Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size‐based survival) and spatial dynamics are important. We present a Python package, We demonstrate how Moving beyond our example system, we describe how
We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries,
- Award ID(s):
- 2126510
- NSF-PAR ID:
- 10375826
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Paleoceanography and Paleoclimatology
- Volume:
- 37
- Issue:
- 10
- ISSN:
- 2572-4517
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract MetaIPM , which places IPMs into a metapopulation framework, and allow users to readily construct and apply these models that combine local population dynamics within a metapopulation framework.MetaIPM includes an IPM for each subpopulation that is connected to other subpopulations via a metapopulation movement model. These movements can include dispersion, migration or other patterns. The IPM can include for size‐specific demographic rates (e.g. survival, recruitment) as well as management actions, such as length‐based harvest (e.g. gear specific capture sizes, varying slot limits across political boundaries). The model also allows for changes in metapopulation connectivity between locations, such as a fish passage ladders to enhance movement or deterrents to reduce movement. Thus, resource managers can useMetaIPM to compare different management actions such as the harvest gear type (which can be length‐specific) and harvest locations.MetaIPM may be applied to inform managers seeking to limit the spread of an invasive species in a system with important metapopulation dynamics. Specifically, we compared removal lengths (all length fish versus longer fish only) for an invasive fish population in a fragmented, inland river system.MetaIPM allowed users to compare the importance of harvesting source populations away from the invasion front, as well as species at the invasion front. The model would also allow for future comparisons of different deterrent placement locations in the system.MetaIPM can be applied to other species, systems and management approaches. TheMetaIPM packages includes Jupyter Notebooks documenting the package as well as a second set of JupyterNotebooks showing the application of the package to our example system. -
Abstract Paleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate quantities to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructions of past climate. We develop and apply a data assimilation uncertainty quantification approach to paleoclimate networks and observational uncertainties representative of data for the last millennium. We find that structural uncertainties arising from uncertain spatial covariance relationships typically contribute 10% of the total uncertainty in reconstructed temperature variability at small (
∼ 200 km), continental, and hemispheric length scales, with larger errors (50% or larger) in regions where long‐range climate covariances are least certain. These structural uncertainties contribute far more to errors in uncertainty quantification, sometimes by a factor of 5 or higher. Accounting for and reducing uncertainties in climate model dynamics and resulting covariance relationships will improve paleoclimate reconstruction accuracy. -
Abstract Comprehensive, time‐scaled phylogenies provide a critical resource for many questions in ecology, evolution and biodiversity. Methodological advances have increased the breadth of taxonomic coverage in phylogenetic data; however, accessing and reusing these data remain challenging.
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McHenry, K ; Schreiber, L (Ed.)The paleogeosciences are becoming more and more interdisciplinary, and studies increasingly rely on large collections of data derived from multiple data repositories. Integrating diverse datasets from multiple sources into complex workflows increases the challenge of creating reproducible and open science, as data formats and tools are often noninteroperable, requiring manual manipulation of data into standardized formats, resulting in a disconnect in data provenance and confounding reproducibility. Here we present a notebook that demonstrates how the Linked PaleoData (LiPD) framework is used as an interchange format to allow data from multiple data sources to be integrated in a complex workflow using emerging packages in R for geochronological uncertainty quantification and abrupt change detection. Specifically, in this notebook, we use the neotoma2 and lipdR packages to access paleoecological data from the Neotoma Database, and paleoclimate data from compilations hosted on Lipdverse. Age uncertainties for datasets from both sources are then quantified using the geoChronR package, and those data, and their associated age uncertainties, are then investigated for abrupt changes using the actR package, with accompanying visualizations. The result is an integrated, reproducible workflow in R that demonstrates how this complex series of multisource data integration, analysis and visualization can be integrated into an efficient, open scientific narrative.more » « less
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