Parent Layer: Juvenile Period in Slow-Maturing Plants (Recent) - SW of WA (DBCA-073)

Name: Juvenile period in years

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Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)

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