Hanley, BrendaDennis, BrianKramer, DavidSchuler, Krysten2019-06-042019-06-042019https://hdl.handle.net/1813/66166This software is being shared under a MIT license. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.Population matrix models are used to assess population viability (Caswell, 2001). Given a fully parameterized population matrix model, managers may use these matrices to inventory the population, diagnose issues with population viability, investigate hypothetical management activities, or prescribe action items to stem or reverse population decline. But what if managers do not have the data to fully parameterize a matrix model? This work explores the use of a combinatorial optimization algorithm (Korte & Vygen, 2018) in backfilling the parameters of a matrix model given adult time series data (Ding et al. 2008). The method is explored using simulated adult trajectories of Northern Spotted Owl (Noon & Biles, 1990), with several noise types and variance stabilizing transformations (Dennis et al., 2001). The algorithm performance is compared against the performance of conditional least squares (Klimko & Nelson, 1978) and ordinary least squares (Fox, 2016), and all are compared against truth. The algorithm assumes a deterministic matrix, that the matrix elements remain static over the 29-year time periods, and that the time series data is free from sampling error.en-USasymptotic growth ratecombinatorial optimization algorithmconservationecologyeigenvalueLefkovitch matrixLeslie matrixNorthern Spotted Owlpopulation dynamicspopulation modelingpopulation matrix modelssuperparameterstime series datadatasethttps://doi.org/10.7298/a416-v747