Appendix F. Decomposition of LTRE conributions to Δλ in cohort 4.

Decomposition of LTRE conributions to Δλ in cohort 4

Appendix B. Details of vital rate estimation, matrix construction, and LTRE analysis.

Details of vital rate estimation, matrix construction, and LTRE analysis

Appendix A. Path coefficients from structural equation modeling of relative fitness in Plantago lanceolata.

Path coefficients from structural equation modeling of relative fitness in Plantago lanceolata

Appendix A. Distributions of adult size classes across years for different paternal lineages in cohort 1.

Distributions of adult size classes across years for different paternal lineages in cohort 1

Appendix E. LTRE analysis of four-cohort dataset.

LTRE analysis of four-cohort dataset

Appendix C. Exploration of fertility assumptions and their impacts on λ.

Exploration of fertility assumptions and their impacts on λ

Appendix A. Responses of Cypripedium calceolus and Cephalanthera longifolia individuals among five Estonian populations to defoliation and shading in 2002 through 2004.

Responses of Cypripedium calceolus and Cephalanthera longifolia individuals among five Estonian populations to defoliation and shading in 2002 through 2004

Figure S6. Life table response experiment analysis for C. candidum from Predicting evolution in response to climate change: the example of sprouting probability in three dormancy-prone orchid species

Although many ecological properties of species respond to climate change, their evolutionary responses are poorly understood. Here, we use data from long-term demographic studies to predict evolutionary responses of three herbaceous perennial orchid species, <i>Cypripedium parviflorum</i>, <i>C. candidum</i> and <i>Ophrys sphegodes</i>, to predicted climate changes in the habitats they occupy. We focus on the evolution of sprouting probability, because all three species exhibit long-term vegetative dormancy, i.e. individual plants may not emerge above-ground, potentially for several consecutive years. The drivers of all major vital rates for populations of the species were analysed with GLMMs. High-dimensionality function-based matrix projection models were then developed to serve as core elements of deterministic and stochastic adaptive dynamics models used to analyse the adaptive context of sprouting in all populations. We then used regional climate forecasts, derived from high-resolution general atmospheric circulation models, of increased mean annual temperatures and spring precipitation at the occupied sites, to predict evolutionary trends in sprouting. The models predicted that <i>C. parviflorum</i> and <i>O. sphegodes</i> will evolve higher and lower probabilities of sprouting, respectively, by the end of the twenty-first century, whereas, after considerable variation, the probability of sprouting in <i>C. candidum</i> will return to its current level. These trends appear to be driven by relationships between mortality and size: in <i>C. parviflorum</i> and <i>C. candidum</i>, mortality is negatively related to size in the current year but positively related to growth since the previous year, whereas in <i>O. sphegodes</i>, mortality is positively related to size

Table S2. Best-fit parameters for C.candidum from Predicting evolution in response to climate change: the example of sprouting probability in three dormancy-prone orchid species

Although many ecological properties of species respond to climate change, their evolutionary responses are poorly understood. Here, we use data from long-term demographic studies to predict evolutionary responses of three herbaceous perennial orchid species, <i>Cypripedium parviflorum</i>, <i>C. candidum</i> and <i>Ophrys sphegodes</i>, to predicted climate changes in the habitats they occupy. We focus on the evolution of sprouting probability, because all three species exhibit long-term vegetative dormancy, i.e. individual plants may not emerge above-ground, potentially for several consecutive years. The drivers of all major vital rates for populations of the species were analysed with GLMMs. High-dimensionality function-based matrix projection models were then developed to serve as core elements of deterministic and stochastic adaptive dynamics models used to analyse the adaptive context of sprouting in all populations. We then used regional climate forecasts, derived from high-resolution general atmospheric circulation models, of increased mean annual temperatures and spring precipitation at the occupied sites, to predict evolutionary trends in sprouting. The models predicted that <i>C. parviflorum</i> and <i>O. sphegodes</i> will evolve higher and lower probabilities of sprouting, respectively, by the end of the twenty-first century, whereas, after considerable variation, the probability of sprouting in <i>C. candidum</i> will return to its current level. These trends appear to be driven by relationships between mortality and size: in <i>C. parviflorum</i> and <i>C. candidum</i>, mortality is negatively related to size in the current year but positively related to growth since the previous year, whereas in <i>O. sphegodes</i>, mortality is positively related to size

Table S3. Best-fit parameters for O. sphegodes from Predicting evolution in response to climate change: the example of sprouting probability in three dormancy-prone orchid species

Although many ecological properties of species respond to climate change, their evolutionary responses are poorly understood. Here, we use data from long-term demographic studies to predict evolutionary responses of three herbaceous perennial orchid species, <i>Cypripedium parviflorum</i>, <i>C. candidum</i> and <i>Ophrys sphegodes</i>, to predicted climate changes in the habitats they occupy. We focus on the evolution of sprouting probability, because all three species exhibit long-term vegetative dormancy, i.e. individual plants may not emerge above-ground, potentially for several consecutive years. The drivers of all major vital rates for populations of the species were analysed with GLMMs. High-dimensionality function-based matrix projection models were then developed to serve as core elements of deterministic and stochastic adaptive dynamics models used to analyse the adaptive context of sprouting in all populations. We then used regional climate forecasts, derived from high-resolution general atmospheric circulation models, of increased mean annual temperatures and spring precipitation at the occupied sites, to predict evolutionary trends in sprouting. The models predicted that <i>C. parviflorum</i> and <i>O. sphegodes</i> will evolve higher and lower probabilities of sprouting, respectively, by the end of the twenty-first century, whereas, after considerable variation, the probability of sprouting in <i>C. candidum</i> will return to its current level. These trends appear to be driven by relationships between mortality and size: in <i>C. parviflorum</i> and <i>C. candidum</i>, mortality is negatively related to size in the current year but positively related to growth since the previous year, whereas in <i>O. sphegodes</i>, mortality is positively related to size