A harmonized database of European forest simulations under climate change.

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

Plant hydraulics at the heart of plant, crops and ecosystem functions in the face of climate change.

Plant hydraulics is crucial for assessing the plants' capacity to extract and transport water from the soil up to their aerial organs. Along with their capacity to exchange water between plant compartments and regulate evaporation, hydraulic properties determine plant water relations, water status and susceptibility to pathogen attacks. Consequently, any variation in the hydraulic characteristics of plants is likely to significantly impact various mechanisms and processes related to plant growth, survival and production, as well as the risk of biotic attacks and forest fire behaviour. However, the integration of hydraulic traits into disciplines such as plant pathology, entomology, fire ecology or agriculture can be significantly improved. This review examines how plant hydraulics can provide new insights into our understanding of these processes, including modelling processes of vegetation dynamics, illuminating numerous perspectives for assessing the consequences of climate change on forest and agronomic systems, and addressing unanswered questions across multiple areas of knowledge.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ψ Leaf ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced ψ Leaf and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Trajectories of wildfire behavior under climate change. Can forest management mitigate the increasing hazard?

Mediterranean forests and fire regimes are closely intertwined. Global change is likely to alter both forest dynamics and wildfire activity, ultimately threatening the provision of ecosystem services and posing greater risks to society. In this paper we evaluate future wildfire behavior by coupling climate projections with simulation models of forest dynamics and wildfire hazard. To do so, we explore different forest management scenarios reflecting different narratives related to EU forestry (promotion of carbon stocks, reduction of water vulnerability, biomass production and business-as-usual) under the RCP 4.5 and RCP 8.5 climate pathways in the period 2020-2100. We used as a study model pure submediterranean Pinus nigra forests of central Catalonia (NE Spain). Forest dynamics were simulated from the 3rd National Forest Inventory (143 stands) using SORTIE-nd software based on climate projections under RCPs 4.5 and 8.5. The climate products were also used to estimate fuel moisture conditions (both live and dead) and wind speed. Fuel parameters and fire behavior were then simulated, selecting crown fire initiation potential and rate of spread as key indicators. The results revealed consistent trade-offs between forest dynamics, climate and wildfire. Despite the clear influence exerted by climate, forest management modulates fire behavior, resulting in different trends depending on the climatic pathway. In general, the maintenance of current practices would result in the highest rates of crown fire activity, while management for water vulnerability reduction is postulated as the best alternative to surmount the increasingly hazardous conditions envisaged in RCP 8.5.

Towards a statistically robust determination of minimum water potential and hydraulic risk in plants.

Minimum water potential (Ψmin ) is a key variable for characterizing dehydration tolerance and hydraulic safety margins (HSMs) in plants. Ψmin is usually estimated as the absolute minimum tissue Ψ experienced by a species, but this is problematic because sample extremes are affected by sample size and the underlying probability distribution. We compare alternative approaches to estimate Ψmin and assess the corresponding uncertainties and biases; propose statistically robust estimation methods based on extreme value theory (EVT); and assess the implications of our results for the characterization of hydraulic risk. Our results show that current estimates of Ψmin and HSMs are biased, as they are strongly affected by sample size. Because sampling effort is generally higher for species living in dry environments, the differences in current Ψmin estimates between these species and those living under milder conditions are partly artefactual. When this bias is corrected using EVT methods, resulting HSMs tend to increase substantially with resistance to embolism across species. Although data availability and representativeness remain the main challenges for proper determination of Ψmin , a closer look at Ψ distributions and the use of statistically robust methods to estimate Ψmin opens new ground for characterizing plant hydraulic risks.

Temperature and water potential co-limit stem cambial activity along a steep elevational gradient.

Efforts to develop mechanistic tree growth models are hindered by the uncertainty of whether and when tree growth responses to environmental factors are driven by carbon assimilation or by biophysical limitations of wood formation. In this study, we used multiannual weekly wood-formation monitoring of two conifer species (Larix decidua and Picea abies) along a 900 m elevational gradient in the Swiss Alps to assess the biophysical effect of temperature and water potential on wood formation. To this end, we developed a model that simulates the effect of water potential on turgor-driven cambial division, modulated by the effect of temperature on enzymatic activity. The model reproduced the observed phenology of tracheid production, as well as intra- and interannual tracheid production dynamics of both species along the elevational gradient, although interannual model performance was lower. We found that temperature alone explains the onset of tracheid production, yet water potential appears necessary to predict the ending and the total amount of tracheids produced annually. We conclude that intra-annual cambial activity is strongly constrained by both temperature and water potential at all elevations, independently of carbon assimilation. At the interannual scale, biophysical constraints likely interact with other factors.

Water potential control of turgor-driven tracheid enlargement in Scots pine at its xeric distribution edge.

The extent to which water availability can be used to predict the enlargement and final dimensions of xylem conduits remains an open issue. We reconstructed the time course of tracheid enlargement in Pinus sylvestris trees in central Spain by repeated measurements of tracheid diameter on microcores sampled weekly during a 2 yr period. We analyzed the role of water availability in these dynamics empirically through time-series correlation analysis and mechanistically by building a model that simulates daily tracheid enlargement rate and duration based on Lockhart's equation and water potential as the sole input. Tracheid enlargement followed a sigmoid-like time course, which varied intra- and interannually. Our empirical analysis showed that final tracheid diameter was strongly related to water availability during tracheid enlargement. The mechanistic model was calibrated and successfully validated (R2  = 0.92) against the observed tracheid enlargement time course. The model was also able to reproduce the seasonal variations of tracheid enlargement rate, duration and final diameter (R2  = 0.84-0.99). Our results support the hypothesis that tracheid enlargement and final dimensions can be modeled based on the direct effect of water potential on turgor-driven cell expansion. We argue that such a mechanism is consistent with other reported patterns of tracheid dimension variation.

Temporal dimension of forest vulnerability to fire along successional trajectories.

Understanding ecosystem vulnerability is essential in risk management to anticipate disasters. While valuable efforts have been made to characterize vulnerability components (exposure, sensitivity, and response capacity) at particular ecosystem stages, there is still a lack of context-specific studies accounting for the temporal dimension of vulnerability. In this study, we developed a procedure to identify the main natural dynamics of monospecific and mixed forests and to assess the variations of sensitivity and response capacity to fire along successional dynamics. In the procedure, we generated forest chronosequences by summarizing the dynamics between consecutive surveys of permanent plots into a set of longer successional trajectories represented in a multidimensional space. Then, we calculated several variables of sensitivity and response capacity to fire of forest stages associated with each trajectory and we assessed their variation along succession. The procedure was applied to Mediterranean forests in Spain dominated by a pine species poorly adapted to severe crown fires. We found that forest vulnerability components varied differently among successional trajectories, which depended on the composition and structure of their initial stages and the environmental context in which they occurred. Autosuccessional dynamics of pine forests showed relatively low sensitivity to fire along trajectories. However, their response capacity was related to the changes in shrub cover. In contrast, diversifying dynamics showed an increasing sensitivity to fire, but also a higher response capacity the greater the functional diversity along succession. These results highlight the need for considering the temporal dimension of vulnerability in risk management and the importance of assessing sensitivity and response capacity as independent components of vulnerability that can be modified through management at critical forest stages.

Adapting prescribed burns to future climate change in Mediterranean landscapes.

Fire regimes are shifting or are expected to do so under global change. Current fire suppression is not able to control all wildfires, and its capability to do so might be compromised under harsher climate conditions. Alternative fire management strategies may allow to counteract predicted fire trends, but we lack quantitative tools to evaluate their potential effectiveness at the landscape scale. Here, we sought to quantify changes in fire regimes induced after the implementation of different fire management strategies. We developed and applied a new version of the model MEDFIRE in Catalonia (Mediterranean region of ~32,000 km2 in NE Spain). We first projected burnt area from 2016 to 2100 resulting from climate change under the Representative Concentration Pathway 8.5 scenario of HadGEM-CC model and under current fire suppression levels. We then evaluated the impacts of four fire management strategies: 'Let it burn', fixed effort of prescribed burning with two different spatial allocations, and adaptive prescribed burning dynamically adjusting efforts according to recent past fires. Results predicted the emergence of novel climates associated with similar barometric configurations to current conditions but with higher temperatures (i.e. hot wind events). These novel climates led to an increase in burnt area, which was partially counteracted by negative fire-vegetation feedbacks. All prescribed burning scenarios decreased the amount of high-intensity fires and extreme fire events. The 'Let it burn' strategy, although less costly, was not able to reduce the extent of high-intensity fires. The adaptive prescribed burning scenario resulted in the most cost-efficient strategy. Our results provide quantitative evidence of fire management effectiveness, and bring to light key insights that could guide the design of fire policies fit for future novel climate conditions. We propose adaptive landscape management focused on the reduction of fire negative impacts rather than on the elimination of this disturbance from the system.

Using unplanned fires to help suppressing future large fires in Mediterranean forests.

Despite the huge resources invested in fire suppression, the impact of wildfires has considerably increased across the Mediterranean region since the second half of the 20th century. Modulating fire suppression efforts in mild weather conditions is an appealing but hotly-debated strategy to use unplanned fires and associated fuel reduction to create opportunities for suppression of large fires in future adverse weather conditions. Using a spatially-explicit fire-succession model developed for Catalonia (Spain), we assessed this opportunistic policy by using two fire suppression strategies that reproduce how firefighters in extreme weather conditions exploit previous fire scars as firefighting opportunities. We designed scenarios by combining different levels of fire suppression efficiency and climatic severity for a 50-year period (2000-2050). An opportunistic fire suppression policy induced large-scale changes in fire regimes and decreased the area burnt under extreme climate conditions, but only accounted for up to 18-22% of the area to be burnt in reference scenarios. The area suppressed in adverse years tended to increase in scenarios with increasing amounts of area burnt during years dominated by mild weather. Climate change had counterintuitive effects on opportunistic fire suppression strategies. Climate warming increased the incidence of large fires under uncontrolled conditions but also indirectly increased opportunities for enhanced fire suppression. Therefore, to shift fire suppression opportunities from adverse to mild years, we would require a disproportionately large amount of area burnt in mild years. We conclude that the strategic planning of fire suppression resources has the potential to become an important cost-effective fuel-reduction strategy at large spatial scale. We do however suggest that this strategy should probably be accompanied by other fuel-reduction treatments applied at broad scales if large-scale changes in fire regimes are to be achieved, especially in the wider context of climate change.

A resource-based modelling framework to assess habitat suitability for steppe birds in semiarid Mediterranean agricultural systems.

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species' key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species' distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species' occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models.

Ultraviolet radiation dose calculation for algal suspensions using UVA and UVB extinction coefficients.

Although the biological importance of ultraviolet light (UVR) attenuation has been recognised in marine and freshwater environments, it is not generally considered in in vitro ecotoxicological studies using algal cell suspensions. In this study, UVA and UVB extinction were determined for cultures of algae with varying cell densities, and the data were used to calculate the corresponding extinction coefficients for both UVA and UVB wavelength ranges. Integrating the Beer-Lambert equation to account for changes in the radiation intensity reaching each depth, from the surface until the bottom of the experimental vessel, we obtained the average UVA and UVB intensity to which the cultured algal cells were exposed. We found that UVR intensity measured at the surface of Chlamydomonas reinhardtii cultures lead to a overestimation of the UVR dose received by the algae by 2-40 times. The approach used in this study allowed for a more accurate estimation of UVA and UVB doses.

Beta diversity as the variance of community data: dissimilarity coefficients and partitioning.

Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a dissimilarity matrix obtained using any dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova. Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within- and among-group components by manova, into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.

How fire history, fire suppression practices and climate change affect wildfire regimes in Mediterranean landscapes.

Available data show that future changes in global change drivers may lead to an increasing impact of fires on terrestrial ecosystems worldwide. Yet, fire regime changes in highly humanised fire-prone regions are difficult to predict because fire effects may be heavily mediated by human activities We investigated the role of fire suppression strategies in synergy with climate change on the resulting fire regimes in Catalonia (north-eastern Spain). We used a spatially-explicit fire-succession model at the landscape level to test whether the use of different firefighting opportunities related to observed reductions in fire spread rates and effective fire sizes, and hence changes in the fire regime. We calibrated this model with data from a period with weak firefighting and later assess the potential for suppression strategies to modify fire regimes expected under different levels of climate change. When comparing simulations with observed fire statistics from an eleven-year period with firefighting strategies in place, our results showed that, at least in two of the three sub-regions analysed, the observed fire regime could not be reproduced unless taking into account the effects of fire suppression. Fire regime descriptors were highly dependent on climate change scenarios, with a general trend, under baseline scenarios without fire suppression, to large-scale increases in area burnt. Fire suppression strategies had a strong capacity to compensate for climate change effects. However, strong active fire suppression was necessary to accomplish such compensation, while more opportunistic fire suppression strategies derived from recent fire history only had a variable, but generally weak, potential for compensation of enhanced fire impacts under climate change. The concept of fire regime in the Mediterranean is probably better interpreted as a highly dynamic process in which the main determinants of fire are rapidly modified by changes in landscape, climate and socioeconomic factors such as fire suppression strategies.

Align or not to align? Resolving species complexes within the Caloplaca saxicola group as a case study.

The Caloplaca saxicola group is the main group of saxicolous, lobed-effigurate species within genus Caloplaca (Teloschistaceae, lichen-forming Ascomycota). A recent monographic revision by the first author detected a wide range of morphological variation. To confront the phenotypically based circumscription of these taxa and to resolve their relationships morphological and ITS rDNA data were obtained for 56 individuals representing eight Caloplaca species belonging to the C. saxicola group. We tested the monophyly of these eight morphospecies by performing maximum parsimony, maximum likelihood and two different types of Bayesian analyses (with and without a priori alignments). Restricting phylogenetic analyses to unambiguously aligned portions of ITS was sufficient to resolve, with high bootstrap support, five of the eight previously recognized species within the C. saxicola group. However, phylogenetic resolution of all or most of the eight species currently included as two distinct subgroups within the C. saxicola group was possible only by combining morphological characters and signal from ambiguously aligned regions with the unambiguously aligned ITS sites or when the entire ITS1 and 2 regions were not aligned a priori and included as an integral component of a Bayesian analysis (BAli-Phy). The C. arnoldii subgroup includes C. arnoldii, comprising four subspecies, and the C. saxicola subgroup encompasses seven species. Contrary to the C. saxicola subgroup, monophyly of taxa included within the C. arnoldii subgroup and their relationships could not be resolved with combined ITS and morphological data. Unequivocal morphological synapomorphies for all species except C. arnoldii and C. pusilla are recognized and presented.

Community surveys through space and time: testing the space-time interaction in the absence of replication.

In order to test hypotheses about changes in the environment induced by man, including climatic change, ecologists are sampling portions of the environment repeatedly across time. This paper describes a method for testing a space-time interaction in repeated ecological survey data, when there is no replication at the level of individual sampling units (sites). This methodological development is important for the analysis of long-term monitoring data, including systems under anthropogenic influence. In these systems, an interaction may indicate that the spatial structure of community composition has changed in the course of time or that the temporal evolution is not the same at all sites. This paper describes ANOVA models corresponding to the steps leading to a solution to the problem, which is based on the representation of space and time by principal coordinates of neighbor matrices (PCNM eigenfunctions) in the ANOVA. Numerical simulations showed that ANOVA Model 5 was the model of choice for the analysis of the space-time interaction because it always had correct rates of Type I error, and its power was always equal to or higher than those of other possible models of analysis. If the hypothesis of absence of interaction is not rejected, one cannot conclude that a change has occurred in the spatial structure of the response data across time; one should follow the ordinary rules of two-way ANOVA if testing the significance of the main factors is of interest. If the hypothesis of absence of interaction is rejected, one should model the spatial structure of each time period in a separate way. One can also conduct a single test involving a separate model of the spatial structure for each time period. This paper presents two applications to real ecological data.

Associations between species and groups of sites: indices and statistical inference.

Ecologists often face the task of studying the association between single species and one or several groups of sites representing habitat types, community types, or other categories. Besides characterizing the ecological preference of the species, the strength of the association usually presents a lot of interest for conservation biology, landscape mapping and management, and natural reserve design, among other applications. The indices most frequently employed to assess these relationships are the phi coefficient of association and the indicator value index (IndVal). We compare these two approaches by putting them into a broader framework of related measures, which includes several new indices. We present permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals. Correlation measures, such as the phi coefficient, are more context-dependent than indicator values but allow focusing on the preference of the species. In contrast, the two components of an indicator value index directly assess the value of the species as a bioindicator because they can be interpreted as its positive predictive value and sensitivity. Ecologists should select the most appropriate index of association strength according to their objective and then compute confidence intervals to determine the precision of the estimate.

Beals smoothing revisited.

Beals smoothing is a multivariate transformation specially designed for species presence/absence community data containing noise and/or a lot of zeros. This transformation replaces the observed values of the target species by predictions of occurrence on the basis of its co-occurrences with the remaining species. In many applications, the transformed values are used as input for multivariate analyses. As Beals smoothing values provide a sense of "probability of occurrence", they have also been used for inference. However, this transformation can produce spurious results, and it must be used with caution. Here we study the statistical and ecological bases underlying the Beals smoothing function, and the factors that may affect the reliability of transformed values are explored using simulated data sets. Our simulations demonstrate that Beals predictions are unreliable for target species that are not related to the overall ecological structure. Furthermore, the presence of these "random" species may diminish the quality of Beals smoothing values for the remaining species. A statistical test is proposed to determine when observed values can be replaced with Beals smoothing predictions. Two real-data example applications are presented to illustrate the potentially false predictions of Beals smoothing and the necessary checking step performed by the new test.