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03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, run_adaptiveMD.py : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

00_Caver.tar.gz - Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA).  final_clustering ├── tunnel_custers # contains caver output files for individual tunnels clusters  │   ├── ... ├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters  │   ├── ... 01_CaverDock_Tunnels_Profile.tar.gz - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained.  # Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are: - calculations/*/caverdock.conf :  The config file input for caverdock calculation.  - calculations/*/DBE.pdbqt : Input file for the substrate DBE. - calculations/*/stripped_system*.pdbqt : Input file for the Protein/Receptor - calculations/*/stripped_system*.dsd : Tunnel discretization file. Notes:  - calculations/*/stripped_system*.pdb : PDB file for the tunnel.  02_Minimization_and_Equilibration.tar.gz -  Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations. 03_HTMD_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  04_HTMD_Cavity - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. 05_HTMD_Cavity_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  06_HTMD_Tunnels - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  07_MD-Analysis.tar.gz - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K.  # Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are: - Epoch_Convergence/epochs_dist_counts.csv :  Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs.  -  Distances/*/dist_s_r*.csv : Contains .csv file for the  frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  - RMSD_RMSF/*/*.csv : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program.             Example input: pytraj.rmsd(traj, mask='1-295@CA')             Example input: pytraj.rmsf(traj, mask=':1-295', options='byres') - COM_RMSF/.xlsx : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213) - Kinetics/kinetics.txt : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates. - Percentages/.csv : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å). - Tunnels_lengths/.csv : Contains tunnel_lengths.csv, Summary of tunnel lengths.xlsx files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in tunnel_lengths.csv and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the Summary of tunnel lengths.xlsx file.  - Sample_proportions/.csv : Contains sample_proportions.csv file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html  or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path  - *.py : Python scripts used to build and analysis Markov state models with use case and distances of ligand. - Generated_models/ : Contains models_rep[].dat files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below: ├── Cavity │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat ├── Cavity_Bulk │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat └── Tunnels     ├── model_rep1.dat     ├── model_rep2.dat     └── model_rep3.dat  08_TransportTools.tar.gz - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... - event.csv file contains the aggregated summary of events inferred from the 4-filtered_events_statistics.txt files of each results of respective schemes 09_MSM_states.tar.gz - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below:  ├── Bulk │   ├── rep1 # MSM states for replicate 1 │   ├── rep2 # MSM states for replicate 2 │   ├── rep3 # MSM states for replicate 3 ├── Cavity │   ├── rep1  │   ├── rep2  │   ├── rep3  ├── Cavity&Bulk │   ├── rep1  │   ├── rep2 │   ├── rep3 ├── Tunnels │   ├── rep1  │   ├── rep2 │   ├── rep3 10_MSM_fingerprints.tar.gz - Contains the Markov state models (MSMs) distances generated from repository dir 09_MSM_states consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package. - MSM_Distances/*/rep*/*.csv : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  11_ULS_clustering_and_transition_assignments.tar.gz - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files: 1. for the transition detection of DBE and the classification in  Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and  2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown. # the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels: ├── average_tunnel_utilization_per_scheme.png ├── average_tunnel_utilization.png ├── Bulk │   ├── Bulk_run_htmd_0_combined_df.csv │   ├── Bulk_run_htmd_0_transitions_counts.csv │   ├── Bulk_run_htmd_1_combined_df.csv │   ├── Bulk_run_htmd_1_transitions_counts.csv │   ├── Bulk_run_htmd_2_combined_df.csv │   └── Bulk_run_htmd_2_transitions_counts.csv ├── Bulk&Cavity │   ├── Cavity&Bulk_run_htmd_0_combined_df.csv │   ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_1_combined_df.csv │   ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_2_combined_df.csv │   └── Cavity&Bulk_run_htmd_2_transitions_counts.csv ├── Cavity │   ├── Cavity_run_htmd_0_combined_df.csv │   ├── Cavity_run_htmd_0_transitions_counts.csv │   ├── Cavity_run_htmd_1_combined_df.csv │   ├── Cavity_run_htmd_1_transitions_counts.csv │   ├── Cavity_run_htmd_2_combined_df.csv │   └── Cavity_run_htmd_2_transitions_counts.csv ├── parse_distances_msm.py ├── schemes_comparison_piechart_per_scheme.png └── Tunnels     ├── Tunnels_run_htmd_0_combined_df.csv     ├── Tunnels_run_htmd_0_transitions_counts.csv     ├── Tunnels_run_htmd_1_combined_df.csv     ├── Tunnels_run_htmd_1_transitions_counts.csv     ├── Tunnels_run_htmd_2_combined_df.csv     └── Tunnels_run_htmd_2_transitions_counts.csv

05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

opencc-zeroSep 2021View details →

A non-native earthworm shifts the seed predation dynamics of a native weed

Seed predators both consume and disperse seeds, with important consequences for the population dynamics of many plant species. The net effect of multiple seed predators depends on the relative proportion of the seed pool each predator obtains, and this proportion should reflect species-specific habitat preferences. We studied the effect of the non-native earthworm, Lumbricus terrestris, on seed loss dynamics in the native weed, Ambrosia trifida (giant ragweed). Giant ragweed seeds are predated by mice, but L. terrestris may protect the seeds against rodent predation by caching them in its burrows. We investigated these interactions, as well as how environmental factors affected net seed losses by competing seed predators. A two-year field study was conducted in which we measured removal of experimentally dispersed giant ragweed seeds by earthworms and mice in habitats varying in plant cover. We analyzed the relative proportion of seeds taken by each species under the varying experimental conditions. Species-specific responses to abiotic conditions and plant cover drove variation in the share of seeds taken by earthworms versus mice, with earthworms gaining relatively more seeds under warmer, wetter conditions and low plant cover habitats, and mice obtaining more seeds under colder, drier conditions and high plant cover habitats. Plant cover and weather conditions also determined which predator species accessed seeds first, and this conferred a competitive advantage that was compounded over time. Earthworms cached some seeds under all experimental conditions, suggesting that L. terrestris can act mutualistically with giant ragweed by making seeds inaccessible to rodent seed predators. Synthesis and applications. Our results support the view that interactions among the environment and competing seed predators determine the fate of seed pools. The data also support the hypothesis that L. terrestris facilitates giant ragweed by competing with mice for giant ragweed seeds, likely contributing to its spread across the landscape and hindering effective weed management. Lumbricus terrestris is prevalent throughout temperate regions and may similarly affect seed predation dynamics of other large-seeded species, impacting plant communities across a range of habitats.

opencc-zeroMay 2023View details →

Density dependence of seed dispersal and fecundity profoundly alters the spread dynamics of plant populations

<ol> <li>Plant population spread has fundamental ecological and evolutionary importance. Both determinants of plant population spread, fecundity and dispersal, can be density-dependent, which should cause feedback between population densities and spread dynamics. Yet it is poorly understood how density-dependence affects key characteristics of spread: spread rate at which the location of the furthest forward individual moves, edge depth (the geographical area over which individuals contribute to spread) and population continuity (occupancy of the spreading population).</li> <li>We present a general modelling framework for analysing the effects of density-dependent fecundity and dispersal on population spread and parameterize this framework with experimental data from a common-garden experiment using five wind-dispersed plant species grown at different densities. </li> <li>Our model shows that density-dependent fecundity and dispersal strongly affect all three population spread characteristics for both exponential and lognormal dispersal kernels. Spread rate and edge depth are strongly correlated but show weaker correlations with population continuity. Positive density-dependence of fecundity increases all three spread characteristics. Increasingly positive density-dependence of dispersal increases spread rate and edge depth but generally decreases population continuity. Density-dependent fecundity and dispersal are largely additive in their effect on spread characteristics. For population continuity, the joint effects of density-dependent fecundity and dispersal are somewhat contingent on the dispersal kernel.</li> <li>The common-garden experiment and the experimentally parameterized mechanistic dispersal model revealed density-dependent fecundity and dispersal across study species. All study species exhibited negative density-dependent fecundity, but they differed qualitatively in the density-dependence of dispersal distance and probability of long-distance dispersal. The negative density-dependence of fecundity and dispersal found for three species reinforced each other in reducing spread rate and edge depth. The positively density-dependent dispersal found for two species markedly increased spread rate and edge depth. Population continuity was hardly affected by population density in all study species except Crepis sancta in which it was strongly reduced by negatively density-dependent fecundity.</li> <li> Synthesis. Density-dependent fecundity and seed dispersal profoundly alter population spread. In particular, positively density-dependent dispersal should promote the spread and genetic diversity of plant populations migrating under climate change but also complicate the control of invasive species.</li> </ol>

publicMay 2020View details →

How does increasing mast seeding frequency affect population dynamics of seed consumers? Wild boar as a case study

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publicDec 2025View details →

Ecological dynamics of two seed predators, Araecerus levipennis and Acanthoscelides macrophthalmus, on Leucaena leucocephala

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publicMay 2023View details →

Density dependence of seed dispersal and fecundity profoundly alters the spread dynamics of plant populations

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publicSep 2021View details →

A non-native earthworm shifts the seed predation dynamics of a native weed

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opencc-zeroDec 2016View details →

Data from: Scatterhoarders drive long- and short-term population dynamics of a nut-producing tree, while pre-dispersal seed predators and herbivores have little effect

1.Both seed predators and herbivores can have profound effects on individual plant growth, reproduction and survival, but their population level effects are less well understood. While most plants interact with a suite of seed predators and herbivores over their life cycle, few studies incorporate the effects of multiple interacting partners and multiple life stages on plant population growth. 2.We constructed a matrix model using six years of data from a rare, seed-producing population of American chestnut (Castanea dentata). We combined field demographic data with published experimental results on the effects of pre-dispersal seed predators (weevils) and post-dispersal seed predators (scatter-hoarding vertebrates) and incorporated the effect of vertebrate herbivores estimated from the field data. We explored the impact of these three different animal interactions for short-term (transient) and long-term (asymptotic) tree population growth. In addition, we used the model to explore the conditions under which scatter-hoarding would function as a mutualism. 3.Seed predators had greater effect on both short- and long-term population growth than herbivores. Although weevil infestation can greatly reduce the probability of germination, pre-dispersal seed predators had smaller effects on both short- or long-term population growth than post-dispersal predators. The elasticities of weevil-related parameters were also small. The effect of browsers on both the short- and long-term population growth rate were the smallest of the effects studied. Post-dispersal seed predation affected population growth the most of the interactions studied. The probability of seed removal was amongst the largest elasticities, similar in magnitude to survival of large trees. 4.Synthesis Our results indicate that neither weevils nor the intensity of browse damage observed at our study site are likely to hinder tree regeneration or reintroduction, though both reduced population growth. Although researchers and forest managers often assume that seeds are unimportant for long-lived tree populations, our test of this assumption shows that scatterhoarders and other post-dispersal seed consumers can significantly limit natural regeneration. Forest management that alters scatterhoarder behaviour could have significant effects on tree population dynamics that are largely unexplored.

opencc-zeroNov 2019View details →

Data from: Proximity to roads disrupts rodents' contributions to seed dispersal services and subsequent recruitment dynamics

1. Seed dispersal and subsequent recruitment dynamics play a crucially important role in regulating species coexistence and structuring tree diversity in diverse forests. Wildlife, which can dually shape the tree recruitment process by simultaneously functioning as natural enemies and seed dispersers, are undergoing widespread changes in population and behaviour due to the detrimental effects of expanding global road networks. However, the impact of these changes on recruitment dynamics through the alteration of seed dispersal processes remains understudied. 2. Here, we assessed how roads affect animal-mediated recruitment dynamics from the seed to adult stages of trees using a ubiquitous vertebrate-seed model: rodents and acorns. To quantify the degree to which proximity to a road alters seed dispersal and regulates subsequent recruitment dynamics, we conducted seed dispersal and predation experiments, and investigated the natural recruitment of a dominant tree species (Quercus aliena) from September 2009 to July 2012 in a subtropical forest of central China. 3. Roads caused a decreased seed dispersal distance and increasing larder hoarding, demonstrating a weakened contribution of rodents to seed dispersal services. These patterns were stronger in the masting year than in the non-masting year. Correspondingly, seedlings, saplings, and adult trees had higher densities near roads than far from roads. Near roads, recruit transition rates (i.e., seedling-to-sapling and sapling-to-juvenile) were low, and the influential strength of conspecific adult density was weakened over these recruit transitions, indicating that space limitation and lottery competition, not conspecific negative density-dependent effects, approximately determined the near-road recruitment process. Furthermore, most adults near roads were young, and their ages matched the road ages. 4. Synthesis. Roads diminished animal-mediated seed dispersal services and disrupted subsequent recruitment dynamics in Q. aliena populations, thus weakening the key process promoting diversity in forest ecosystems. Given that these animal-regulated roles in recruitment dynamics are widespread stabilizing forces for tree coexistence, our findings suggest that the ubiquity of roads and their continued expansion will contribute to the increase in forests dominated by a relatively low number of species. Consequently, the growing expansion of road networks will ultimately cause profound changes in community composition and structure across diverse forests worldwide.

zenodo32/100

Data files for «Wood warbler population dynamics in response to mast seeding regimes in Europe», Journal: Ecology

AbstractMast seeding is the episodic, massive production of plant seeds synchronized over large areas. The resulting superabundance of seeds represents a resource pulse that can profoundly affect animal populations across trophic levels. Following years of high seed production, abundances of both seed consumers and their predators increase. Higher predator abundance leads to increased predation pressure across the trophic web, impacting non-seed consumers such as the wood warbler Phylloscopus sibilatrix through increased nest predation after tree mast years. Over the past 30 years, the frequency of tree seed masts has increased while wood warbler populations have declined in several regions of Europe. We hypothesised that increasing mast frequencies may have contributed to the observed population declines by creating suboptimal breeding conditions in years after masting. We measured reproductive output in four study areas in central Europe, which was between 0.61 and 1.24 fledglings lower in years following masting than non-masting. For each study area, we used matrix population models to predict population trends based on the estimated reproductive output and the local mast frequencies. We then compared the predicted with the observed population trends to assess if the frequency of mast years contributed to the population dynamics. In Wielkopolska National Park (PL) and Hessen (DE), masting occurred on average only every 4 years and populations were stable or nearly so, whereas in Jura (CH) and Białowieża National Park (PL), masting occurred every 2 and 2.5 years, respectively, and populations were declining. The simple matrix population models predicted the relative difference among local population trends over the past 10-20 years well, suggesting that the masting frequency may partly explain regional variation in population trends. Simulations suggest that further increases in mast frequency will lead to further declines in wood warbler populations. We show that changes in a natural process, such as mast seeding, may contribute to the declines of animal populations through cascading effects.

opencc-by-4.0Nov 2023View details →

opencc-zeroJan 2022View details →

Climate change reshapes the eco-evolutionary dynamics of a Neotropical seed dispersal system

Aim: Global changes will redistribute biodiversity, reshaping ecological interactions and ecosystem processes. The distribution decoupling of plants and their mutualistic seed dispersers, for instance, may have overlooked eco-evolutionary effects. How animal-dispersed plants will respond to changes in the distribution of their seed dispersers is, however, an open question. Here, we forecast the consequences of climate change and frugivory interactions for the spatial distribution and seed size evolution of a Neotropical palm. Location: Atlantic forests of South America Time period: Present day, end of 21st Century Major taxa studied: Thirty-two species of frugivorous birds, and a palm (Euterpe edulis). Methods: Future patterns of animal-plant co-occurrence were derived from ecological niche models, climate forecasts, projections of future forest loss, and seed dispersal simulations. We further explored the evolutionary effect of the spatial reorganization of interactions by modelling palm seed sizes as function of changes in the distribution of frugivore traits. Results: Our models indicate that future climate change and deforestation may reduce the palm's suitable distribution by 20-50%. However, our simulations suggest that 66% of all remaining future suitable distribution (76.200 km²) would still be inaccessible to the palm without the active dispersal of seeds by frugivores. In addition, novel frugivore communities are projected to have smaller body mass and gape size (-23% and -10%, respectively), due to the loss of large frugivores, which may translate into a 6–17% reduction of seed sizes across the palm's remaining distribution. Main conclusion: Our projections indicate that frugivore seed dispersal may be critical to allow occupancy of future habitat by the studied plant. However, loss of large frugivores may affect trait selection regimes, creating hotspots of plant evolution towards smaller seeds. We argue that such complex dynamics from species-specific responses to global change may drive the distribution and evolution of several interacting partners worldwide.

opencc-zeroMay 2024View details →

Data from: Boom and bust: The effects of masting on seed predator range dynamics and trophic cascades

Aim: Spatiotemporal variation in resource availability is a strong driver of animal distributions. In northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high elevation songbirds. Location: Northeastern United States Methods: We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in tree mast. We also assessed population responses using systematic auditory surveys analyzed with an open-population binomial mixture model. Further, we used modeled red squirrel abundance in nest-survival models to evaluate whether their abundance is correlated with daily nest survival of three songbird species. Results: Tree mast provided a critical resource pulse that resulted in a two-fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663 – 1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443 – 1545 m asl) after a large mast event. Daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels. Main Conclusions: Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.