scholarly journals AlleleShift: An R package to predict and visualize population-level changes in allele frequencies in response to climate change

2021 ◽  
Author(s):  
Roeland Kindt
Keyword(s):  
Climate Change ◽  
Environmental Gradients ◽  
Additive Model ◽  
Population Level ◽  
R Package ◽  
Allele Frequencies ◽  
Data Sets ◽  
Climate Data ◽  
Constrained Ordination ◽  
And Migration

AbstractBackgroundAt any particular location, frequencies of alleles in organisms that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at populations’ locations.MethodsThe prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). My methodology of AlleleShift is also different in modelling and predicting allele counts through constrained ordination (not frequencies as in the CCA approach) and modelling both alleles for a locus (not solely the minor allele as in the CCA method; both methods were developed for diploid organisms where individuals are homozygous (AA or BB) or heterozygous (AB)). Whereas the GAM step ensures that allele frequencies are in the range of 0 to 1 (negative values are sometimes predicted by the RDA and CCA approaches), the RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). The AlleleShift::amova.rda enables users to verify that the same ‘mean-square’ values are calculated by AMOVA and RDA, and gives the same final statistics with balanced data.ResultsBesides data sets with predicted frequencies, AlleleShift provides several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These include ‘dot plot’ graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), ‘waffle’ diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplementary materials. In addition, graphical methods are provided of showing shifts of populations in environmental space (population.shift) and to assess how well the predicted frequencies reflect the original frequencies for the baseline climate (freq.ggplot).AvailabilityAlleleShift is available as an open-source R package from https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

scholarly journals AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11534
Author(s):  
Roeland Kindt
Keyword(s):  
Climate Change ◽  
Environmental Gradients ◽  
Additive Model ◽  
Population Level ◽  
R Package ◽  
Allele Frequencies ◽  
Data Sets ◽  
Climate Data ◽  
Link Type ◽  
And Migration

Background At any particular location, frequencies of alleles that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at the population level. Methods The prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). The RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). Because the RDA step or CCA approach sometimes predict negative allele frequencies, the GAM step ensures that allele frequencies are in the range of 0 to 1. Results AlleleShift provides data sets with predicted frequencies and several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These visualizations include ‘dot plot’ graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), ‘waffle’ diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these visualizations were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplemental videos. Availability AlleleShift is available as an open-source R package from https://cran.r-project.org/package=AlleleShift and https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

scholarly journals AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal

2022 ◽  
Vol 7 ◽  
Author(s):  
Ibrahima Hathie ◽  
Dilys MacCarthy ◽  
Bright Freduah ◽  
Mouhamed Ly ◽  
Ahmadou Ly ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Integrated Assessment ◽  
Future Climate ◽  
Sub Saharan Africa ◽  
Agricultural System ◽  
Data Sets ◽  
Agricultural Systems ◽  
Climate Data ◽  
Improvement Project

The Agricultural Model Intercomparison and Improvement Project (AgMIP) developed protocol-based methods for Regional Integrated Assessment (RIA) of agricultural systems. These methods have been applied by teams of scientists working with regional and national stakeholders across Sub-Saharan Africa and South Asia. This paper describes the data sets that were used to implement the AgMIP RIA methods for the Nioro region of Senegal. The goal of the RIA is to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin comprised of peanut, millet, maize and other minor crops and livestock, and to assess adaptations of that system to climate change, under current as well as future climate and socio-economic conditions. The data sets include: the Representative Agricultural Pathways (RAPs) developed for Nioro from 2000-2050; climate data used to implement crop yield simulations; the data used to parameterize the Agricultural Production Systems sIMulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop models, which include historical climate data and future climate scenarios; and the data used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA-MD) economic simulation model. The analysis is structured around four AgMIP “core questions'' of climate impact assessment.

scholarly journals Ten Priority Science Gaps in Assessing Climate Data Record Quality

2019 ◽  
Vol 11 (8) ◽  
pp. 986 ◽  
Author(s):  
Joanne Nightingale ◽  
Jonathan P.D. Mittaz ◽  
Sarah Douglas ◽  
Dick Dee ◽  
James Ryder ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Data Sets ◽  
Climate Data ◽  
Science Knowledge ◽  
Knowledge Gaps ◽  
Quality Aspects ◽  
Data Products ◽  
In Situ Observations

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union’s Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons. This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described. The development of the EQC framework has highlighted cross-domain as well as ECV specific science knowledge gaps in relation to addressing the quality of climate data sets derived from satellite and in situ observations. We discuss 10 common priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and provide users with the range of information necessary to confidently select relevant products for their specific application.

scholarly journals Impact of Climate Change on Wine Tourism: An Approach through Social Media Data

2021 ◽  
Vol 13 (13) ◽  
pp. 7489
Author(s):  
Veronica Alampi Sottini ◽  
Elena Barbierato ◽  
Iacopo Bernetti ◽  
Irene Capecchi
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Additive Model ◽  
Climatic Conditions ◽  
Climate Data ◽  
Public And Private ◽  
Wine Tourism ◽  
Public And Private Stakeholders ◽  
Using Data ◽  
Tourism Flows

Wine tourism is one of the best opportunities for rural development, but because it is partially exposed to climatic conditions, it is a climate-vulnerable tourism activity. However, an understanding of the potential impacts of global climate change on this popular activity remains limited. This study proposes a new methodology that combines current daily gridded climate data from the E-OBS project with big spatiotemporal data from the Flickr photo-sharing platform through a generalized additive model This methodology was implemented to study the potential impacts on tourism flows due to climate change and to make predictions about the future using data from the CMIP5 project. We applied the methodology to 5 European wine tourism regions: Alsace (FR), Chianti (IT), La Rioja (SP), Langhe-Monferrato (IT), and Moselle (DE). Results show an increased probability of presence and increased deseasonalization of tourism in all study areas and an anticipation of peak presence from summer to spring in three of the five regions. We believe that these results can be useful for public and private stakeholders to adapt the offer of wine tourism services to changes in demand and to direct the organization of events such as festivals and thematic tours.

scholarly journals climwin: An R Toolbox for Climate Window Analysis

2016 ◽  
Author(s):  
Liam D. Bailey ◽  
Martijn van de Pol
Keyword(s):  
Climate Change ◽  
Worked Examples ◽  
R Package ◽  
Information Criteria ◽  
Future Climate Change ◽  
Type I ◽  
Climate Data ◽  
Spring Temperature ◽  
Response Variable ◽  
Small Set

AbstractWhen studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

scholarly journals Is it feasible to estimate radiosonde biases from interlaced measurements?

2018 ◽  
Vol 11 (5) ◽  
pp. 3021-3029
Author(s):  
Stefanie Kremser ◽  
Jordis S. Tradowsky ◽  
Henning W. Rust ◽  
Greg E. Bodeker
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Synthetic Data ◽  
Internal Variability ◽  
Data Series ◽  
Data Sets ◽  
Climate Variables ◽  
Climate Data ◽  
Reference Quality ◽  
New Instruments

Abstract. Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and as such are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure, and humidity, together with well-characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive at measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

scholarly journals Assessing Effects of Variation in Global Climate Data Sets on Spatial Predictions from Climate Envelope Models

2014 ◽  
Vol 5 (1) ◽  
pp. 14-25 ◽  
Author(s):  
James I. Watling ◽  
Robert J. Fletcher ◽  
Carolina Speroterra ◽  
David N. Bucklin ◽  
Laura A. Brandt ◽  
...  
Keyword(s):  
Climate Change ◽  
Initial Conditions ◽  
Model Performance ◽  
Future Climate ◽  
Data Sets ◽  
Climate Data ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Climate Envelope ◽  
Contemporary Climate

Abstract Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

scholarly journals Population demography maintains biogeographic boundaries

2022 ◽  
Author(s):  
Chloé Schmidt ◽  
Gabriel Muñoz ◽  
Lesley T Lancaster ◽  
Jean-Philippe Lessard ◽  
Katharine A Marske ◽  
...  
Keyword(s):  
Adaptive Capacity ◽  
Environmental Gradients ◽  
Mammalian Species ◽  
Population Level ◽  
Data Sets ◽  
Population Demography ◽  
Effective Population ◽  
Transition Zones ◽  
Biogeographic Regions ◽  
Global Biodiversity

Global biodiversity is organized into biogeographic regions that comprise distinct biotas. The contemporary factors maintaining differences in species composition between biogeographic regions are poorly understood. Given the evidence that populations with sufficient genetic variation can adapt to fill new habitats, it is surprising that we do not see more homogenization of species assemblages among regions. Theory suggests that the expansion of populations across biogeographic transition zones could be limited by environmental gradients that affect population demography in ways that could limit adaptive capacity, but this has not been empirically explored. Using three independently curated data sets describing continental patterns of mammalian demography and population genetics, we show that populations closer to biogeographic transition zones have lower effective population sizes and genetic diversity, and are more genetically differentiated. These patterns are consistent with reduced adaptive capacity near biogeographic transition zones. The consistency of these patterns across mammalian species suggests they are stable, predictable, and generalizable in their contribution to long-term limits on expansion and homogenization of biodiversity across biogeographic transition zones. Understanding the contemporary processes acting on populations that maintain differences in the composition of regional biotas is crucial for our basic understanding of the current and future organization of global biodiversity. The importance of contemporary, population-level processes on the maintenance of global biogeographic regions suggests that biogeographic boundaries are susceptible to environmental perturbation associated with human-caused global change.

scholarly journals Is it feasible to estimate radiosonde biases from interlaced measurements?

2018 ◽  
Author(s):  
Stefanie Kremser ◽  
Jordis S. Tradowsky ◽  
Henning W. Rust ◽  
Greg E. Bodeker
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Synthetic Data ◽  
Internal Variability ◽  
Data Series ◽  
Data Sets ◽  
Climate Variables ◽  
Climate Data ◽  
Reference Quality ◽  
New Instruments

Abstract. Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and, as such, are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure and humidity, together with well characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

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