Publications

This page lists publications from my research group. You can also find my publications on Google Scholar.

References

Hong, J., Stoudt, S., & de Valpine, P. (2025). Fast maximum likelihood estimation for general hierarchical models. Journal of Applied Statistics, 52(3), 595–623. https://doi.org/10.1080/02664763.2024.2383284

Milleret, C., Dupont, P., Dey, S., Brøseth, H., Kindberg, J., Turek, D., de Valpine, P., Åkesson, M., Wabakken, P., Zimmermann, B., & Bischof, R. (2025). Map of death: Spatially explicit mortality of the grey wolf. Proceedings of the Royal Society B: Biological Sciences, 292(2053), 20250948. https://doi.org/10.1098/rspb.2025.0948

Norman, K. E. A., de Valpine, P., & Boettiger, C. (2025). No General Trend in Functional Diversity in Bird and Mammal Communities Despite Compositional Change. Global Ecology and Biogeography, 34(1), e13950. https://doi.org/10.1111/geb.13950

Paganin, S., & de Valpine, P. (2025). Computational Methods for Fast Bayesian Model Assessment via Calibrated Posterior p-values. Journal of Computational and Graphical Statistics, 34(2), 462–473. https://doi.org/10.1080/10618600.2024.2374585

Clare, J. D. J., de Valpine, P., Moanga, D. A., Tingley, M. W., & Beissinger, S. R. (2024). A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change. Global Change Biology, 30(1), e17019. https://doi.org/10.1111/gcb.17019

Foster, D. E., Stephens, S. S., de Valpine, P., & Battles, J. J. (2024). Threats to the persistence of sugar pine (Pinus Lambertiana) in the western USA. Forest Ecology and Management, 554, 121659. https://doi.org/10.1016/j.foreco.2023.121659

Goldstein, B. R., Furnas, B. J., Calhoun, K. L., Larsen, A. E., Karp, D. S., & de Valpine, P. (2024). Drought influences habitat associations and abundances of birds in California’s Central Valley. Diversity and Distributions, 30(5), e13827. https://doi.org/10.1111/ddi.13827

Goldstein, B. R., Keller, A. G., Calhoun, K. L., Barker, K. J., Montealegre-Mora, F., Serota, M. W., Van Scoyoc, A., Parker-Shames, P., Andreozzi, C. L., & de Valpine, P. (2024). How do ecologists estimate occupancy in practice? Ecography, n/a(n/a), e07402. https://doi.org/10.1111/ecog.07402

Beissinger, S. R., MacLean, S. A., Iknayan, K. J., & de Valpine, P. (2023). Concordant and opposing effects of climate and land-use change on avian assemblages in California’s most transformed landscapes. Science Advances, 9(8), eabn0250. https://doi.org/10.1126/sciadv.abn0250

Milleret, C., Dey, S., Dupont, P., Brøseth, H., Turek, D., de Valpine, P., & Bischof, R. (2023). Estimating spatially variable and density-dependent survival using open-population spatial capture–recapture models. Ecology, 104(2), e3934. https://doi.org/10.1002/ecy.3934

Newman, K., King, R., Elvira, V., de Valpine, P., McCrea, R. S., & Morgan, B. J. T. (2023). State-space models for ecological time-series data: Practical model-fitting. Methods in Ecology and Evolution, 14(1), 26–42. https://doi.org/10.1111/2041-210X.13833

Paganin, S., Paciorek, C. J., Wehrhahn, C., Rodríguez, A., Rabe-Hesketh, S., & de Valpine, P. (2023). Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models. Journal of Educational and Behavioral Statistics, 48(2), 147–188. https://doi.org/10.3102/10769986221136105

Stoudt, S., de Valpine, P., & Fithian, W. (2023). Nonparametric Identifiability in Species Distribution and Abundance Models: Why it Matters and How to Diagnose a Lack of it Using Simulation. Journal of Statistical Theory and Practice, 17(3), 39. https://doi.org/10.1007/s42519-023-00336-5

Zhang, W., Chipperfield, J. D., Illian, J. B., Dupont, P., Milleret, C., de Valpine, P., & Bischof, R. (2023). A flexible and efficient Bayesian implementation of point process models for spatial capture–recapture data. Ecology, 104(1), e3887. https://doi.org/10.1002/ecy.3887

Beck, M. W., de Valpine, P., Murphy, R., Wren, I., Chelsky, A., Foley, M., & Senn, D. B. (2022). Multi-scale trend analysis of water quality using error propagation of generalized additive models. Science of The Total Environment, 802, 149927. https://doi.org/10.1016/j.scitotenv.2021.149927

Campbell, H., de Valpine, P., Maxwell, L., Jong, V. M. T. de, Debray, T. P. A., Jaenisch, T., & Gustafson, P. (2022). Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic. The Annals of Applied Statistics, 16(1), 436–459. https://doi.org/10.1214/21-AOAS1499

de Valpine, P., Paganin, S., & Turek, D. (2022). compareMCMCs: An R package for studying MCMC efficiency. Journal of Open Source Software, 7(69), 3844. https://doi.org/10.21105/joss.03844

Fung, Y. L., Newman, K., King, R., & de Valpine, P. (2022). Building integral projection models with nonindependent vital rates. Ecology and Evolution, 12(3), e8682. https://doi.org/10.1002/ece3.8682

Goldstein, B. R., & de Valpine, P. (2022). Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset. Scientific Reports, 12(1), 12276. https://doi.org/10.1038/s41598-022-16368-z

Hong, J., Karaoz, U., de Valpine, P., & Fithian, W. (2022). To rarefy or not to rarefy: Robustness and efficiency trade-offs of rarefying microbiome data. Bioinformatics, 38(9), 2389–2396. https://doi.org/10.1093/bioinformatics/btac127

Levine, J. I., Collins, B. M., Steel, Z. L., de Valpine, P., & Stephens, S. L. (2022). Higher incidence of high-severity fire in and near industrially managed forests. Frontiers in Ecology and the Environment, 20(7), 397–404. https://doi.org/10.1002/fee.2499

Paganin, S., Paciorek, C. J., Wehrhahn, C., Rodríguez, A., Rabe-Hesketh, S., & de Valpine, P. (2022). Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models. Journal of Educational and Behavioral Statistics, 10769986221136105. https://doi.org/10.3102/10769986221136105

Stoudt, S., Goldstein, B. R., & de Valpine, P. (2022). Identifying engaging bird species and traits with community science observations. Proceedings of the National Academy of Sciences, 119(16), e2110156119. https://doi.org/10.1073/pnas.2110156119

Levine, J., de Valpine, P., & Battles, J. (2021). Generalized additive models reveal among-stand variation in live tree biomass equations. Canadian Journal of Forest Research, 51(4), 546–564. https://doi.org/10.1139/cjfr-2020-0219

Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., & Nguyen, D. (2021). Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages. Journal of Statistical Software, 100, 1–39. https://doi.org/10.18637/jss.v100.i03

Socolar, Y., Goldstein, B. R., de Valpine, P., & Bowles, T. M. (2021). Biophysical and policy factors predict simplified crop rotations in the US Midwest. Environmental Research Letters, 16(5), 054045. https://doi.org/10.1088/1748-9326/abf9ca

Turek, D., Milleret, C., Ergon, T., Brøseth, H., Dupont, P., Bischof, R., & de Valpine, P. (2021). Efficient estimation of large-scale spatial capture–recapture models. Ecosphere, 12(2), e03385. https://doi.org/10.1002/ecs2.3385

Bischof, R., Milleret, C., Dupont, P., Chipperfield, J., Tourani, M., Ordiz, A., de Valpine, P., Turek, D., Royle, J. A., Gimenez, O., Flagstad, Ø., Åkesson, M., Svensson, L., Brøseth, H., & Kindberg, J. (2020). Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring. Proceedings of the National Academy of Sciences, 117(48), 30531–30538. https://doi.org/10.1073/pnas.2011383117

Milleret, C., Dupont, P., Chipperfield, J., Turek, D., Brøseth, H., Gimenez, O., de Valpine, P., & Bischof, R. (2020). Estimating abundance with interruptions in data collection using open population spatial capture–recapture models. Ecosphere, 11(7), e03172. https://doi.org/10.1002/ecs2.3172

Nguyen, D., de Valpine, P., Atchade, Y., Turek, D., Michaud, N., & Paciorek, C. (2020). Nested Adaptation of MCMC Algorithms. Bayesian Analysis, 15(4), 1323–1343. https://doi.org/10.1214/19-BA1190

Ponisio, L. C., de Valpine, P., Michaud, N., & Turek, D. (2020). One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE. Ecology and Evolution, 10(5), 2385–2416. https://doi.org/10.1002/ece3.6053

Anderson, M. J., de Valpine, P., Punnett, A., & Miller, A. E. (2019). A pathway for multivariate analysis of ecological communities using copulas. Ecology and Evolution, 9(6), 3276–3294. https://doi.org/10.1002/ece3.4948

Ponisio, L. C., de Valpine, P., M’Gonigle, L. K., & Kremen, C. (2019). Proximity of restored hedgerows interacts with local floral diversity and species’ traits to shape long-term pollinator metacommunity dynamics. Ecology Letters, 22(7), 1048–1060. https://doi.org/10.1111/ele.13257

Dougherty, E. R., de Valpine, P., Carlson, C. J., Blackburn, J. K., & Getz, W. M. (2018). Commentary to: A cross-validation-based approach for delimiting reliable home range estimates. Movement Ecology, 6(1), 10. https://doi.org/10.1186/s40462-018-0128-2

MacLean, S. A., Rios Dominguez, A. F., de Valpine, P., & Beissinger, S. R. (2018). A century of climate and land-use change cause species turnover without loss of beta diversity in California’s Central Valley. Global Change Biology, 24(12), 5882–5894. https://doi.org/10.1111/gcb.14458

Salazar, D., Lokvam, J., Mesones, I., Vásquez Pilco, M., Ayarza Zuñiga, J. M., de Valpine, P., & Fine, P. V. A. (2018). Origin and maintenance of chemical diversity in a species-rich tropical tree lineage. Nature Ecology & Evolution, 2(6), 983–990. https://doi.org/10.1038/s41559-018-0552-0

de Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Lang, D. T., & Bodik, R. (2017). Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE. Journal of Computational and Graphical Statistics, 26(2), 403–413. https://doi.org/10.1080/10618600.2016.1172487

Frishkoff, L. O., de Valpine, P., & M’Gonigle, L. K. (2017). Phylogenetic occupancy models integrate imperfect detection and phylogenetic signal to analyze community structure. Ecology, 98(1), 198–210. https://doi.org/10.1002/ecy.1631

Harmon-Threatt, A. N., de Valpine, P., & Kremen, C. (2017). Estimating resource preferences of a native bumblebee: The effects of availability and use–availability models on preference estimates. Oikos, 126(5), 633–641. https://doi.org/10.1111/oik.03550

Kueppers, L. M., Conlisk, E., Castanha, C., Moyes, A. B., Germino, M. J., de Valpine, P., Torn, M. S., & Mitton, J. B. (2017). Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest. Global Change Biology, 23(6), 2383–2395. https://doi.org/10.1111/gcb.13561

Turek, D., de Valpine, P., Paciorek, C. J., & Anderson-Bergman, C. (2017). Automated Parameter Blocking for Efficient Markov Chain Monte Carlo Sampling. Bayesian Analysis, 12(2), 465–490. https://doi.org/10.1214/16-BA1008

Eitzel, M. V., Kelly, M., Dronova, I., Valachovic, Y., Quinn-Davidson, L., Solera, J., & de Valpine, P. (2016). Challenges and opportunities in synthesizing historical geospatial data using statistical models. Ecological Informatics, 31, 100–111. https://doi.org/10.1016/j.ecoinf.2015.11.011

Knape, J., & de Valpine, P. (2016). Monte Carlo estimation of stage structured development from cohort data. Ecology, 97(4), 992–1002. https://doi.org/10.1890/15-0942.1

Nuccio, E. E., Anderson-Furgeson, J., Estera, K. Y., Pett-Ridge, J., de Valpine, P., Brodie, E. L., & Firestone, M. K. (2016). Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology, 97(5), 1307–1318. https://doi.org/10.1890/15-0882.1

Turek, D., de Valpine, P., & Paciorek, C. J. (2016). Efficient Markov chain Monte Carlo sampling for hierarchical hidden Markov models. Environmental and Ecological Statistics, 23(4), 549–564. https://doi.org/10.1007/s10651-016-0353-z

de Valpine, P., & Knape, J. (2015). Estimation of General Multistage Models From Cohort Data. Journal of Agricultural, Biological, and Environmental Statistics, 20(1), 140–155. https://doi.org/10.1007/s13253-014-0189-7

Eitzel, M. V., Battles, J., York, R., & de Valpine, P. (2015). Can’t see the trees for the forest: Complex factors influence tree survival in a temperate second growth forest. Ecosphere, 6(11), art247. https://doi.org/10.1890/ES15-00105.1

Ponisio, L. C., M’Gonigle, L. K., Mace, K. C., Palomino, J., de Valpine, P., & Kremen, C. (2015). Diversification practices reduce organic to conventional yield gap. Proceedings of the Royal Society B: Biological Sciences, 282(1799), 20141396. https://doi.org/10.1098/rspb.2014.1396

de Valpine, P. (2014). The common sense of P values. Ecology, 95(3), 617–621. https://www.jstor.org/stable/43495186

de Valpine, P., Scranton, K., Knape, J., Ram, K., & Mills, N. J. (2014). The importance of individual developmental variation in stage-structured population models. Ecology Letters, 17(8), 1026–1038. https://doi.org/10.1111/ele.12290

Gimenez, O., Buckland, S. T., Morgan, B. J. T., Bez, N., Bertrand, S., Choquet, R., Dray, S., Etienne, M.-P., Fewster, R., Gosselin, F., Mérigot, B., Monestiez, P., Morales, J. M., Mortier, F., Munoz, F., Ovaskainen, O., Pavoine, S., Pradel, R., Schurr, F. M., … Rexstad, E. (2014). Statistical ecology comes of age. Biology Letters, 10(12), 20140698. https://doi.org/10.1098/rsbl.2014.0698

Knape, J., Daane, K. M., & de Valpine, P. (2014). Estimation of stage duration distributions and mortality under repeated cohort censuses. Biometrics, 70(2), 346–355. https://doi.org/10.1111/biom.12138

Popescu, V. D., de Valpine, P., & Sweitzer, R. A. (2014). Testing the consistency of wildlife data types before combining them: The case of camera traps and telemetry. Ecology and Evolution, 4(7), 933–943. https://doi.org/10.1002/ece3.997

Scranton, K., Knape, J., & de Valpine, P. (2014). An approximate Bayesian computation approach to parameter estimation in a stochastic stage-structured population model. Ecology, 95(5), 1418–1428. https://doi.org/10.1890/13-1065.1

Bolker, B. M., Gardner, B., Maunder, M., Berg, C. W., Brooks, M., Comita, L., Crone, E., Cubaynes, S., Davies, T., de Valpine, P., Ford, J., Gimenez, O., Kéry, M., Kim, E. J., Lennert-Cody, C., Magnusson, A., Martell, S., Nash, J., Nielsen, A., … Zipkin, E. (2013). Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution, 4(6), 501–512. https://doi.org/10.1111/2041-210X.12044

Chaplin-Kramer, R., de Valpine, P., Mills, N. J., & Kremen, C. (2013). Detecting pest control services across spatial and temporal scales. Agriculture, Ecosystems & Environment, 181, 206–212. https://doi.org/10.1016/j.agee.2013.10.007

de Valpine, P., & Harmon-Threatt, A. N. (2013). General models for resource use or other compositional count data using the Dirichlet-multinomial distribution. Ecology, 94(12), 2678–2687. https://doi.org/10.1890/12-0416.1

Eitzel, M., Battles, J., York, R., Knape, J., & de Valpine, P. (2013). Estimating tree growth from complex forest monitoring data. Ecological Applications, 23(6), 1288–1296. https://doi.org/10.1890/12-0504.1

Knape, J., Besbeas, P., & de Valpine, P. (2013). Using uncertainty estimates in analyses of population time series. Ecology, 94(9), 2097–2107. https://doi.org/10.1890/12-0712.1

Scranton, K., Stavrinides, M., Mills, N. J., & de Valpine, P. (2013). Small-Scale Intraspecific Life History Variation in Herbivorous Spider Mites (Tetranychus pacificus) Is Associated with Host Plant Cultivar. PLOS ONE, 8(9), e72980. https://doi.org/10.1371/journal.pone.0072980

de Valpine, P. (2012). Frequentist analysis of hierarchical models for population dynamics and demographic data. Journal of Ornithology, 152(2), 393–408. https://doi.org/10.1007/s10336-010-0642-5

de Valpine, P. (2012). Model Fitting. In Model Fitting (pp. 450–456). University of California Press. https://doi.org/10.1525/9780520951785-082

Knape, J., & de Valpine, P. (2012). Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? Ecology Letters, 15(1), 17–23. https://doi.org/10.1111/j.1461-0248.2011.01702.x

Knape, J., & de Valpine, P. (2012). Fitting complex population models by combining particle filters with Markov chain Monte Carlo. Ecology, 93(2), 256–263. https://doi.org/10.1890/11-0797.1

Popescu, V. D., de Valpine, P., Tempel, D., & Peery, M. Z. (2012). Estimating population impacts via dynamic occupancy analysis of Before–After Control–Impact studies. Ecological Applications, 22(4), 1389–1404. https://doi.org/10.1890/11-1669.1

Knape, J., & de Valpine, P. (2011). Effects of weather and climate on the dynamics of animal population time series. Proceedings of the Royal Society B: Biological Sciences, 278(1708), 985–992. https://doi.org/10.1098/rspb.2010.1333

Risk, B. B., de Valpine, P., & Beissinger, S. R. (2011). A robust-design formulation of the incidence function model of metapopulation dynamics applied to two species of rails. Ecology, 92(2), 462–474. https://doi.org/10.1890/09-2402.1

de Valpine, P., Scranton, K., & Ohmart, C. P. (2010). Synchrony of population dynamics of two vineyard arthropods occurs at multiple spatial and temporal scales. Ecological Applications, 20(7), 1926–1935. https://doi.org/10.1890/09-0468.1

Ingersoll, T. E., Navo, K. W., & de Valpine, P. (2010). Microclimate preferences during swarming and hibernation in the Townsend’s big-eared bat, Corynorhinus townsendii. Journal of Mammalogy, 91(5), 1242–1250. https://doi.org/10.1644/09-MAMM-A-288.1

Karban, R., & de Valpine, P. (2010). Population dynamics of an Arctiid caterpillar–tachinid parasitoid system using state-space models. Journal of Animal Ecology, 79(3), 650–661. https://doi.org/10.1111/j.1365-2656.2010.01664.x

Nesmith, J. C. B., O’Hara, K. L., Mantgem, P. J. van, & de Valpine, P. (2010). The Effects of Raking on Sugar Pine Mortality following Prescribed Fire in Sequoia and Kings Canyon National Parks, California, USA. Fire Ecology, 6(3), 97–116. https://doi.org/10.4996/fireecology.0603097

de Valpine, P. (2009). Stochastic development in biologically structured population models. Ecology, 90(10), 2889–2901. https://doi.org/10.1890/08-0703.1

de Valpine, P., Bitter, H.-M., Brown, M. P. S., & Heller, J. (2009). A simulation–approximation approach to sample size planning for high-dimensional classification studies. Biostatistics, 10(3), 424–435. https://doi.org/10.1093/biostatistics/kxp001

Moritz, M. A., Moody, T. J., Miles, L. J., Smith, M. M., & de Valpine, P. (2009). The fire frequency analysis branch of the pyrostatistics tree: Sampling decisions and censoring in fire interval data. Environmental and Ecological Statistics, 16(2), 271–289. https://doi.org/10.1007/s10651-007-0088-y

Polansky, L., de Valpine, P., Lloyd-Smith, J. O., & Getz, W. M. (2009). Likelihood ridges and multimodality in population growth rate models. Ecology, 90(8), 2313–2320. https://doi.org/10.1890/08-1461.1

de Valpine, P. (2008). Improved Estimation of Normalizing Constants From Markov Chain Monte Carlo Output. Journal of Computational and Graphical Statistics, 17(2), 333–351. https://doi.org/10.1198/106186008X320258

de Valpine, P., Cuddington, K., Hoopes, M. F., & Lockwood, J. L. (2008). Is Spread of Invasive Species Regulated? Using Ecological Theory to Interpret Statistical Analysis. Ecology, 89(9), 2377–2383. https://doi.org/10.1890/07-0090.1

de Valpine, P., & Eadie, J. M. (2008). Conspecific Brood Parasitism and Population Dynamics. The American Naturalist, 172(4), 547–562. https://doi.org/10.1086/590956

de Valpine, P., & Rosenheim, J. A. (2008). Field-Scale Roles of Density, Temperature, Nitrogen, and Predation on Aphid Population Dynamics. Ecology, 89(2), 532–541. https://doi.org/10.1890/06-1996.1

Polansky, L., de Valpine, P., Lloyd-Smith, J. O., & Getz, W. M. (2008). Parameter estimation in a generalized discrete-time model of density dependence. Theoretical Ecology, 1(4), 221–229. https://doi.org/10.1007/s12080-008-0022-4

Adler, L. S., de Valpine, P., Harte, J., & Call, J. (2007). Effects of Long-term Experimental Warming on Aphid Density in the Field. Journal of the Kansas Entomological Society, 80(2), 156–168. https://doi.org/10.2317/0022-8567(2007)80[156:EOLEWO]2.0.CO;2

de Valpine, P., & Hilborn, R. (2005). State-space likelihoods for nonlinear fisheries time-series. Canadian Journal of Fisheries and Aquatic Sciences, 62(9), 1937–1952. https://doi.org/10.1139/f05-116

de Valpine, P. (2004). Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation. Journal of the American Statistical Association, 99(466), 523–536. https://doi.org/10.1198/016214504000000476

de Valpine, P. (2003). Better Inferences from Population-Dynamics Experiments Using Monte Carlo State-Space Likelihood Methods. Ecology, 84(11), 3064–3077. https://doi.org/10.1890/02-0039

de Valpine, P. (2002). Review of Methods for Fitting Time-Series Models with Process and Observation Error and Likelihood Calculations for Nonlinear, Non-gaussian State-Space Models. Bulletin of Marine Science, 70(2), 455–471.

de Valpine, P., & Hastings, A. (2002). Fitting Population Models Incorporating Process Noise and Observation Error. Ecological Monographs, 72(1), 57–76. https://doi.org/10.1890/0012-9615(2002)072[0057:FPMIPN]2.0.CO;2

de Valpine, P., & Harte, J. (2001). Plant Responses to Experimental Warming in a Montane Meadow. Ecology, 82(3), 637–648. https://doi.org/10.1890/0012-9658(2001)082[0637:PRTEWI]2.0.CO;2

de Valpine, P. (2000). A new demographic function maximized by life-history evolution. Proceedings of the Royal Society Biological Sciences Series B, 267(1441), 357–362.

Koster, R. D., de Valpine, D. P., & Jouzel, J. (1993). Continental water recycling and H218O concentrations. Geophysical Research Letters, 20(20), 2215–2218. https://doi.org/10.1029/93GL01781