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1. Water availability dictates how plant traits predict demographic rates

   https://doi.org/10.1002/ecy.3799  

   Stears, Alice E.; Adler, Peter B.; Blumenthal, Dana M.; Kray, Julie A.; Mueller, Kevin E.; Ocheltree, Troy W.; Wilcox, Kevin R.; Laughlin, Daniel C. (November 2022, Ecology)
2. Evolutionary tracking is determined by differential selection on demographic rates and density dependence

   https://doi.org/10.1002/ece3.6311  

   Vinton, Anna Christina; Vasseur, David Alan (June 2020, Ecology and Evolution)
3. Statistical Methods for Assessing Differences in False Non-Match Rates Across Demographic Groups

   Schuckers, M. (August 2022, 2022 International Conference on Pattern Recognition (ICPR))

   Biometric recognition is used across a variety of applications from cyber security to border security. Recent research has focused on ensuring biometric performance (false negatives and false positives) is fair across demographic groups. While there has been significant progress on the development of metrics, the evaluation of the performance across groups, and the mitigation of any problems, there has been little work incorporating statistical variation. This is important because differences among groups can be found by chance when no difference is present. In statistics this is called a Type I error. Differences among groups may be due to sampling variation or they may be due to actual difference in system performance. Discriminating between these two sources of error is essential for good decision making about fairness and equity. This paper presents two novel statistical approaches for assessing fairness across demographic groups. The first methodology is a bootstrapped-based hypothesis test, while the second is simpler test methodology focused upon non-statistical audience. For the latter we present the results of a simulation study about the relationship between the margin of error and factors such as number of subjects, number of attempts, correlation between attempts, underlying false non-match rates(FNMR's), and number of groups. 

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4. Statistical Methods for Assessing Differences in False Non-Match Rates Across Demographic Groups

   Schuckers, ME (August 2022, Proceedings of the Understanding and Mitigating Demographic Bias in Biometric Systems(UMDBB) Workshop of the International Conference on Pattern Recognition)
5. Integrative demographic modelling reduces uncertainty in estimated rates of species' historical range shifts

   https://doi.org/10.1111/jbi.14754  

   Castilla, Antonio R.; Brown, Alissa; Hoban, Sean; Abhainn, Everett Andrew; Robinson, John D.; Romero‐Severson, Jeanne; Smith, Adam B.; Strand, Allan E.; Tipton, John R.; Dawson, Andria (November 2023, Journal of Biogeography)

   Abstract AimBiogeographers have used three primary data types to examine shifts in tree ranges in response to past climate change: fossil pollen, genetic data and contemporary occurrences. Although recent efforts have explored formal integration of these types of data, we have limited understanding of how integration affects estimates of range shift rates and their uncertainty. We compared estimates of biotic velocity (i.e. rate of species' range shifts) using each data type independently to estimates obtained using integrated models. LocationEastern North America. TaxonFraxinus pennsylvanicaMarshall (green ash). MethodsUsing fossil pollen, genomic data and modern occurrence data, we estimated biotic velocities directly from 24 species distribution models (SDMs) and 200 pollen surfaces created with a novel Bayesian spatio‐temporal model. We compared biotic velocity from these analyses to estimates based on coupled demographic‐coalescent simulations and Approximate Bayesian Computation that combined fossil pollen and SDMs with population genomic data collected across theF. pennsylvanicarange. ResultsPatterns and magnitude of biotic velocity over time varied by the method used to estimate past range dynamics. Estimates based on fossil pollen yielded the highest rates of range movement. Overall, integrating genetic data with other data types in our simulation‐based framework reduced apparent uncertainty in biotic velocity estimates and resulted in greater similarity in estimates between SDM‐ and pollen‐integrated analyses. Main ConclusionsBy reducing uncertainty in our assessments of range shifts, integration of data types improves our understanding of the past distribution of species. Based on these results, we propose further steps to reach the integration of these three lines of biogeographical evidence into a unified analytical framework. 

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