <p>We study a diversity experiments, part of the International Diversity Experiment Network with Trees (IDENT), composed of two identical sites, each with two orthogonal diversity gradients: species richness and functional diversity. The functional gradient consists of species combinations of equal richness but increasing functional diversity, using different combinations of species provenance to assess the relationship between productivity, functional diversity and species provenance, independently of species richness. We grew a total of 12 species (six native, six exotic) in different combinations of one, two and six species mixtures. The exotic species were selected based on their functional equivalence to their native congeneric species.</p> <p><strong>Key Results</strong></p> <p>Eight years after planting, we found that exotic species were more productive than native species, but only at high functional diversity. Results indicate that exotic species overall benefit from a reduced apparent competition, and that exotic-increased productivity at high functional diversity is consistent with the enemy release hypothesis.</p> <p><strong>Conclusions</strong></p> <p>After eight years, exotic species were more productive overall than their native counterparts, but only in the most functionally diverse communities. This study represents a first step in understanding the relative importance of complementarity in resource-use and apparent competition in a context of an exotic tree species invasion.</p>

opencc-zeroJul 2021View details →
zenodo40/100

FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.) in Review of the Frog Genus Silverstoneia, with Descriptions of Five New Species from the Colombian Chocó (Dendrobatidae: Colostethinae)

FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.)

opencc-by-4.0Oct 2013View details →
dryad40/100

Data from: Robustness of divergence time estimation despite gene tree error: A case study of fireflies (Coleoptera: Lampyridae)

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publicOct 2024View details →
dryad40/100

Exotics are more complementary over time in tree biodiversity-ecosystem functioning experiments

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publicJul 2021View details →
dryad40/100

Climate model experiments of regional-scale tree die-off replaced by shrubs (select variables at daily time resolution): Part 4

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publicApr 2025View details →
dryad40/100

Climate model experiments of regional-scale tree die-off replaced by grass (select variables at daily time resolution): Part 2

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publicApr 2025View details →
dryad40/100

The implications of incongruence between gene tree and species tree topologies for divergence time estimation

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publicMar 2022View details →
dryad40/100

Completing gene trees without species trees in sub-quadratic time

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publicJun 2023View details →
edi40/100

Interior Alaska managed sites: tree inventory measured one time in either summer 2012 or 2013

This dataset contains the diameter of all trees (diameter at breast height) and shrubs (basal diameter) measured in managed areas and adjacent unmanaged black spruce forest, in Summers 2012 and 2013.

openOpenSep 2017View details →
edi40/100

National Phenology Network tree phenology: Phenology, the timing of biological events such as bud break, plant flowering times and bird migration

Phenology is the study of recurring plant/animal phenophases. Environmental changes will likely impact phenological events at the species level and above. Most of the phenological changes are unknown at the species level and may have large impacts on natural ecosystems in the future. As part of a long term phenological experiment on forest ecosystems, since spring 2009 phenophases were observed on strategically selected forest species. This experiment is part of the National Phenology Network (USA-NPN).

openCC0Jul 2021View details →
zenodo36/100

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> &nbsp;&nbsp;&nbsp;3.1. Raw data<br> &nbsp;&nbsp;&nbsp;3.2. List of trees<br> &nbsp;&nbsp;&nbsp;3.3. Tree-size frequencies</p> <p>4. LIST OF FILES<br> &nbsp;&nbsp;&nbsp;4.1. Raw data<br> &nbsp;&nbsp;&nbsp;4.2. List of trees<br> &nbsp;&nbsp;&nbsp;4.3. Tree-size frequencies<br> &nbsp; &nbsp;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]: &#39;Topological properties of epidemic aftershock processes&#39;, by J. Bar&oacute; 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: &quot;ETASbranch_b(b)r(r)N(nb)*&quot;</p> <p>- M0(= 1) = magnitude of completeness (arbitrary for the study of topological properties of trees)<br> - (b) &nbsp;= b-value (arbitrary for the study of topological properties of trees)<br> - (nb) = average branching ratio<br> - (r) &nbsp;= 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 &quot;*.Seq&quot; 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> &nbsp; c0:Time (arbitrary, used here as id.)<br> &nbsp; c1:Magnitude of the event<br> &nbsp; c2:Identification number of the cluster or tree<br> &nbsp; c3:Depth of the event in the tree structure (background events have Depth = 0)<br> &nbsp; c4:Time of the direct parent of the event (set to -1 for background event)<br> &nbsp; c5:Magnitude of the direct parent of the event (set to -10 if background event)<br> &nbsp; c6:Time of the event initiating the tree (set to own time if background)<br> &nbsp; c7:Magnitude of the event initiating the tree &nbsp;(set to own magnitude if background)<br> &nbsp; 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 &quot;*TopoTrees.dat&quot; files obtained from simulations (after processing of *.Seq files. Files ending with &quot;N0.99&quot;, &quot;N0.50&quot; obtained from 10^5 background events from files above. Files ending with &quot;N0.500*&quot; 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> &nbsp; c0:Maximum Depth of the tree<br> &nbsp; c1:Number of events in the tree<br> &nbsp; c2:Average depth of leaves<br> &nbsp; c3:total number of leaves<br> &nbsp; c4:Sum of the depth of all leaves (=c2*c3)<br> &nbsp; c5:(=0) not used<br> &nbsp; c6:Magnitude of root<br> &nbsp; c7:Maximum magnitude of an event inside the tree</p> <p><br> 4.3. Tree-size frequencies<br> --------------------------</p> <p>18 x &quot;*TopoTrees.FK&quot; files obtained from &quot;*TopoTrees.dat&quot;. 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&nbsp;13 Kb</p> <p>4.1. Raw data (42 files)<br> ------------------------</p> <p>ETASbranch_b0.50r0.00N0.50.Seq<br> md5:bae21678db4fe575b06b69a5f32253d2 &nbsp;&nbsp; &nbsp;12.5 Mb<br> ETASbranch_b0.50r0.00N0.99.Seq<br> md5:76ea70ba94aeeed7ba3c358305f4b85b &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.05N0.50.Seq<br> md5:0ccf5af07efb9dd99d63a9675fd3069f &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.05N0.99.Seq<br> md5:77a7bfd490af54a5a3d15c4183e9e6f7 &nbsp;&nbsp; &nbsp;1.4 Gb<br> ETASbranch_b0.50r0.10N0.50.Seq<br> md5:f274717039f8f69ce57d5054577777af &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.10N0.99.Seq<br> md5:e18f4c26acfdff8d79a6223aa86867d2 &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.15N0.50.Seq<br> md5:5f93c14a8d0f88fd021bc6d8a5fc7d6e &nbsp;&nbsp; &nbsp;12.3 Mb<br> ETASbranch_b0.50r0.15N0.99.Seq<br> md5:906e09ecb6c6c18fdbcfdca3d9dbe225 &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.20N0.50.Seq<br> md5:2467c2c7fafeddaa630074e991cb7767 &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.20N0.99.Seq<br> md5:64c45a47b7f953d7088b10d4badd068c &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.25N0.50.Seq<br> md5:d65abaa4664b2412707b27b6e9143215 &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.25N0.99.Seq<br> md5:8d7467b0bd80ce7e8cbcaf5dfe2725b7 &nbsp;&nbsp; &nbsp;1.2 Gb<br> ETASbranch_b0.50r0.30N0.50.Seq<br> md5:fd3af9802b3b66a623172bb2aafc0a0b &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.30N0.99.Seq<br> md5:2ad1b9b9c858638067af6068dfd25d59 &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.35N0.50.Seq<br> md5:8cf36cc90a19f1283c838dee4849626b &nbsp;&nbsp; &nbsp;12.5 Mb<br> ETASbranch_b0.50r0.35N0.99.Seq<br> md5:6b35085100561865ac9463edfb166d7f &nbsp;&nbsp; &nbsp;1.4 Gb<br> ETASbranch_b0.50r0.40N0.50.Seq<br> md5:317b154c8ed74262b18c41762597d236 &nbsp;&nbsp; &nbsp;12.3 Mb<br> ETASbranch_b0.50r0.40N0.99.Seq<br> md5:c83dc299035928c9ae8a7f6d101ac9b8 &nbsp;&nbsp; &nbsp;1.2 Gb<br> ETASbranch_b0.50r0.45N0.50.Seq<br> md5:d3baccfbf347e555c30f3dfb1db1d9c0 &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.45N0.99.Seq<br> md5:5bf42163acc2eae1eeae8549b850ccc9 &nbsp;&nbsp; &nbsp;1.1 Gb<br> ETASbranch_b0.50r0.50N0.50.Seq<br> md5:3ad7427725dfcf302c2dc2c519dad8c0 &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.50N0.99.Seq<br> md5:7316fb3661b87215a1a7b1c11a54975b &nbsp;&nbsp; &nbsp;1.3 Gb<br> ETASbranch_b0.50r0.55N0.50.Seq<br> md5:6dbc9846506675c61d110ae918165a6b &nbsp;&nbsp; &nbsp;12.4 Mb<br> ETASbranch_b0.50r0.55N0.99.Seq<br> md5:5d4d007bf898512185dd2f87b715715e &nbsp;&nbsp; &nbsp;1.4 Gb<br> ETASbranch_b0.50r0.60N0.50.Seq<br> md5:569c1c409ef618f518fe6fb6f41493a3 &nbsp;&nbsp; &nbsp;12.7 Mb<br> ETASbranch_b0.50r0.60N0.99.Seq<br> md5:e739613598e8d2530d108ff24e8c045a &nbsp;&nbsp; &nbsp;1.1 Gb<br> ETASbranch_b0.50r0.65N0.50.Seq<br> md5:e95cead475c1eedb0618bebf1b548ec2 &nbsp;&nbsp; &nbsp;12.3 Mb<br> ETASbranch_b0.50r0.65N0.99.Seq<br> md5:8fe8b042085f261971d94bf12f96dc7e &nbsp;&nbsp; &nbsp;1 Gb<br> ETASbranch_b0.50r0.70N0.50.Seq<br> md5:48824f1efdbc31c5bb4d4f799bc166d5 &nbsp;&nbsp; &nbsp;12.2 Mb<br> ETASbranch_b0.50r0.70N0.99.Seq<br> md5:2ead803394a4c3b04f1e22e9b8c5ba46 &nbsp;&nbsp; &nbsp;872.9 Mb<br> ETASbranch_b0.50r0.75N0.50.Seq<br> md5:8c5cf4753836076c677a74d831354f33 &nbsp;&nbsp; &nbsp;12.2 Mb<br> ETASbranch_b0.50r0.75N0.99.Seq<br> md5:91ed931aedfb365761799e0882fcfb86 &nbsp;&nbsp; &nbsp;1 Gb<br> ETASbranch_b0.50r0.80N0.50.Seq<br> md5:f93e1779bc5344ffd17021bb387bb373 &nbsp;&nbsp; &nbsp;11 Mb<br> ETASbranch_b0.50r0.80N0.99.Seq<br> md5:6b0fd7fcad20ac4467fecda9502e9954 &nbsp;&nbsp; &nbsp;75.6 Mb<br> ETASbranch_b0.50r0.85N0.50.Seq<br> md5:a5debe21de70204a490c75dd7c68657a &nbsp;&nbsp; &nbsp;11 Mb<br> ETASbranch_b0.50r0.85N0.99.Seq<br> md5:f9ed3ad6db12ad4a612c3945ccf72f18 &nbsp;&nbsp; &nbsp;48.5 Mb<br> ETASbranch_b0.50r0.90N0.50.Seq<br> md5:4493eef2170b68052cf56636c63091d7 &nbsp;&nbsp; &nbsp;9.7 Mb<br> ETASbranch_b0.50r0.90N0.99.Seq<br> md5:70043ed6c84e3896268c46f77eabefd3 &nbsp;&nbsp; &nbsp;26.7 Mb<br> ETASbranch_b0.50r0.95N0.50.Seq<br> md5:874916faa95c63312aa27aecea45de8f &nbsp;&nbsp; &nbsp;7.5 Mb<br> ETASbranch_b0.50r0.95N0.99.Seq<br> md5:bbc54464c2e3cbac64401978216ae993 &nbsp;&nbsp; &nbsp;11.7 Mb<br> ETASbranch_b0.50r1.00N0.50.Seq<br> md5:3ff467b894f46a4be0b0747c686edad5 &nbsp;&nbsp; &nbsp;5.8 Mb<br> ETASbranch_b0.50r1.00N0.99.Seq<br> md5:d4d5416d56ed3de6eb2702a8eeb0a0b5 &nbsp;&nbsp; &nbsp;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 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.00N0.99TopoTrees.dat<br> md5:34eb899782444475151073ed6fcfc6aa &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.05N0.50TopoTrees.dat<br> md5:cc5f0edfc31a5f15394444243560e0ad &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.05N0.99TopoTrees.dat<br> md5:58cc7e7e746051f9efbe510c164a2b09 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.10N0.500TopoTrees.dat<br> md5:52cb3a9a5aeb1e222743bcdad3ea3b3d &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.10N0.50TopoTrees.dat<br> md5:a68a69280c0129ee3554d5c97dd0fa47 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.10N0.99TopoTrees.dat<br> md5:307edf1353af5141f11d89cbab6c4d30 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.15N0.500TopoTrees.dat<br> md5:52807004c32bdc1a999a2aaf1ff91bb9 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.15N0.50TopoTrees.dat<br> md5:0bdb45b13ed0fd5c0fa1619310d71670 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.15N0.99TopoTrees.dat<br> md5:f26e351184921ed9a85c8993404a4718 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.20N0.500TopoTrees.dat<br> md5:5fd930a98e28d551c9c204d22c6f564a &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.20N0.50TopoTrees.dat<br> md5:4aaeb952e0ecb7d4fc4bd2fb7822c729 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.20N0.99TopoTrees.dat<br> md5:a30687662b269f447d7e4cc020bd3773 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.25N0.500TopoTrees.dat<br> md5:4168e6e70b247977213d2278b94d65f3 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.25N0.50TopoTrees.dat<br> md5:b360877be00088b52676ba4717377761 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.25N0.99TopoTrees.dat<br> md5:34d11b64c78c852d4f50de7b1265c9b7 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.30N0.500TopoTrees.dat<br> md5:8c1a8b07c5f2350646d415a687f84492 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.30N0.50TopoTrees.dat<br> md5:0c1bfccd8cd141090a0bfb0cc7ae1ccd &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.30N0.99TopoTrees.dat<br> md5:652f975754241fed317acba066cf339b &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.35N0.500TopoTrees.dat<br> md5:6bc2a3f7b4f7fd8a92595271d2ea425f &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.35N0.50TopoTrees.dat<br> md5:d1d4e8a467d8d445cd3f6ca27c3c8af3 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.35N0.99TopoTrees.dat<br> md5:e56eee0119b193898ea729b75c572617 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.40N0.500TopoTrees.dat<br> md5:370a2f6c670fd7c9b515e43891417b73 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.40N0.50TopoTrees.dat<br> md5:9f50c23e33cca983df58e2b17f29f8e5 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.dat<br> md5:d9971930b46838de09a7ccdc7cd9b459 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.45N0.500TopoTrees.dat<br> md5:cfef99746043d0718ad19e48ffdfeee4 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.45N0.50TopoTrees.dat<br> md5:44667f56360024e40b11be4f52ccf9c3 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.45N0.99TopoTrees.dat<br> md5:09b84c6f3c9dd58331c6f9ab4eec8f58 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.50N0.500TopoTrees.dat<br> md5:e9e880f24ffc9ba40db7c7f78d0f8d89 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> md5:ce4835d532513bffd54723f285207898 &nbsp;&nbsp; &nbsp;1.9 Mb<br> ETASbranch_b1.00r0.50N0.99TopoTrees.dat<br> md5:4c935d76aee113c88f4a3348197f75f3 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.55N0.500TopoTrees.dat<br> md5:46cd9e8d508c15009e90a194a141008f &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.55N0.50TopoTrees.dat<br> md5:8e1cb9c364c2a275d10373b54dab6239 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.55N0.99TopoTrees.dat<br> md5:304b947c52e826a9b4cb68c38f007443 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.60N0.500TopoTrees.dat<br> md5:df8b7889e76694188b29f7fc16f6dce2 &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.60N0.50TopoTrees.dat<br> md5:fc6c2110e139290ae9401765e8aae782 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.dat<br> md5:6af466cc527b7422f1f958bfc6a87d15 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.65N0.500TopoTrees.dat<br> md5:129abd6de7821da465265a485217156d &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.65N0.50TopoTrees.dat<br> md5:469fc2ae3768fe9ae0ab0b8fbfaf3051 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.65N0.99TopoTrees.dat<br> md5:b7148c414b243b1911de8543d25e3d38 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> md5:3a3eed181ae6308c29f274d5e14a97df &nbsp;&nbsp; &nbsp;1.8 Gb<br> ETASbranch_b1.00r0.70N0.50TopoTrees.dat<br> md5:ca7472f17916e7f8634a97c08ca96c58 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.70N0.99TopoTrees.dat<br> md5:8c2a4ab3800e8a8178be5d6e856f5b50 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.75N0.50TopoTrees.dat<br> md5:374796f34a3e25f711601161f8a34ae7 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.75N0.99TopoTrees.dat<br> md5:591f6ac31545f3e7558b4a5e151c80a5 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.80N0.50TopoTrees.dat<br> md5:473c6e9ac33cc6e8eabe0ed3c91b40b7 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.80N0.99TopoTrees.dat<br> md5:81f41fbce89c931f87890133b4c0f9f9 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.85N0.50TopoTrees.dat<br> md5:2c38db4208898d95c1c5b2ef0a94c930 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.85N0.99TopoTrees.dat<br> md5:4866f179b52ff5a6191d47e6d8ead5ce &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.90N0.50TopoTrees.dat<br> md5:f14a7f700c0c73743eb5747399abdcce &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.dat<br> md5:da4bd00aeefcbd1e67f0d7fe8fb1d8be &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.95N0.50TopoTrees.dat<br> md5:57b7f0bd901bb47ba3c253f801301716 &nbsp;&nbsp; &nbsp;18.6 Mb<br> ETASbranch_b1.00r0.95N0.99TopoTrees.dat<br> md5:6381354768603c6d2129c99df2a7798a &nbsp;&nbsp; &nbsp;18.6 Mb</p> <p>4.3. Tree-size frequencies (18 files)<br> -------------------------------------</p> <p>ETASbranch_b1.00r0.00N0.99TopoTrees.FK<br> md5:5b6ccaf1c1218f101ed24c332bfefec3 &nbsp;&nbsp; &nbsp;612 Kb<br> ETASbranch_b1.00r0.10N0.30TopoTrees.FK<br> md5:f9f07bbd7be5377e1589101e85640149 &nbsp;&nbsp; &nbsp;468 B<br> ETASbranch_b1.00r0.20N0.30TopoTrees.FK<br> md5:6c7e40f8b93c7a3e62f2c105f0e7a89b &nbsp;&nbsp; &nbsp;558 B<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> md5:158884c9bc4afa10c8ebb2e7b516a421 &nbsp;&nbsp; &nbsp;3.3 Mb<br> ETASbranch_b1.00r0.30N0.30TopoTrees.FK<br> md5:14d837b9763b9a54e64311e832e29f04 &nbsp;&nbsp; &nbsp;846 B<br> ETASbranch_b1.00r0.30N0.99TopoTrees.FK<br> md5:90b8b5a38a83cb4b4c833b605cf6b38e &nbsp;&nbsp; &nbsp;2.1 Mb<br> ETASbranch_b1.00r0.40N0.30TopoTrees.FK<br> md5:1f198a7c08078066e7fa57ce9ebda8eb &nbsp;&nbsp; &nbsp;3 Kb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.FK<br> md5:229b837e7950fe85ea1c51be8e3f457e &nbsp;&nbsp; &nbsp;3.3 Mb<br> ETASbranch_b1.00r0.50N0.30TopoTrees.FK<br> md5:6ead8bff09c3c2687c526648bfec6ab3 &nbsp;&nbsp; &nbsp;16 Kb<br> ETASbranch_b1.00r0.60N0.30TopoTrees.FK<br> md5:38c5cb9f544367ff9bdab766c4bcf03f &nbsp;&nbsp; &nbsp;112 Kb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.FK<br> md5:2591d4e2dd1c2051c0b7d96f423c526a &nbsp;&nbsp; &nbsp;106.5 Mb<br> ETASbranch_b1.00r0.70N0.30TopoTrees.FK<br> md5:4f7741314e1a0d0b1c1733532d909277 &nbsp;&nbsp; &nbsp;7.5 Mb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 &nbsp;&nbsp; &nbsp;602 Kb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 &nbsp;&nbsp; &nbsp;602 Kb<br> ETASbranch_b1.00r0.90N0.30TopoTrees.FK<br> md5:914a782fa2e73c0bc5a6e70bbb2afec9 &nbsp;&nbsp; &nbsp;1.3 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.FK<br> md5:cc165045d4d9f25c0f9c30c0ec71759f &nbsp;&nbsp; &nbsp;864 Kb<br> ETASbranch_b1.00r0.99N0.30TopoTrees.FK<br> md5:a08cfc27a0e42d54f207ea05b7210714 &nbsp;&nbsp; &nbsp;187 Kb<br> ETASbranch_b1.00r0.99N0.99TopoTrees.FK<br> md5:a15681be56f870a95fe62794b11524df &nbsp;&nbsp; &nbsp;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> &nbsp;</p>

opencc-by-4.0Feb 2020View details →
dryad36/100

Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod

<p><span>1. Leaf-out and flowering are two key phenological events of plants, denoting the respective onsets of visible vegetative growth and reproduction during the year. For each species, the schedule of vegetative growth and reproduction is crucial to the maximization of its fitness. Warming-induced advances of leaf-out and flowering have been reported frequently, however, it is unclear whether the responses of the two events are equal for any given species. </span></p> <p><span>2. Using long-term phenological records in Europe, we examined simultaneously the responses of both leaf-out and flowering of four common temperate tree species to climate warming and further examined the effects of winter chilling, spring forcing and photoperiod on the responses of the two events. </span></p> <p><span>3. We found that regardless whether flowering or leaf-out occurred first, the first event advanced more than the second during 1950 – 2013, resulting in a prolonged time interval between the two events. The temporal changes were also supported by a similar geographical trend that the time interval between the two events increased from cold to warm sites. Due to the warming-induced reduction in chilling, the spring forcing accumulated until the second event was increased more than the forcing accumulated until the first event, and that reduced the temperature sensitivity of the second event. In addition to the effect of chilling, the shorter photoperiod, associated with the advanced spring phenology, was also likely to substantially increase the spring forcing accumulated until the second event, which thus slowed down its advance, compared to the advance of the first event. The relative contributions of chilling and photoperiod to the increased forcing varied between species and events, with chilling mostly outweighing photoperiod. </span></p> <p><span>4. Synthesis. This study provides the large-scale empirical evidence of prolonged time interval between leaf-out and flowering with climate warming. The unequal advances of the two events may alter the partition of resources between vegetative growth and reproduction and cause different changes of spring frost damage to vegetative and reproductive tissues, which may alter species fitness and further affect ecosystem structure and function.</span></p>

opencc-zeroNov 2020View details →
dryad36/100

Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance

<p><span>Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (<span>Landsat-based Detection of Trends in Disturbance and Recovery) with</span> a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (<i>Dendroctonus rufipennis</i>) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. <span>Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record.  </span></span></p>

opencc-zeroDec 2020View details →
dryad36/100

Genomic vulnerability to rapid climate warming in a tree species with a long generation time

<p><span><span><span><span><span><span><span><span><span><span><span>The ongoing increase in global temperature affects biodiversity, especially in mountain regions where climate change is exacerbated. As sessile, long-lived organisms, trees are especially challenged in terms of adapting to rapid climate change. Here, we show that low rates of allele frequency shifts in Swiss stone pine (<i>Pinus cembra</i>) occurring near the treeline result in high genomic vulnerability to future climate warming, presumably due to the species' long generation time. Using exome sequencing data from adult and juvenile cohorts in the Swiss Alps, we found an average rate of allele frequency shift of 1.23×10<sup>-2</sup>/generation (i.e. 40 years) at presumably neutral loci, with similar rates for putatively adaptive loci associated with temperature (0.96×10<sup>-2</sup>/generation) and precipitation (0.91×10<sup>-2</sup>/generation). These recent shifts were corroborated by forward-in-time simulations at neutral and adaptive loci. Additionally, in juvenile trees at the colonisation front we detected alleles putatively beneficial under a future warmer and drier climate. Notably, the observed past rate of allele frequency shift in temperature-associated loci was decidedly lower than the estimated average rate of 6.29×10<sup>-2</sup>/generation needed to match a moderate future climate scenario (RCP4.5). Our findings suggest that species with long generation times may have difficulty keeping up with the rapid climate change occurring in high mountain areas and thus are prone to local extinction in their current main elevation range.</span></span></span></span></span></span></span></span></span></span></span></p>

opencc-zeroDec 2020View details →
dryad36/100

Comprehensive phylogenomic time tree of bryophytes reveals deep relationships and uncovers gene incongruences in the last 500 million years of diversification

<p class="MsoNormal"><strong><span>Premise</span></strong></p> <p class="MsoNormal"><span>Bryophytes, land plants defined by a free-living gametophyte and an unbranched sporophyte, form a major component of terrestrial plant biomass, structuring ecological communities in all biomes. </span><span>Our understanding of the evolutionary history of hornworts, liverworts and mosses has been significantly reshaped by inferences from molecular data, highlighting extensive homoplasy in various traits and repeated bursts of diversification. However, the timing of key events in the phylogeny, and the degree to which the observed homoplasy represents error or biological processes, remain poorly resolved.</span></p> <p class="MsoNormal"><strong><span>Methods</span></strong></p> <p class="MsoNormal"><span>Using the GoFlag probe set, we sampled 405 exons representing 228 nuclear genes for 531 species from 51 of the 53 orders of bryophytes. We inferred the species phylogeny from gene tree analyses using concatenated and coalescence approaches, assessed gene conflict, and estimated the timing of divergences based on 29 fossil calibrations.</span></p> <p class="MsoNormal"><strong><span>Results</span></strong></p> <p class="MsoNormal"><span>The phylogeny resolves many relationships across the bryophytes, enabling us to resurrect five liverwort orders and recognize three more, and propose ten new orders of mosses. Most orders originated in the Jurassic or earlier and diversified in the Cretaceous or later. The phylogenomic data also highlight topological conflict in parts of the tree, suggesting complex processes of diversification that cannot be adequately captured in a single gene tree topology. </span></p> <p class="MsoNormal"><strong><span>Conclusions</span></strong></p> <p class="MsoNormal"><span>We sampled hundreds of homologous loci across a broad phylogenetic spectrum spanning at least 450 Ma of evolution, and these data resolved many of the critical nodes of the diversification of bryophytes. The data also highlight the need to explore the mechanisms underlying the phylogenetic ambiguity at specific nodes. The phylogenomic data provide an expandable framework toward reconstructing a comprehensive phylogeny of bryophytes and for investigating the transformations of traits in this important group of plants.</span></p>

opencc-zeroDec 2022View details →
zenodo36/100

National-scale tree species/genera map for Poland from Sentinel-2 time series

<p>Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024)<em>: <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a></em></p> <p>The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2023View details →
dryad36/100

Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth

<p>These are the data reported in van Kampen et al. (2022) "Drought timing and species growth phenology determine intra-annual recovery of tree height and diameter growth" published in AoB Plants. They describe patterns of height and diameter growth for saplings of six tree species undergoing experimental drought conditions at different times of year. </p>

opencc-zeroMar 2022View details →
dryad36/100

No apparent trade-off between the quality of nest grown feathers and time spent in the nest in an aerial insectivore, the tree swallow

<p>Life history theory provides a framework for understanding how trade-offs generate negative trait associations. Among nestling birds, time spent in the nest, risk of predation, and lifespan covary, but some associations are only found within species while others are only observed between species. A recent comparative study suggests that allocation trade-offs may be alleviated by disinvestment in ephemeral traits, such as nest-grown feathers, that are quickly replaced. However, direct resource allocation trade-offs cannot be inferred from inter-specific trait-associations without complementary intra-specific studies. Here, we asked whether there is evidence for a within-species allocation trade-off between feather quality and time spent in the nest in tree swallows (<em>Tachycineta bicolor</em>). Consistent with the idea that ephemeral traits are deprioritized, nest-grown feathers had lower barb density than adult feathers. However, despite substantial variation in fledging age among nestlings, there was no evidence for a negative association between time in the nest and feather quality. Furthermore, accounting for differences in resource availability by considering provisioning rate and a nest predation treatment did not reveal a trade-off that was masked by variation in resources. Our results are most consistent with the idea that the inter-specific association between development and feather quality arises from adaptive specialization, rather than from a direct allocation trade-off.</p>

opencc-zeroApr 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record