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Rising temperatures and changes in precipitation patterns under climate change scenarios are accelerating the depletion of soil moisture and increasing the risk of drought, disrupting the conditions that many plant species need to survive. This study aims to establish the bioclimatic characterisation, both qualitative and quantitative, of ten native Californian Pinales for the period 1980–2019, and to determine their habitat suitability by 2050. To achieve this, an exhaustive search of the Gbif database for records of ten conifer taxa was carried out. To conduct the bioclimatic characterisation of the studied taxa, we worked with the monthly values of average temperature and precipitation for the period 1980–2019 from 177 meteorological stations. Linear regressions was performed in order to compile the future evolution of California's climate. Suitable areas and optimal areas were defined at the present time (1980–2019) and its future projection (2050). We applied Boolean logic and, in this investigation, the Conditional Logic Operator (CON) was used to determine the possible species presence (one) or absence (zero) for each of the 15 variables analysed. In general, most of the conifers studied here will experience a reduction in their habitat range in California by the year 2050 due to climate change, as well as the displacement of species towards optimal areas. Furthermore, the results have highlighted the applicability of bioclimatology to future conditions under climate change. This will aid conservation managers in implementing strategic measures to ameliorate the detrimental impacts of climate change, thereby ensuring the ecological integrity and sustainability of the affected conifer species. [ABSTRACT FROM AUTHOR]

The global mean surface temperature has risen since the late 19th century. However, temperatures do not increase uniformly in space or time and few studies have focused on that peculiarity in the State of California. The aim of this research is to deepen our knowledge of the evolution of mean temperatures in the State of California on monthly, seasonal and annual time scales. The period under study comprises 40 years (from 1980 to 2019) and data from 170 meteorological stations were analysed. Statistical techniques, including Sen's slope and Mann-Kendall, were applied to each of the stations to establish the sign and slopes of trends and their statistical significance. The spatial distribution of monthly, seasonal and annual trends was analysed using the Empirical Bayesian Kriging (EBK) geostatistical technique. The trend analysis was also carried out for the State as a whole. This research also studies the relationships between mean temperatures and nine teleconnection patterns with influence on the Californian climate. To find out these links, a correlation analysis was performed using the partial non-parametric Spearman Test at a 95% confidence level. The study reveals a positive trend of +0.01 °C year−1 for the whole state and that Southern California is getting warmer than Northern California for the study period. On a seasonal scale, the local temperature increased significantly both in autumn and summer (+0.06 °C and +0.035 °C year−1 respectively) from 1980 to 2019. On a monthly scale, the largest increases are found in November at +0.04 °C year−1. Temperatures in February, March, April and May are highly correlated with most of the teleconnection patterns studied in the State of California. West Pacific Oscillation (WPO) teleconnection pattern has shown the highest negative correlation. However, The Pacific Decadal Oscillation (PDO) has a positive correlation with mean temperatures in coastal areas such as Los Angeles, San Francisco and Monterey. Moreover, Antarctic Oscillation (AAO) and Arctic Oscillation patterns (AO) are unlikely to show great influence on average temperature trends in California. [ABSTRACT FROM AUTHOR]