Periodic Reporting for period 1 - STAMP (Spatiotemporal Analytical Modelling for Paleobiology)
Periodo di rendicontazione: 2023-07-01 al 2025-12-31
STAMP is a project for modelling our ecological past and understanding paleo-ecological processes using integrative methods that combine insights from across different angles of analysis. Current approaches to ecological modelling of species distributions, including species ranges and patterns of connectivity, rely on present-day data. Increasing availability of fossil or pollen record presence data as well as the detection of DNA from specific species in sedimentary cores provide two additional sources of information about where species may live, or might have lived in the past. Yet, these sources of information are rarely used together. Lack of tools for combining this different data types is one reason for this, as well as the absence of an overall research program that servers to collate various types of data into a single standardized framework. The Spatiotemporal Analytical Modelling for Paleobiology (STAMP) ERC research grant is making headways into this endeavor, by developing tools for inference of demographic processes and species ranges using information from disparate data types, as well as collecting new data from different data sources, particularly those sources that are as yet under-represented in multi-way comparisons, namely sedimentary and ancient DNA. As case studies, we focus on inference of species dynamics in three different context across over the past 30,000 years: 1) the reconstruction of megafauna species ranges on land, in the north hemisphere, across the late Pleistocene and Holocene; 2) the reconstruction of boreal paleo-forest dynamics with a focus on what is now Alaska and California; 3) the study of the historical resilience and mobility of arctic marine mammals in the North Atlantic, with a focus on bowhead whales. These empirical projects are being supported by the computational development of a spatiotemporal toolbox for analysis: including methods based on descriptive spatiotemporal modelling and on dynamic simulation-based inference.
We now have a full team working towards the objectives of the grant and have hit several milestones, as well as encountered several challenges in the course of the last years.
First, in WP1, we are using a combination of paleo-datasets to understand the dynamics of megafauna species range and dispersal, with a focus on the late Pleistocene and Holocene. The postdoc hired for this project now has a rough draft of a paper that we will submit as an output of WP1. In it, he uses existing data to compare paleo-ecological niche models that include sedimentary DNA data only, fossil data only, present-day data only, or different combinations of these three sets. We have been assessing model fit as well as predictive performance, to determine the strengths and weaknesses of these different data sources, and are working to find the best way to combine these so as to develop a method to efficiently estimate species distributions from them. On the more empirical side of WP1, we have already obtained DNA data from six cores from Alaska, to identify megafauna that may have been present in the region. At the moment, we are carrying out read mapping and species classification to figure out what species are present in the various cores at different age depths.
As part of WP2, we are investigating the evolutionary history and genetic landscape of paleo-forests. While we wait for the Alaskan data to be species-identified, we are focusing on another dataset from an area in what is currently California, with a specific goal of studying the ecological dynamics of Sequoiadendron giganteum (giant sequoia) within the broader Cupressaceae family as a keystone forest species, for which environmental DNA reads have been sequenced from sedimentary layers. We have found that sequoia reads, while present throughout the cores, are not as abundant as one would expect from the present-day distribution of the species in the region. At the moment, we are hoping to turn this into a taphonomic study on challenges to environment DNA preservation, in combination with two other datasets (one focusing on sedimentary DNA reads from salmon and another from coral) in which we see similar patterns.
We have also been working to integrate paleo-data to understand marine species dynamics. The DNA technician hired for this WP has already finished with DNA extraction and sequencing. We are currently being bioinformatically processing DNA sequencing results from bowhead whales, for mapping and genotyping, and these will soon be ready for analysis. We have chosen to prioritize bowhead whales over the two other species we had originally sought to include in WP3 (narwhal and polar bear) as the latter have poor DNA preservation results from a pilot analysis, and we are able to get a lot more sequencing reads from bowhead whales that covers a broad spatial and temporal ranges. This adds to ongoing work investigating how whaling has affected the population stability and geographic range of whales across the North Atlantic. An analysis on pilot data (significantly smaller than the one we are generating now) has recently resulted in a preprint that is currently under review.
A newly hired postdoc for WP4 and WP5 is working on a method to simulate spatiotemporal genomic data using ecological niche models as input. We have also been developing a simulation package for geographically-explicit simulations, and have also developed a new companion package for doing simulation-based inferences of spatio-temporal demographic parameters using summary statistics (from either genomic or non-genomic data). The simulation package is now already published and placed into a repository, while the inference package is available for download as a beta version, and a manuscript for it has been submitted for peer review. We expect to be able to deploy these methods on some of the datasets from the other work packages soon, particularly on the mammal marine samples, and possibly also the paleobotanical samples.
First, in WP1, we are using a combination of paleo-datasets to understand the dynamics of megafauna species range and dispersal, with a focus on the late Pleistocene and Holocene. The postdoc hired for this project now has a rough draft of a paper that we will submit as an output of WP1. In it, he uses existing data to compare paleo-ecological niche models that include sedimentary DNA data only, fossil data only, present-day data only, or different combinations of these three sets. We have been assessing model fit as well as predictive performance, to determine the strengths and weaknesses of these different data sources, and are working to find the best way to combine these so as to develop a method to efficiently estimate species distributions from them. On the more empirical side of WP1, we have already obtained DNA data from six cores from Alaska, to identify megafauna that may have been present in the region. At the moment, we are carrying out read mapping and species classification to figure out what species are present in the various cores at different age depths.
As part of WP2, we are investigating the evolutionary history and genetic landscape of paleo-forests. While we wait for the Alaskan data to be species-identified, we are focusing on another dataset from an area in what is currently California, with a specific goal of studying the ecological dynamics of Sequoiadendron giganteum (giant sequoia) within the broader Cupressaceae family as a keystone forest species, for which environmental DNA reads have been sequenced from sedimentary layers. We have found that sequoia reads, while present throughout the cores, are not as abundant as one would expect from the present-day distribution of the species in the region. At the moment, we are hoping to turn this into a taphonomic study on challenges to environment DNA preservation, in combination with two other datasets (one focusing on sedimentary DNA reads from salmon and another from coral) in which we see similar patterns.
We have also been working to integrate paleo-data to understand marine species dynamics. The DNA technician hired for this WP has already finished with DNA extraction and sequencing. We are currently being bioinformatically processing DNA sequencing results from bowhead whales, for mapping and genotyping, and these will soon be ready for analysis. We have chosen to prioritize bowhead whales over the two other species we had originally sought to include in WP3 (narwhal and polar bear) as the latter have poor DNA preservation results from a pilot analysis, and we are able to get a lot more sequencing reads from bowhead whales that covers a broad spatial and temporal ranges. This adds to ongoing work investigating how whaling has affected the population stability and geographic range of whales across the North Atlantic. An analysis on pilot data (significantly smaller than the one we are generating now) has recently resulted in a preprint that is currently under review.
A newly hired postdoc for WP4 and WP5 is working on a method to simulate spatiotemporal genomic data using ecological niche models as input. We have also been developing a simulation package for geographically-explicit simulations, and have also developed a new companion package for doing simulation-based inferences of spatio-temporal demographic parameters using summary statistics (from either genomic or non-genomic data). The simulation package is now already published and placed into a repository, while the inference package is available for download as a beta version, and a manuscript for it has been submitted for peer review. We expect to be able to deploy these methods on some of the datasets from the other work packages soon, particularly on the mammal marine samples, and possibly also the paleobotanical samples.
The most promising results at the moment are coming from WP1 and WP5. In WP1, we are learning of the power of using ancient data to better inform species niche ecological models, beyond the information that is only available from present-day data, especially once these data are explicitly incorporated into these models while accounting for its idiosyncracies (under-sampling, presence vs. presence-absence, over-dispersion, etc). Further research will involve ascertaining how generalizable these results are to other species beyond our reindeer case-study. When it comes to demographic inference, as part of WP5, we have a widely used R package (slendr) which now benefits from its complementarity with a new powerful engine for inference - demografr - which is currently also an R package. We are finding we have the ability to carry out powerful spatiotemporal demographic inferences using test data under this package. Once the sequence data is available for the arctic marine mammals, we plan to deploy it on it to understand mobility patterns of these speices using summary statistics obtained from both ancient DNA and fossil presence data. The key challenge there will be to see how much sequence data we can obtain, and which summary statistics are most informative for the questions about bowhead whale dynamics (including responses to climatic changes, traditional whaling and industrial-scale whaling) that we seek to answer.