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Current climate changes imply an imminent risk for forest species. In this context, somatic embryogenesis is a valuable tool to study the response of plants to different abiotic stresses. Based on this, we applied a high-temperature regime (50 °C, 5 min) during the maturation of Pinus radiata D. Don embryogenic masses in order to evaluate the development of an epigenetic memory months later. Therefore, somatic plants (SP) resulting from somatic embryos (ses) maturated at control temperature and cultivated in a greenhouse were submitted to heat stress (40 °C, 2 h, 10 days; 23 °C, 10 days) or at a control temperature (23 °C, 20 days); while another 20 SP resulting from ses maturated in the two temperature regimes and cultivated in the greenhouse were submitted to drought stress or weekly irrigated. All plants were evaluated for relative water content, water potential, electrolyte leakage, stomatal conductance, transpiration, methylation (5-mC) and hydroxymethylation (5-hmC) levels. The results showed that the SP obtained from ses maturated at 50 °C showed an adaptation to drought stress based on water potential and transpiration. Furthermore, SP kept under heat stress in a greenhouse showed lower 5-hmC levels than SP kept at 23 °C. Furthermore, the 5-hmC and 5-hmC/5-mC ratio showed a significantly negative correlation with changes in water potential; and a significantly negative correlation was observed between the levels of stomatal conductance and 5-mC. We conclude that the manipulation of conditions during the maturation process in somatic embryogenesis modulates the physiological characteristics of the SP obtained. This research was funded by MINECO (Spanish Government) project (AGL2016-76143-C4-3R), CYTED (P117RT0522), and MINECO (BES-2017-081249, 'Ayudas para contratos predoctorales para la formación de doctores'). MULTIFOREVER (Project MULTIFOREVER is supported under the umbrella of ERA-NET cofound Forest Value by ANR (FR), FNR (DE), MINCyT (AR), MINECO-AEI (ES), MMM (FI) and VINNOVA (SE). Forest value has received funding from the European Union's Horizon 2020 research and innovation programmed under Agreement No. 773324.

Vegetative propagation through somatic embryogenesis is an effective method to produce elite varieties and can be applied as a tool to study the response of plants to different stresses. Several studies show that environmental changes during embryogenesis could determine future plant development. Moreover, we previously reported that physical and chemical conditions during somatic embryogenesis can determine the protein, hormone and metabolite profiles, as well as the micromorphological and ultrastructural organization of embryonal masses and somatic embryos. In this sense, phytohormones are key players throughout the somatic embryogenesis process as well as during numerous stress-adaptation responses. In this work, we first applied different higherature regimes (30 °C, 4 weeks; 40 °C, 4 days; 50 °C, 5 min) during induction of Pinus radiata D. Don somatic embryogenesis, together with control temperature (23 °C). Then, the somatic plants regenerated from initiated embryogenic cell lines and cultivated in greenhouse conditions were subjected to drought stress and control treatments to evaluate survival, growth and several physiological traits (relative water content, water potential, photosynthesis, stomatal conductance and transpiration). Based on those preliminary results, even more extreme higherature regimes were applied during induction (40 °C, 4 h; 50 °C, 30 min; 60 °C, 5 min) and the corresponding cytokinin profiles of initiated embryonal masses from different lines were analysed. The results showed that the temperature regime during induction had delayed negative effects on drought resilience of somatic plants as indicated by survival, photosynthetic activity and water- use efficiency. However, high temperatures for extended periods of time enhanced subsequent plant growth in well-watered conditions. Higherature regime treatments induced significant differences in the profile of total cytokinin bases, N6-isopentenyladenine, cis-zeatin riboside and trans-zeatin riboside. We concluded that phytohormones could be potential regulators of stress-response processes during initial steps of somatic embryogenesis and that they may have delayed implications in further developmental processes, determining the performance of the generated plants. This research was funded by MINECO (Spanish Government) project (AGL2016-60476143-C4-3R), CYTED (P117RT0522), DECO (Basque government, ‘Ayudas de formación a jóvenes investigadores y tecnólogos’) and MULTIFOREVER (Forest Value, ERANET program, EU). Project MULTIFOREVER is supported under the umbrella of ERA-NET Cofund ForestValue by ANR (FR), FNR (DE), MINCyT (AR), MINECO-AEI (ES), MMM (FI) and VINNOVA (SE). ForestValue has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°773324. This work was also supported by the Internal Grant Agency of Palacký University (IGA_PrF_2019_020) and from European Regional Development Fund-Project ‘Plants as a tool for sustainable global development’ (No. CZ.02.1.01/0.0/0.0/16_019/0000827).

Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on mortality/incidence relative risk estimation. We present a pragmatic and simple idea that permits to fit hierarchical spatio-temporal models when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important. This work has been supported by Projects MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033). Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time. This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033

The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated Open Access funding provided by Universidad Pública de Navarra. This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.

Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001-2013. Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.130 39/501100011033.

The distribution of lip, oral cavity, and pharynx (LOCP) cancer mortality rates in small domains (defined as the combination of province, age group, and gender) remains unknown in Spain. As many of the LOCP risk factors are preventable, specific prevention programmes could be implemented but this requires a clear specification of the target population. This paper provides an in-depth description of LOCP mortality rates by province, age group and gender, giving a complete overview of the disease. This study also presents a methodological challenge. As the number of LOCP cancer cases in small domains (province, age groups and gender) is scarce, univariate spatial models do not provide reliable results or are even impossible to fit. In view of the close link between LOCP and lung cancer, we consider analyzing them jointly by using shared component models. These models allow information-borrowing among diseases, ultimately providing the analysis of cancer sites with few cases at a very disaggregated level. Results show that males have higher mortality rates than females and these rates increase with age. Regions located in the north of Spain show the highest LOCP cancer mortality rates. The work was supported by Project MTM2017-82553-R (AEI, UE), Project PID2020-113125RB-I00/MCIN/ AEI/10.13039/501100011033 and Proyecto Jóvenes Investigadores PJUPNA2018-11.

Development of hybrid pines of Pinus radiata D. Don for commercial forestry presents an opportunity to diversify the current resource of plant material. Climate change and different land uses pose challenges, making alternative species necessary to guarantee wood and non-wood products in the future. Pinus radiata var. cedrosensis &times, Pinus attenuata hybrid possesses different attributes, such as tolerance to drought conditions, better growth and resistance to snow damage at higher altitudes, and more importantly, different wood quality characteristics. Embryogenic cell lines were successfully initiated reciprocal hybrids using as initial explants megagametophytes, excised zygotic embryos and excised zygotic embryos plus nurse culture. However, the questions raised were: does the initiation environment affect the conversion to somatic plantlets months later? Does the mother tree or the cross have an effect on the conversion to somatic plantlets? In the present work we analysed the maturation rate, number of somatic embryos, germination rate, and the ex-vitro growth in cell lines derived from different initiation treatments, mother tree species, and crosses. Differences were not observed for in vitro parameters such as maturation and germination. However, significant differences were observed due to the mother tree species in relation with the ex-vitro growth rates observed, being higher those in which P. radiata acted as a mother. Moreover, embryogenic cell lines from these hybrids were stored at &minus, 80 &deg, C and regenerated after one and five years.

Climatic variations in the current environmental scenario require plants with tolerance to sudden changes in temperature and a decrease in water availability. Accordingly, this tolerance will enable successful plantations and the maintenance of natural and planted forests. Consequently, in the last two decades, drought tolerance and high temperatures in conifers have been an important target for morphological, physiological, and epigenetic studies. Based on this, our research team has optimized different stages of somatic embryogenesis (SE) in Pinus spp. improving the success of the process. Through this method, we can obtain a large amount of clonal material and then analyze the somatic plants under different conditions ex vitro. The analysis of the morphological and physiological parameters in somatic embryos (ses) and plants with different tolerances to abiotic stress can provide us with valuable information about the mechanisms used by plants to survive under adverse environmental conditions. Thus, the objective of this work was to evaluate the influence of high temperatures (23, 40, 50, and 60 &deg, C, after 12 weeks, 90, 30, 5 min, respectively) on the morphology of somatic embryos obtained from Pinus radiata D.Don (Radiata pine) and Pinus halepensis Mill. (Aleppo pine). In addition, we carried out a physiological evaluation of the somatic plants of P. radiata submitted to heat and water stress in a greenhouse. We observed that the number of somatic embryos was not affected by maturation temperatures in both species. Likewise, P. radiata plants obtained from these somatic embryos survived drought and heat stress in the greenhouse. In addition, plants originating from embryonal masses (EMs) subjected to high maturation temperature (40 and 60 &deg, C) had a significant increase in gs and E. Therefore, it is possible to modulate the characteristics of somatic plants produced by the manipulation of environmental conditions during the process of SE.

Joint analyses of two or more related diseases bring advantages over individual studies to look into diseases with low incidence, or to disentangle spatial patterns by age-group and gender. Incidence and/or mortality are not homogeneously distributed according to age-groups and ignoring age may lead to biased estimates. In addition, counts at a very disaggregated level can be very scarce or even zero for rare diseases and powerful models to borrow strength from related processes and geographical areas are strongly recommended. In this paper, gender and age-specific spatial shared component models are proposed for a joint modelling of brain cancer incidence and mortality rates in Navarre and the Basque Country, two regions in the north of Spain with the highest incidence rates among European regions.

Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

In recent years, models incorporating splines have been considered for smoothing risks in disease mapping. Although these models are very flexible, they can be computationally demanding in certain cases. In this work, one, two, and three-dimensional B-splines (penalized or unpenalized) are considered to model space–time interactions. Model identifiability issues are discussed and appropriate constraints are clearly established. As computing time could be a limitation in real practice, integrated nested Laplace approximations are used for model fitting and inference. The complete set of proposed models are illustrated using cancer mortality data in small areas. We conclude that if the number of small areas is not big, one dimensional P-splines for the space–time interaction could be a good choice. When the number of small areas increases substantially, two-dimensional and mainly three dimensional splines are computationally better alternatives.

Background Ovarian cancer is a silent and largely asymptomatic cancer, leading to late diagnosis and worse prognosis. The late-stage detection and low survival rates, makes the study of the space-time evolution of ovarian cancer particularly relevant. In addition, research of this cancer in small areas (like provinces or counties) is still scarce. Methods The study presented here covers all ovarian cancer deaths for women over 50 years of age in the provinces of Spain during the period 1989-2015. Spatio-temporal models have been fitted to smooth ovarian cancer mortality rates in age groups [50,60), [60,70), [70,80), and [80,+), borrowing information from spatial and temporal neighbours. Model fitting and inference has been carried out using the Integrated Nested Laplace Approximation (INLA) technique. Results Large differences in ovarian cancer mortality among the age groups have been found, with higher mortality rates in the older age groups. Striking differences are observed between northern and southern Spain. The global temporal trends (by age group) reveal that the evolution of ovarian cancer over the whole of Spain has remained nearly constant since the early 2000s. Conclusion Differences in ovarian cancer mortality exist among the Spanish provinces, years, and age groups. As the exact causes of ovarian cancer remain unknown, spatio-temporal analyses by age groups are essential to discover inequalities in ovarian cancer mortality. Women over 60 years of age should be the focus of follow-up studies as the mortality rates remain constant since 2002. High-mortality provinces should also be monitored to look for specific risk factors.

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently—though in fact not—equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India. This work has been supported by Project MTM2017-82553-R (AEI/FEDER, UE). It has also been partially funded by ‘la Caixa’ Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also been partially funded by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Crimes against women in India have been continuously increasing lately as reported by the National Crime Records Bureau. Gender-based violence has become a serious issue to such an extent that it has been catalogued as a high impact health problem by the World Health Organization. However, there is a lack of spatiotemporal analyses to reveal a complete picture of the geographical and temporal patterns of crimes against women. We focus on analysing how the geographical pattern of 'dowry deaths' changes over time in the districts of Uttar Pradesh during the period 2001–2014. The study of the geographical distribution of dowry death incidence and its evolution over time aims to identify specific regions that exhibit high risks and to hypothesize on potential risk factors. We also look into different spatial priors and their effects on final risk estimates. Various priors for the hyperparameters are also reviewed. The risk estimates seem to be robust in terms of the spatial prior and hyperprior choices and final results highlight several districts with extreme risks of dowry death incidence. Statistically significant associations are also found between dowry deaths, sex ratio and some forms of overall crime. This work has been supported by project MTM2017‐82553‐R (Agencia Estatal de Investigación–Fondos de Desarrollo Regional, European Union). It has also been partially funded by the Caixa Foundation (grant 1000010434), Caja Navarra Foundation and National University of Distance Learning, Pamplona, under agreement LCF/PR/PR15/51100007.

Superparamagnetic iron oxide nanoparticles (MNPs) have the potential to act as heat sources in magnetic hyperthermia. The key parameter for this application is the specific absorption rate (SAR), which must be as large as possible in order to optimize the hyperthermia treatment. We applied a Plackett-Burman fractional factorial design to investigate the effect of total iron concentration, ammonia concentration, reaction temperature, sonication time and percentage of ethanol in the aqueous media on the properties of iron oxide MNPs. Characterization techniques included total iron content, Fourier Transform Infrared Spectroscopy, X-Ray Diffraction, High Resolution Transmission Electron Microscopy, and Dynamic Magnetization. The reaction pathway in the coprecipitation reaction depended on the initial Fe concentration. Samples synthesized from 0.220 mol L−1 Fe yielded magnetite and metastable precipitates of iron oxyhydroxides. An initial solution made up of 0.110 mol L−1 total Fe and either 0.90 or 1.20 mol L−1 NH3(aq) led to the formation of magnetite nanoparticles. Sonication of the reaction media promoted a phase transformation of metastable oxyhydroxides to crystalline magnetite, the development of crystallinity, and the increase of specific absorption rate under dynamic magnetization.

Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not well-specified. In this paper the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.

In Europe, rural depopulation and the abandonment of pastoral practices in mountain areas trigger deep changes in the landscape, which result in the accumulation of lignified fuels and the increased risk of fires, a sensitive issue in southern areas of the continent. From prehistory, a pyric herbivory has been practiced in European mountain regions. Pastoral fires created open communities, amenable to wild and domestic grazing, and herbivores perpetuated them by controlling fuel accumulation. In the last decades, the declining of extensive herbivory has given a prominent role to prescribed fires in order to preserve open communities. As a consequence, a new scenario of increased burning frequency and reduced herbivory emerges, which may affect the soil function in different ways. Our aim was to evaluate the effects of experimental burnings on soil function and nutrients cycling of a mountain gorse (Ulex gallii Planch.) shrubland, with the absence/presence of extensive grazing. We performed traditional “bush-to-bush” burnings in three experimental mountain plots and analysed seasonally along a 2-year period the soil function in relation to C-cycle (dissolved organic C, microbial biomass C and glucosidase activity), N-cycle (inorganic N forms, dissolved organic N, microbial biomass N and urease activity), P-cycle (phosphatase activity) and overall bacterial catabolic activity. Fire effects were time dependent and extensive grazing had a low influence on them. Fire originated a transient pulse of inorganic-N forms in the short term, which disappeared after 1 year, and increased dissolved organic N forms, which attenuated with time. In burned areas, a decrease of total N and microbial biomass N, and a slow-down of N- and P-cycle enzymatic activities were observed in the mid-term coinciding with a decrease of soil moisture. Since a higher burning frequency is a feasible situation that may affect mountain, nutrient-poor soils, the enduring effects of prescribed fires need to be taken into account to establish the optimal date of burning and the adequate recurrence regime that avoid negative impacts on the soil function and minimize the loss of nutrients from the soil reservoir.

This work focuses on extending some classical spatio-temporal models in disease mapping. The objective is to present a family of flexible models to analyze real data naturally organized in two different levels of spatial aggregation like municipalities within health areas or provinces, or counties within states. Model fitting and inference will be carried out using integrated nested Laplace approximations. The performance of the new models compared to models including a single spatial random effect is assessed by simulation. Results show good behavior of the proposed two-level spatially structured models in terms of several criteria. Brain cancer mortality data in the municipalities of two regions in Spain will be analyzed using the new model proposals. It will be shown that a model with two-level spatial random effects overcomes the usual single-level models.

Pinus halepensis has been described as a drought-tolerant species with high plasticity to growth in different environments. Its eco-physiological characteristics could facilitate the use of this species in large afforestations in the future scenery of climate change. Somatic embryogenesis is a biotechnological tool with potential for large-scale clonal propagation. In order to establish an improved regeneration protocol for Pinus halepensis, the effects of different temperatures (18, 23, and 28 °C) and water availability conditions (2, 3, and 4 g L−1 Gelrite®), during initiation of embryonal masses on the rate of initiation, proliferation, maturation, and the number of embryos developed, were evaluated. It was found that environmental conditions during the initiation stage of Pinus halepensis somatic embryogenesis influence the success of initiation and proliferation. In contrast, there was no effect of these conditions on the maturation rates and the number of somatic embryos. Somatic embryos were obtained in all treatments tested, indicating that plants can be produced from extreme conditions of induction, such as high temperatures (28 °C) and low water availability conditions (4 g L−1).