Timothy Thrippleton
email: timothy.thrippleton@usys.ethz.ch

Lisa Hülsmann
email: lisa.huelsmann@biologie.uni-regensburg.de

Maxime Cailleret
email: maxime.cailleret@wsl.ch

Harald Bugmann
email: harald.bugmann@env.ethz.ch

Projecting forest dynamics across Europe: potentials and pitfalls of empirical mortality algorithms RUNNING HEAD: Potentials of empirical mortality algorithms

Keywords: Dynamic vegetation model, tree mortality, mortality models, forest dynamics, climate change, environmental gradient, Pinus sylvestris

Mortality is a key process of forest ecosystem dynamics and functioning strongly altering biomass stocks and carbon residence times. Dynamic vegetation models (DVMs) used to predict forest dynamics are typically based on simple, largely data-free ('theoretical') mortality algorithms (MAs). To improve DVM projections, the use of empirically-based MAs has been suggested, but little is known about their impact on DVM behavior.

A systematic comparison of eight MAs (seven inventory-based, one 'theoretical') for the pan-European tree species Pinus sylvestris L. was carried out within the DVM ForClim for present and future climate scenarios at three contrasting sites across Europe. Model accuracy was furthermore evaluated with empirical data from young and old-growth forests.

We found strongly diverging mortality patterns among the MAs for present climate. Based on their behavior, we identified two distinct empirical MA groups that were related to their structure (i.e., variables considered), but not to their geographic origin (i.e., the environmental conditions they were calibrated to). Under climate change, MAs based on a competition index produced ecologically inconsistent results, while MAs based on growth showed more plausible and less extreme behaviors. Furthermore, MAs based on growth reached a higher accuracy for projecting young and old-growth forest dynamics.

Our results demonstrate that using empirical MAs in DVMs has a high potential to better predict forest dynamics, but also a risk of yielding implausible results if their structure is inadequate.

For DVM applications across large spatiotemporal scales, we thus suggest using MAs based on growth, particularly under future no-analogue climates.

INTRODUCTION

The dynamics and functioning of forest ecosystems are inherently driven by demographic processes [START_REF] Fisher | Vegetation demographics in Earth System Models: A review of progress and priorities[END_REF], in particular tree mortality, which shapes forest structure, biomass and resource availability [START_REF] Franklin | Tree Death as an Ecological Process[END_REF]. Consequently, much research has focused on better understanding the processes of mortality and its ecological consequences [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF][START_REF] Lutz | Tree mortality during early forest development: A long-term study of rates, causes, and consequences[END_REF]. A particular challenge lies in the multifaceted nature of mortality, which is caused by various processes that act on a continuum from the individual level (dominated by competition for resources) to larger scales (where disturbanceimpacts predominate, e.g. windthrow and fire, [START_REF] Seidl | Forest disturbances under climate change[END_REF][START_REF] Thom | The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes[END_REF]. While significant progress has been achieved for disentangling drivers of disturbance-related mortality at the landscape scale (e.g., [START_REF] Temperli | Interactions among spruce beetle disturbance, climate change and forest dynamics captured by a forest landscape model[END_REF][START_REF] Scheller | Interactions Among Fuel Management, Species Composition, Bark Beetles, and Climate Change and the Potential Effects on Forests of the Lake Tahoe Basin[END_REF], a high uncertainty remains for individual-scale mortality [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF][START_REF] Das | Why do trees die? Characterizing the drivers of background tree mortality[END_REF]. This type of mortality typically comprises stress-related processes (such as competition and unfavorable environmental conditions, e.g. drought, [START_REF] Bigler | Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland[END_REF] and further causes of tree death (e.g., lightning strikes, mechanical breakage, etc., typically referred to as 'background mortality', [START_REF] Das | Why do trees die? Characterizing the drivers of background tree mortality[END_REF]. The uncertainty in individual-scale mortality has far-reaching consequences and can scale up to large variations in long-term projections of ecosystem dynamics and functioning [START_REF] Friend | Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2[END_REF][START_REF] Bugmann | Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale[END_REF].

Dynamic vegetation models (DVMs) have become an increasingly important tool for upscaling individual processes and projecting future forest dynamics [START_REF] Shugart | Gap models and their individual-based relatives in the assessment of the consequences of global change[END_REF]. However, individual-level mortality still remains poorly represented in DVMs, many of which rely on simplistic theoretical assumptions (e.g., [START_REF] Galbraith | Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change[END_REF], or still use the same algorithm structure defined in the first generation of DVMs [START_REF] Bugmann | A review of forest gap models[END_REF][START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF][START_REF] Bircher | The agony of choice: different empirical mortality models lead to sharply different future forest dynamics[END_REF]. This slow pace of progress can be explained by the complexity of physiological mechanisms and the interwoven nature of multiple processes involved [START_REF] Hartmann | Research frontiers for improving our understanding of drought-induced tree and forest mortality[END_REF] as well as difficulties in empirically studying the rare and episodic process of mortality [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF][START_REF] Weiskittel | Mortality. Forest Growth and Yield Modeling[END_REF]. While simple, 'theoretical' mortality algorithms (MAs) lack a strong empirical basis [START_REF] Wunder | Growthmortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests[END_REF], 'physiologically-based' MAs require detailed process-level data and are currently not applicable to a larger number of species and mortality sources [START_REF] Mcdowell | Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework[END_REF][START_REF] Hartmann | Research frontiers for improving our understanding of drought-induced tree and forest mortality[END_REF]. In the last years, an increasing number of studies have thus emphasized the benefits of using more empirically-based MAs that maintain a simple and generic structure, but are based on correlative relationships derived from large datasets [START_REF] Adams | Empirical and process-based approaches to climate-induced forest mortality models[END_REF][START_REF] Hülsmann | How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model[END_REF][START_REF] Vanoni | How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?[END_REF]. By synthesizing empirical mortality patterns encoded in large-scale datasets (e.g. national forest inventories, tree ring databases), empirical MAs may thus provide a potentially promising approach for a more accurate representation of mortality in DVMs [START_REF] Adams | Empirical and process-based approaches to climate-induced forest mortality models[END_REF][START_REF] Cailleret | A synthesis of radial growth patterns preceding tree mortality[END_REF].

Empirically-based MAs developed from forest inventories have a particular potential for the application in DVMs, as they are based on datasets covering large environmental gradients and a large number of species, thus facilitating their widespread application [START_REF] Hülsmann | How to predict tree death from inventory datalessons from a systematic assessment of European tree mortality models[END_REF].

However, a primary source of uncertainty of this approach lies in the extrapolation of the MAs beyond their calibration range, i.e., the range of conditions represented in the underlying data [START_REF] Adams | Empirical and process-based approaches to climate-induced forest mortality models[END_REF]. This effect is a key challenge in many ecological models [START_REF] Yates | Outstanding Challenges in the Transferability of Ecological Models[END_REF] and is particularly relevant when applying empirical MAs outside their geographic origin (i.e., the environmental conditions they were calibrated to). Furthermore, biased tree size ranges in the calibration datasets can lead to extrapolation errors [START_REF] Bircher | The agony of choice: different empirical mortality models lead to sharply different future forest dynamics[END_REF]. A good performance of MAs validated with empirical datasets under similar conditions (but outside of a DVM framework, e.g., [START_REF] Hülsmann | How to predict tree death from inventory datalessons from a systematic assessment of European tree mortality models[END_REF] may thus not necessarily imply good performance for long-term projections within a DVM. However, very few studies have analyzed the implications of using empirical MAs within a DVM framework (e.g., [START_REF] Wyckoff | The relationship between growth and mortality for seven cooccurring tree species in the southern Appalachian Mountains[END_REF], and little is known about their generality and limitations. This lack of knowledge is particularly crucial in view of climate change impacts: higher temperature and altered precipitation patterns may create 'no-analogue' conditions [START_REF] Williams | Novel climates, no-analog communities, and ecological surprises[END_REF] where MAs can become less robust or even implausible [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF].

The response of empirical MAs to climate change also depends on their structure, i.e. their predictor variables, and their parameter estimates [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF]. Most empirical MAs for individual-level mortality (i.e., unrelated to large-scale disturbances) are based on tree size (diameter at breast height, DBH), since high mortality is characteristic of small trees [START_REF] Hawkes | Woody plant mortality algorithms: description, problems and progress[END_REF] as well as tall trees, which are more vulnerable e.g. to hydraulic stress [START_REF] Bennett | Larger trees suffer most during drought in forests worldwide[END_REF]. Empirical MAs differ, however, in the way they consider environmental effects and competition (e.g., via growth or a competition index, [START_REF] Weiskittel | Mortality. Forest Growth and Yield Modeling[END_REF]. Consequently, not only geographic origin, but also MA structure needs to be considered, particularly under 'noanalogue' climate conditions.

The objective of the present study was to address these knowledge gaps by a systematic evaluation of multiple empirical MAs within a consistent DVM framework (ForClim; [START_REF] Bugmann | A simplified forest model to study species composition along climate gradients[END_REF] and to explore their effects on simulated forest development across a large environmental gradient. The study focuses on MAs developed for the species Pinus sylvestris

MATERIAL AND METHODS

Model ForClim

ForClim is a process-based DVM that simulates short-and long-term forest dynamics as an emergent property of environmental conditions and vegetation interactions [START_REF] Bugmann | A simplified forest model to study species composition along climate gradients[END_REF].

In this framework, a forest stand is divided into small patches (here 800 m 2 , i.e. the size of a canopy gap created by the death of one large tree), where the life-cycle of tree cohorts (groups of trees of the same species and age) is simulated at an annual resolution. Tree demographic processes, i.e. establishment, growth and mortality, are simulated for each cohort and driven by environmental conditions (i.e., temperature, light, water and nutrients).

Overall, the model has been widely applied throughout Europe (e.g., [START_REF] Bugmann | A simplified forest model to study species composition along climate gradients[END_REF][START_REF] Huber | Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions[END_REF] and other parts of the temperate zone, including North America and Asia (e.g., [START_REF] Shao | A comparative analysis of the structure and behavior of three gap models at sites in northeastern China[END_REF][START_REF] Gutierrez | Using a dynamic forest model to predict tree species distributions[END_REF]. For the present study, the most recent version of ForClim (v4.0.1) featuring an improved representation of establishment, growth and mortality (Huber et al., subm.) was used (see Appendix A1 for details about ForClim). All simulations for this study were also carried out with an earlier, much different version of ForClim (v3.3, used by [START_REF] Mina | Accurate modeling of harvesting is key for projecting future forest dynamics: a case study in the Slovenian mountains[END_REF], which yielded the same qualitative patterns and thus supports the robustness of our results to variations in DVM structure.

Mortality Algorithms

We compared seven inventory-based MAs developed for Pinus sylvestris by [START_REF] Monserud | Modeling individual tree mortality for Austrian forest species[END_REF], [START_REF] Eid | Models for individual tree mortality in Norway[END_REF], [START_REF] Palahi | Individual-tree growth and mortality models for Scots pine (Pinus sylvestris L.) in north-east Spain[END_REF], [START_REF] Trasobares | Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain[END_REF][START_REF] Bravo-Oviedo | Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L[END_REF][START_REF] Crecente-Campo | Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations[END_REF] and [START_REF] Hülsmann | How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model[END_REF]. These MAs were calibrated using data from National Forest Inventories (NFI), Permanent Sample Plots (PSP) or Natural Forest Reserves (NFR) (see Table 1). Geographically, they cover a cross-European gradient ranging from Mediterranean to temperate and boreal forests (Fig. 1).

Methodologically, all MAs are based on logistic regression models [START_REF] Monserud | Simulation of Forest Tree Mortality[END_REF][START_REF] Weiskittel | Mortality. Forest Growth and Yield Modeling[END_REF], in which mortality probability (pt) of tree i is expressed as:

pi, t = logit -1 (Xi ) = exp(Xi ) / (1 + exp(Xi )) (Eq. 1)
with Xi denoting the design matrix of the linear predictor and β the respective parameter vector.

In terms of algorithm structure, the MAs feature different predictor variables (Table 1). Most MAs use tree size (typically DBH) and competition (typically basal area of larger trees, BAL) as the main variables, with some models considering site index as indicator of its productivity.

Only two empirical MAs used growth as a predictor. A detailed description of the MAs including their predictor variables and their calculation in ForClim is given in Appendix A1.

Since many DVMs incorporate a similar 'theoretical' representation of mortality [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF], the 'theoretical' MA currently used in ForClim v4.0.1 was included for comparability. This MA comprises two components: (1) a species-specific 'background' mortality rate representing various mortality agents a tree is exposed to during its life (e.g., fungal infections, mechanical damage) based on the approach of [START_REF] Manusch | Tree mortality in dynamic vegetation models -A key feature for accurately simulating forest properties[END_REF], which incorporates a relationship between size and mortality. (2) A 'stress-induced' mortality component that represents increased mortality during times of slow growth [START_REF] Bugmann | A review of forest gap models[END_REF]. Details for the 'theoretical' MA are provided in Appendix A1.

Simulation approach

The simulations were carried out in three sets: Set 1 aimed at a systematic evaluation of the different MAs in ForClim under present and future climate conditions; Set 2 and Set 3 aimed at testing model accuracy for young and old-growth forest dynamics, respectively. Since the focus of this study was on mortality unrelated to disturbances, simulations for all three sets were carried out in the absence of management and other exogenous disturbances such as windthrows, wildfires, or insect outbreaks.

Set 1: Systematic evaluation of mortality algorithms

For a systematic and comprehensive evaluation of the MAs within ForClim, a full factorial design was used. This allowed for testing the behavior of the MAs under a wide range of combinations of environment, competition, and tree sizes under clearly defined boundary conditions. Simulations were initialized with a pre-defined (planted) cohort of 250 trees of Pinus sylvestris (in the following referred to as the 'target cohort'), which represents a cohort of sufficiently large size to trace tree mortality over a simulation time of 1000 years. The simulations were carried out for three specific tree sizes (i.e., DBH at beginning of the simulation), referred to as 'Small' (DBH of 5 cm), 'Intermediate' (DBH of 20 cm) and 'Large' (DBH of 40 cm). This setup was chosen to explore the range of tree sizes that are underlying the empirical MAs (Table 1). To investigate the effect of competition on the mortality of the 'target cohort', taller cohorts of Pinus sylvestris were included as competitors in the simulation.

They were initialized to produce a basal area of larger trees (BAL) of 10, 20, 30, 40 and 50 m 2 ha -1 , corresponding to the BAL range found in many calibration datasets [START_REF] Hülsmann | How to predict tree death from inventory datalessons from a systematic assessment of European tree mortality models[END_REF]. It was furthermore assumed that these larger 'competing' cohorts were able to grow but were not subject to mortality. These assumptions were necessary to avoid the confounding effects that (1) the 'target cohort' overgrew the competing cohort, and (2) higher mortality in the 'competing cohort' creates better growing conditions for the 'target cohort', thus leading to uncontrollable release effects at some point during the simulation. The simulations were carried out at three locations in Northern Europe (Jönköping, Sweden, temperature-limited conditions), Central Europe (Bern, Switzerland, optimum conditions) and Southern Europe (Léon, Spain, drought-limited conditions; Fig. 1) to test model behavior across a gradient of environmental conditions corresponding to their geographic origins (Table 1). Four climate scenarios were employed for this simulation set: one under current climatic conditions, and three assuming climate change scenarios with: (1) an increase in temperature by +4 °C ('warmer' scenario),

(2) an increase in both temperature and precipitation (+4 °C and +20%, respectively; 'warmer and moister' scenario), and (3) a temperature increase by + 4 °C and precipitation decrease by -20% ('warmer and drier' scenario). Details about site location, environmental conditions and climate change scenarios are given in Appendix A3.

Set 2: Accuracy of mortality in young forests

To evaluate the accuracy of mortality in young forest dynamics (i.e. short-term simulations, <150 years), results were compared to empirical self-thinning data from fully-stocked, unmanaged experimental plots by [START_REF] Pretzsch | Species-specific allometric scaling under self-thinning: evidence from longterm plots in forest stands[END_REF]. For this set, a Pinus sylvestris stand with a defined stem density (6460 indiv. ha -1 ) and tree size (mean DBH of 6.6 cm) was initialized to mimic a typical young stand growing under intermediate site conditions (site index of 20; see [START_REF] Lembcke | Ertragstafel für die Kiefer (Pinus sylvestris L.) im norddeutschen Tiefland[END_REF], and run for 150 years. Since the focus of this simulation experiment was merely on self-thinning, subsequent management or tree regeneration was not included. The simulations were carried out at the study site Coburg (see [START_REF] Huber | Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions[END_REF], located close to the experimental Pinus sylvestris plots of [START_REF] Pretzsch | Species-specific allometric scaling under self-thinning: evidence from longterm plots in forest stands[END_REF] in Northern Bavaria (Fig. 1). Further site details are given in Table S1 in Appendix A3.

Set 3: Accuracy of mortality in old-growth forests

For evaluating the accuracy of mortality under old-growth forest conditions, emerging stand structures from long-term simulations were compared to the empirical data from the Pfynwald forest reserve [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF]. It is located in the dry inner-alpine valley of the Valais in Switzerland, and represents one of the few unmanaged Pinus sylvestris forests in Central Europe that can be viewed as being in a dynamic equilibrium [START_REF] Leibundgut | Europäische Urwälder -Wegweiser zur naturnahen Waldwirtschaft[END_REF]. Stand structure in the reserve plots follows a typical reverse J-shaped diameter distribution with abundant regeneration below a canopy dominated by a few large trees [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF].

The simulations were started from bare ground with a simulation time of 1000 years assuming natural regeneration, as done in many simulation studies (e.g., [START_REF] Manusch | Tree mortality in dynamic vegetation models -A key feature for accurately simulating forest properties[END_REF]. Further site details are given in Table S1 in Appendix A3.

Analysis of simulation outputs

For characterizing the mortality projections of Set 1, a measure describing the intensity of mortality for each MA was applied, defined as the time when 50% of the initial target cohort had died (MT50%). To account for effects of changes in competition (BAL) or with changing climatic conditions, a second measure (MT50%) was applied. MT50% is defined as the change in MT50% due to a change in BAL or climate (MT50%,i) relative to a baseline (MT50%,b):

MT50% = (MT50%,b -MT50%,i) / MT50%,b (Eq. 2)
Positive values ofMT50% thus indicate a higher mortality relative to baseline conditions (note that higher mortality leads to a lower value of MT50% as a result of shorter tree lifespans).

Baseline conditions were defined as the respective lower competition scenario (for analysis of response to competition) and as the present climate scenario (for analysis of response to climate change).

For evaluating the accuracy of mortality in Set 2, simulated self-thinning was expressed as logarithmic stand density (tree number per ha) versus mean tree biomass and compared to the corresponding dataset by [START_REF] Pretzsch | Species-specific allometric scaling under self-thinning: evidence from longterm plots in forest stands[END_REF]. For the evaluation of Set 3, mean basal area and stem density at the end of the simulation were compared to the range of both variables reported in all permanent plots of the forest reserve by [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF].

RESULTS

Set 1: Comparison of MAs under present climate

Simulated mortality dynamics showed high variability among the eight MAs (Fig. 2 and Fig.

A4

.1, A4.2 in Appendix). The differences were most pronounced for the 'large' trees (DBH = 40 cm) and in the absence of larger competing trees (BAL = 0). Under these conditions, several

MAs projected exceedingly high survival rates with individual trees reaching ages of > 500 years. Increasing competition (i.e., higher values of BAL) increased the similarity of the mortality projections because all MAs predicted high mortality rates irrespective of their structure and parameter estimates. Notably, low-competition conditions resulted in faster growth, which led to an earlier exceedance of the DBH calibration range and thus an extrapolation of most MAs (indicated by dashed lines in Fig. 2).

The specific analysis of mortality intensity (defined as MT50%, i.e. the time when 50% of a cohort had died) and its sensitivity to competition (MT50%) also showed a clear differentiation among the eight MAs (Fig. 3). The different MAs followed an order of increasing MT50% and sensitivity to competition (Fig. 3a, direction bottom-left to top-right), starting from the 'theoretical' MA1 followed by MA2, 3, with the other MAs (4-8) reaching highest values.

Increasing BAL resulted in smaller differences but generally the same patterns between the MAs (Fig. 3b). Notably, the 'theoretical' MA showed a negative response to altered competition from BAL 0 to BAL 10 (Fig. 3a), i.e. a minor increase in competition in this range decreased mortality probability. Initial tree size had a consistent effect on mortality intensity (MT50%) and competition responses (MT50%) among the different MAs. While competition effects were highest for the 'small' cohort size, the effect decreased with increasing tree size. Increasing tree size also led to higher MT50% (i.e., lower mortality intensity), with the exception of MA3.

Mortality patterns among the three sites were heterogeneous, showing opposite rank orders in mortality intensity (MT50%) from Leon to Jönköping to Bern between two MA groups, i.e.

increasing MT50% for the MAs 2 and 3, but reverse order for MAs 5,6,7.

Based on the overall mortality patterns (Fig. 3) and the MA characteristics (Table 1) two groups were identified: MAs including a growth component ('Growth-based' MAs, i.e. the 'theoretical' MA1, as well as MAs 2 and 3) on the one hand, and MAs describing competition via a competition index ('CI-based' MAs, i.e. MAs 4-8) on the other hand. A higher similarity of MAs with a similar geographic origin (see Table 1) was however not evident (Fig. 3).

Set 1: Comparison of MAs under future climate

The changes of mortality intensity in the 'warmer' scenario (+4 °C) were relatively small across the sites (MT50% range from -0.12 to + 0.15), but a clearer differentiation emerged for the 'warmer and moister' (+4 °C, +20% precip.; MT50% range from -0.21 to + 0.30) and 'warmer and drier' (+4 °C, -20% precip.; MT50% range from -0.51 to + 0.13) scenarios (Fig. 4). The direction of the response to climate change was generally consistent across the MA groups identified in the previous section and across sites (with the exception of Jönköping under the 'warmer' scenario, where most MAs predicted less mortality). The highest magnitude of change occurred at Leon, while only minor changes occurred for Jönköping and Bern except under the 'warmer and drier' scenario (Fig. 4). The 'CI-based' MAs (4-8) featured mostly a counterintuitive pattern of higher mortality under a 'warmer and moister' scenario, but lower mortality under a 'warmer and drier' scenario. The 'Growth-based' MAs (1-3) showed the opposite response, i.e. they projected less mortality under the 'warmer and moister' but increasing mortality under the 'warmer and drier' climatic conditions (Fig. 4). In contrast to the sign of the response, the MAs showed little agreement regarding the magnitude of change (Fig. 4).

Set 2: Accuracy of mortality in young forests

In the self-thinning simulation experiment for young forests (age <150 years), the simulated relationships between stem density and biomass showed distinct differences among the MAs as well as to those reported by [START_REF] Pretzsch | Species-specific allometric scaling under self-thinning: evidence from longterm plots in forest stands[END_REF] for unmanaged Pinus sylvestris stands (Fig. 5).

While the 'theoretical' MA (1) as well as the MAs 2 and 8 were within the range of the observed data, the other MAs tended to underestimate mortality for all tree sizes (Fig. 5).

Set 3: Accuracy of mortality for old-growth forests

The long-term simulations at Pfynwald showed substantial differences in projected stand structure according to the MA that was used (Fig. 6), with 'Growth-based' MAs (1-3) producing more accurate results than 'CI-based' MAs (4-8). 'Growth-based' MAs predicted stand basal areas and stem densities that were close to reality, although some of these MAs tended to overestimate basal area and underestimate stem density (Fig. 6a,b). They also reproduced the pattern of abundant regeneration below a canopy dominated by a few, large trees (Fig. 6c1-3) as reported by [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF]. In contrast, the 'CI-based' MAs consistently overestimated basal area and underestimated stem density (Fig. 6a,b). This was due to a simulated stand structure dominated by exceedingly large trees (>60 cm DBH) with practically no regeneration or mid-storey (Fig. 6c4-8), which was in stark contrast to observed stand structure (Fig. 6c9).

DISCUSSION

We found strong differences in DVM projections caused by the eight investigated MAs, which were largely associated with MA structure (particularly differentiating 'Competition index (CI)-based' and 'Growth-based' MAs), but not with their geographical origin. Below, we discuss these findings as well their implications for projecting the dynamics and functioning of forest ecosystems at large spatio-temporal scales.

General evaluation of MA behavior

Previous studies that investigated empirical MAs outside a DVM framework (e.g., [START_REF] Hülsmann | How to predict tree death from inventory datalessons from a systematic assessment of European tree mortality models[END_REF] demonstrated the importance of the properties of the calibration dataset (geographic origin, DBH range, etc.). Given the wide geographical and environmental range covered by the MAs in our study (Table 1), we expected a strong influence of the MA's geographical origin, reflecting the different climatic drivers prevailing in the calibration range (e.g., low temperatures in boreal forests or drought in Mediterranean forests, see [START_REF] Kullman | Tree-limit stress and disturbance -A 25-year survey of geoecological change in the scandes mountains of Sweden[END_REF]Galiano et al., 2012, respectively). However, our study showed that within a DVM framework, geographic origin was surprisingly unimportant compared to MA structure. This behavior was largely due to the representation of the direct and indirect effects of competition and the environment.

The 'CI-based' MAs incorporate variables that are directly associated with stand structure and competition (e.g., BAL, stem density, [START_REF] Eid | Models for individual tree mortality in Norway[END_REF][START_REF] Crecente-Campo | Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations[END_REF]). In the simulations, better environmental conditions led to higher diameter growth, which in turn increased BAL. Consequently, trees experienced more competition and a higher mortality probability under better site conditions (favorable temperature and water availability) than under unfavorable conditions (e.g., [START_REF] Sterba | Forest Decline and Increasing Increments -a Simulation Study[END_REF][START_REF] Monserud | Modeling individual tree mortality for Austrian forest species[END_REF]. Most 'CI-based'

MAs furthermore take into account environmental effects indirectly via a site index (see also Appendix A2). Instead of a site-specific representation of important environmental mortality sources (e.g., impacts of frost, heat or drought, [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF][START_REF] Vanoni | Drought and frost contribute to abrupt growth decreases before tree mortality in nine temperate tree species[END_REF], site index typically accounts for higher mortality under better site conditions due to stronger competition and faster turnover [START_REF] Weiskittel | Mortality. Forest Growth and Yield Modeling[END_REF]. While this approach is appropriate for applied questions of forest management under present climate, as shown in a number of studies (e.g., [START_REF] Eid | Models for individual tree mortality in Norway[END_REF][START_REF] Bravo-Oviedo | Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L[END_REF], our results demonstrate that it becomes problematic in global-change applications at larger scales. The lack of a variable accounting for the direct effect of harsh environmental conditions on mortality leads to a relatively low sensitivity of the 'CI-based' MAs to geographic origin. Moreover, the strong emphasis on competition (with unfavorable environmental conditions leading to a decrease in competition)

causes the counter-intuitive pattern of a lower mortality intensity for sites with harsher environmental conditions (cf. Jönköping), which is at odds with large-scale patterns of forest mortality due to climatic stress [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF][START_REF] Thurner | Largescale variation in boreal and temperate forest carbon turnover rate related to climate[END_REF][START_REF] Greenwood | Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area[END_REF].

In contrast, 'Growth-based' MAs (1-3) assume that slow growth leads to increased mortality probability, as frequently observed in dendroecological studies (e.g., [START_REF] Bigler | Predicting the time of tree death using dendrochronological data[END_REF][START_REF] Cailleret | A synthesis of radial growth patterns preceding tree mortality[END_REF]. This relationship leads to an increasing mortality under high levels of competition, which is typically observed in small size classes [START_REF] Hawkes | Woody plant mortality algorithms: description, problems and progress[END_REF], particularly during the early stages of forest development (e.g., [START_REF] Lutz | Tree mortality during early forest development: A long-term study of rates, causes, and consequences[END_REF]. With respect to unfavorable environmental conditions, this group of MAs responds with an increasing mortality probability (as reported e.g. for trees impacted by drought or frost, [START_REF] Vanoni | Drought and frost contribute to abrupt growth decreases before tree mortality in nine temperate tree species[END_REF]. The focus on growth as an indicator for vitality integrates the effect of unfavorable environmental conditions (e.g., limitations by light, temperature, water) on decreased photosynthesis, changes in carbon allocation patterns and ultimately decreased survival probability [START_REF] Dobbertin | Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review[END_REF][START_REF] Wunder | Growthmortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests[END_REF]. Our results show a higher intensity of mortality at sites characterized by strong drought and temperature limitations (i.e., Jönköping and Léon) for the 'Growth-based' MAs, which is in line with both fundamental principles of stress mortality [START_REF] Franklin | Tree Death as an Ecological Process[END_REF][START_REF] Niinemets | Responses of forest trees to single and multiple environmental stresses from seedlings to mature plants: Past stress history, stress interactions, tolerance and acclimation[END_REF] as well as patterns of climate-related mortality reported at larger scales [START_REF] Thurner | Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests[END_REF].

Among the 'Growth-based' MAs, the 'theoretical' MA of ForClim showed a unique behavior featuring a lower mortality probability with a small increase in competition (cf. Fig. 3), which allowed trees to reach older ages. Noticeably, the 'theoretical' MA takes into account maximum size, following the approach of [START_REF] Manusch | Tree mortality in dynamic vegetation models -A key feature for accurately simulating forest properties[END_REF] (see Appendix A1 for details). This design leads to two counteracting mechanisms: at lowest competition (BAL 0), trees reach their maximum size faster, which decreases their life expectancy. A minor increase of competition (BAL 10), however, reduces growth moderately, which thus allows trees to reach higher longevity. At first sight, this behavior is counter-intuitive, but it is actually representing the trade-off between early growth rate and lifespan that has been observed across multiple tree taxa [START_REF] Bigler | Increased early growth rates decrease longevities of conifers in subalpine forests[END_REF][START_REF] Bigler | Trade-Offs between Growth Rate, Tree Size and Lifespan of Mountain Pine (Pinus montana) in the Swiss National Park[END_REF].

Overall, we found that within a DVM framework, differences in MA structure (particularly the representation of competition vs. the environment) played a decisive role, with only MAs based on growth producing ecologically plausible behavior across a wide environmental gradient.

Response of MAs to climate change

An adequate representation of mortality in DVMs is particularly important in the context of climate change projections [START_REF] Keane | Tree mortality in gap models: Application to climate change[END_REF], and can have far-reaching implications for global vegetation cover and the carbon cycle [START_REF] Adams | Empirical and process-based approaches to climate-induced forest mortality models[END_REF][START_REF] Friend | Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2[END_REF][START_REF] Thurner | Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests[END_REF]. Previous studies have shown that DVM projections under climate change can vary drastically with alternative MA formulations (e.g., [START_REF] Bircher | The agony of choice: different empirical mortality models lead to sharply different future forest dynamics[END_REF][START_REF] Bugmann | Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale[END_REF].

The results from the present study extend these findings to a wide range of geographically and structurally different MAs and furthermore highlight that even opposite DVM responses to climate change can occur, depending on the type of MA structure. The two MA groups identified here under present climatic conditions exhibited similar behavior under climate change scenarios, which was due to the same underlying mechanisms (cf. preceding section).

Under warmer and drier climatic conditions, the 'CI-based' MAs decreased the mortality rates due to the reduction of competition intensity. This behavior is in stark contrast to various empirical and modeling studies showing substantial increases in drought-related mortality with ongoing climate change [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF][START_REF] Anderegg | Consequences of widespread tree Mortality triggered by drought and temperature stress[END_REF][START_REF] Greenwood | Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area[END_REF].

The 'CI-based' MAs may thus be suitable for short-term applications under analog climatic conditions (e.g., [START_REF] Crecente-Campo | Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations[END_REF], but they are likely to be structurally inadequate to represent climate change effects on mortality and thus may lead to unexpected and extreme behaviors in DVMs (cf. [START_REF] Williams | Novel climates, no-analog communities, and ecological surprises[END_REF] and [START_REF] Yates | Outstanding Challenges in the Transferability of Ecological Models[END_REF] for similar problems in other ecological models). The behavior of the 'Growth-based' MAs (relying on growth as a climate-sensitive indicator for tree [START_REF] Dobbertin | Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review[END_REF] was in much better agreement with expected mortality responses under a warmer and drier climate [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF][START_REF] Allen | On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene[END_REF]. The results from our study thus suggest that 'Growth-based' MAs provide an ecologically consistent and more robust alternative for projecting climate changerelated mortality in DVMs.

From a methodological point of view, it should be noted that MAs based on forest inventories or other data sources that do not feature an annual resolution tend to underestimate the effects of abrupt growth changes due to shorter-term environmental impacts and may fail to predict mortality induced by extreme events, especially for angiosperms [START_REF] Cailleret | A synthesis of radial growth patterns preceding tree mortality[END_REF]. A potential solution to this problem may lie in an a fusion approach of inventory and tree-ring based MAs, as recently provided by [START_REF] Vanoni | How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?[END_REF]. The benefit of annual data for improving empirical MAs could be further increased if information on tree mortality causes at different size classes were recorded (e.g., [START_REF] Lutz | Tree mortality during early forest development: A long-term study of rates, causes, and consequences[END_REF][START_REF] Das | Why do trees die? Characterizing the drivers of background tree mortality[END_REF]. These datasets are of particular value, as they allow to disentangle the multifactorial nature of mortality and to identify climate-sensitive drivers (e.g., the role of insects, see [START_REF] Das | Why do trees die? Characterizing the drivers of background tree mortality[END_REF].

Evaluation of MA accuracy

Evaluating DVM dynamics at different developmental stages (as in our case 'young' and 'oldgrowth' forests) is of key importance for assuring accurate projections of forest dynamics [START_REF] Brazhnik | SIBBORK: A new spatially-explicit gap model for boreal forest[END_REF][START_REF] Huber | Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions[END_REF]. Compared to empirical patterns for both developmental stages, the 'Growth-based' MAs (particularly MA 1 and 3) performed best, while the 'CI-based' MAs (4-8) were mostly outside the observed data range. A consistent result was the underestimation of mortality in the self-thinning stage (young forest simulations)

and over-predictions of the abundance of large, old trees (old-growth simulations) for the 'CIbased' MAs, which led to a failure of reproducing a reverse J-shaped diameter distribution [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF]. Most 'CI-based' MAs were developed for relatively short-term projections in a forest management context and were fitted to NFI or PSP data [START_REF] Eid | Models for individual tree mortality in Norway[END_REF][START_REF] Crecente-Campo | Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations[END_REF]. In these managed forests, large trees are strongly underrepresented, as trees are typically harvested at target diameters of ca. 40-60 cm (typical rotation periods for Pinus sylvestris <120 years, [START_REF] Holgen | Recreation values of boreal forest stand types and landscapes resulting from different silvicultural systems: An economic analysis[END_REF]. As these biased dataset lack significant mortality of larger size classes (see [START_REF] Monserud | Modeling individual tree mortality for Austrian forest species[END_REF][START_REF] Weiskittel | Mortality. Forest Growth and Yield Modeling[END_REF], the extrapolation of 'CI-based' MAs in a DVM can lead to a situation where the largest, dominant trees have a very low mortality probability. This behavior can also be observed in Fig. 2, where exceedingly high longevities (> 500 years) were projected (while empirical measurements only rarely report ages > 300 years for Pinus sylvestris, e.g. [START_REF] Mason | Spatial structure of semi-natural and plantation stands of Scots pine (Pinus sylvestris L.) in northern Scotland[END_REF]. This problem has been noted in some studies developing 'CI-based' MAs, but is generally considered as unimportant for short-term applications in a management context (e.g., [START_REF] Trasobares | Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain[END_REF]. The 'Growth-based' MAs, in contrast, were developed with the objective of representing longer-term dynamics, and thus they included relatively long monitoring times (>30 years), and in the case of MA 2 [START_REF] Hülsmann | How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model[END_REF] focused on strict forest reserves. Although very large trees are underrepresented even in strict forest reserves in Central Europe [START_REF] Hülsmann | How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model[END_REF], the longer time scales of monitoring increase the probability of adequately characterizing infrequent, episodic mortality events [START_REF] Lutz | Tree mortality during early forest development: A long-term study of rates, causes, and consequences[END_REF]. Altogether, the higher mortality rates led to a to a smaller number of large trees and a continuous regeneration at the forest floor for the 'Growth-based' MAs, which was consistent with the observations from old-growth Pinus sylvestris reserves in both the Pfynwald and Northern Europe [START_REF] Brang | Wenn Waldbrand die Uhr auf Null zurückstellt[END_REF][START_REF] Wood | Ecological survey of the native pinewoods of Scotland 1971[END_REF].

Implications for projecting dynamics and functioning of forest ecosystems

Changes in forest structure are inextricably linked to altered ecosystem functioning (e.g., [START_REF] Rödig | The importance of forest structure for carbon fluxes of the Amazon rainforest[END_REF]. This aspect is of particular importance, as DVMs play a key role for projecting future forest structure and functioning at large spatio-temporal scales [START_REF] Thom | The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes[END_REF][START_REF] Shugart | Gap models and their individual-based relatives in the assessment of the consequences of global change[END_REF]. Our results show that different MAs can lead to drastically different projections of forest structure, thus highlighting the relevance of carefully selecting appropriate empirical MAs at large scales.

With respect to spatial scales, DVMs are increasingly used in combination with remote sensing data to project carbon stocks and fluxes in forests at the regional to continental scale (e.g., [START_REF] Shuman | Sensitivity of Siberian larch forests to climate change[END_REF][START_REF] Rödig | The importance of forest structure for carbon fluxes of the Amazon rainforest[END_REF]. Applying structurally inappropriate empirical MAs (i.e. insufficient representation of climate drivers) can cause severely erroneous DVM behaviors at this scale, with effects likely to increase towards regions where climate-related stress (e.g., water or temperature limitation) becomes pivotal [START_REF] Boisvenue | Impacts of climate change on natural forest productivityevidence since the middle of the 20th century[END_REF][START_REF] Thurner | Largescale variation in boreal and temperate forest carbon turnover rate related to climate[END_REF]. An MA underestimating climate-related mortality can thus lead to a strong overestimation of biomass and carbon stocks in these regions. Furthermore, effects on microclimate and hydrology may be underestimated, which are progressively explored with DVMs at large spatial scales as well (e.g., [START_REF] Yang | Multicriteria evaluation of discharge simulation in Dynamic Global Vegetation Models[END_REF][START_REF] Thom | The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes[END_REF].

With respect to temporal scales, our results show that the effects of MA choice increase with projection timespan and the magnitude of the climate change scenario applied. As shown for the underestimated mortality for large trees in the case of the 'CI-based' MAs, this can result in the long-term buildup of an excessive carbon stock and an overestimation of carbon residence time in terrestrial vegetation (see also [START_REF] Friend | Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2[END_REF].

In conclusion, the use of empirical MAs in DVMs offers a high potential but requires particular caution regarding the choice of the MA structure. Our results suggest that MAs based on growth offer a suitable structure, especially for DVM applications to project forest dynamics and functioning at large spatio-temporal scales.

3Figure 1 :Figure 2 :8Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 123456 Figure 1: Geographic origin of empirical mortality algorithms (marks indicate the country or

  FIGURES

TABLE LEGENDS :Table 1 :

 LEGENDS1 Mortality algorithms (MAs) and their type, geographic origin, calibration data (NFI:

				Country/
	MA Type MA-Nr Study National Forest Inventory, PSP: Permanent Sample Plots, NFR: National Forest Reserves), Region Data Type Time DBHmin DBHmax		Predictor variables	Site
	observation period between the first and last inventories (Time), measured diameter ranges in		DBH	Growth	BAL	Density	index
	(in prep) the calibration dataset (DBHmin and DBHmax in cm), as well as their predictor variables (Size: Theoretical 1 Huber et al. ----	-	x	x
	diameter at breast height (DBH), Growth: radial tree growth, Density: number of trees per ha, Empirical 2 Hülsmann et Lower Saxony, NFR 1961 -2014 4	78	x	x
	Site index: height of dominant trees at a specific age). For further information about empirical al. (2018) Germany, and
	MAs and their variables, see Appendix A2. Additional information about the calibration Switzerland
	(2003) datasets are given in table A2. Empirical 3 Palahi et al.	Spain North-East	PSP	1964 -2000	5	55.6		x	x
	Empirical	4	Trasobares et	Catalonia,	NFI,	1989 -2001	7.5	76.4	x		x
			al. (2004)	Spain	subset
	Empirical	5	Monserud and	Austria	NFI	1981 -1990	5	(50)	x		x
			Sterba (1999)
	Empirical	6	Bravo-Oviedo	Spain	PSP	1960-	4.1	69.1	x		x		x
			et al. (2006)
	Empirical	7	Eid and Tuhus	Norway	NFI	1986 -1998	5	46.3	x		x		x
			(2001)
	Empirical	8	Crecente-	Galicia, Spain	PSP	1996 -2003	5	49.2	x		x	x	x
			Campo et al.
			(2010)

ACKNOWLEDGEMENTS

This work was funded by Swiss National Science Foundation project 'Advanced Tree MOrtality MOdeling' (ATMO^2, project number 163250). LH received funding by the Bavarian Ministry of Science and the Arts in the context of the Bavarian Climate Research Network (bayklif). We are grateful for the support by Dominic Michel in all IT-related questions. We furthermore thank Peter Brang, Jonas Stillhard and the Swiss Forest Reserve Project, a joint endeavor of the Swiss Federal Research Institute WSL, the Swiss Federal Institute of Technology (ETH) Zurich and the Swiss Federal Office of the Environment, for providing access to the Pfynwald data. We also acknowledge the helpful comments of two anonymous reviewers.

L., which has a pan-European distribution [START_REF] Mátyás | EUFORGEN Technical Guidelines for genetic conservation and use for Scots pine (Pinus sylvestris)[END_REF] and thus allows for a cross-European comparison. In particular, we addressed the following research questions:

(1) How similar are mortality dynamics under present climate when simulated with the DVM ForClim in combination with different MAs? In particular, can clusters of MAs with similar behavior be associated with their geographic origin, MA structure, or other factors?

(2) How sensitive are simulation results with different MAs to climate change?

(3) How accurate are the DVM results with different MAs when confronted with independent empirical datasets for young and old-growth forests? baseline. Note that symbol size indicates initial tree size, symbol type indicates the study site, 16 and symbol color indicates the different MAs. Note that cases with an MT50% > 1000 years (i.e. 17 those that did not reach 50% of mortality during the entire simulation period) cannot be 18 displayed.


Rémi Le Guern

Caroline Loïez

Sylvie Armand

Laure Marceau

René Courcol

Frédéric Wallet
email: frederic.wallet@chru-lille.fr

Infective endocarditis: does a new 16S rDNA set of primers improve the microbiological diagnosis?

Keywords: Infective endocarditis, cardiac valve, 16S rDNA amplification, PCR

Background: In infective endocarditis (IE), blood cultures are negative in 2.5-31% of cases because of an antimicrobial treatment previously prescribed. Molecular methods may represent an alternative to conventional microbiological techniques in order to identify the causative agent.

Objectives:

The aim of this prospective study was to evaluate the performance of a new primer pair (341F/785R) for 16S rDNA amplification in heart valves compared with primers 91E/13BS already used for the diagnosis of IE. 341F/785R primer pair was previously selected in silico to allow 16S rDNA amplification for a large coverage of bacterial species.

Results: Seventy-four patients suspected of infective endocarditis (IE) were included in this study.

Infective endocarditis was diagnosed in 55 of these patients using the modified Duke criteria, which was the gold standard here. 91E/13BS primers were more sensitive than 341F/785R primers: 38/55 (69.1%) samples were positive using 91E/13BS primers against 28/55 (50.9%) with 341F/785R (p=0.013). When at least one of the two molecular methods was positive, the sensitivity and specificity of 16S rDNA amplification was 72.7% and 94.7%, respectively.

Conclusion:

Even if the new primer pair 341F/785R seemed promising in silico, it was less sensitive for 16S rDNA amplification in heart valves than the 91E/13BS pair already used. This study underlines a lack of standardisation for 16S rDNA amplification on clinical samples.

Introduction

Infective endocarditis (IE) remains a serious infectious disease: indeed, the incidence has not changed much in recent times and mortality is still high (ranging from 9.6 to 26%) [START_REF] Habib | Guidelines on the prevention, diagnosis, and treatment of infective endocarditis (new version 2009): the Task Force on the Prevention, Diagnosis, and Treatment of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and the International Society of Chemotherapy (ISC) for Infection and Cancer[END_REF]. A collaborative approach between infectious disease specialists, surgeons and microbiologists is required to treat these patients, and a conventional microbiological culture is essential to find the etiologic agent and to adapt the antimicrobial treatment. Among the modified Duke criteria validated in several studies to make a positive diagnosis of endocarditis, blood cultures (BCs) remain the gold standard to isolate and identify the etiologic agent [START_REF] Heiro | Diagnosis of infective endocarditis. Sensitivity of the Duke vs von Reyn criteria[END_REF][START_REF] Murdoch | Clinical presentation, etiology, and outcome of infective endocarditis in the 21st century: the International Collaboration on Endocarditis-Prospective Cohort Study[END_REF].

Unfortunately, BCs are negative in 2.5-31% of suspected IE cases with an average of 10-15% based on to three reasons: prior antibiotic treatment, fastidious organisms such as Brucella spp. or fungi, and intracellular bacteria such as Coxiella burnetii, Bartonella spp., or Tropheryma whipplei [START_REF] Berbari | Infective endocarditis due to unusual or fastidious microorganisms[END_REF][START_REF] Fournier | Comprehensive diagnostic strategy for blood culture-negative endocarditis: a prospective study of 819 new cases[END_REF][START_REF] Hoen | Infective endocarditis in patients with negative blood cultures: analysis of 88 cases from a one-year nationwide survey in France[END_REF][START_REF] Houpikian | Blood culture-negative endocarditis in a reference center: etiologic diagnosis of 348 cases[END_REF][START_REF] Werner | for E. A 10-year survey of blood culture negative endocarditis in Sweden: aminoglycoside therapy is important for survival[END_REF]. In the case of surgical IE, heart valve culture can be performed to identify the causative agent, especially when the BCs remain negative. Unfortunately, the same reasons previously cited may explain a negative culture result.

To overcome these problems, culture-independent molecular techniques based on 16S rDNA gene sequencing have been developed and applied to many samples (e.g. blood, cardiac valve). These polymerase chain reactions (PCRs) are suitable for laboratories able to develop and validate these new molecular techniques with dedicated personnel [START_REF] Breitkopf | Impact of a molecular approach to improve the microbiological diagnosis of infective heart valve endocarditis[END_REF][START_REF] Goldenberger | Molecular diagnosis of bacterial endocarditis by broad-range PCR amplification and direct sequencing[END_REF][START_REF] Greub | Diagnosis of infectious endocarditis in patients undergoing valve surgery[END_REF][START_REF] Kotilainen | Aetiological diagnosis of infective endocarditis by direct amplification of rRNA genes from surgically removed valve tissue. An 11-year experience in a Finnish teaching hospital[END_REF][START_REF] Marin | Molecular diagnosis of infective endocarditis by real-time broad-range polymerase chain reaction (PCR) and sequencing directly from heart valve tissue[END_REF][START_REF] Vollmer | 23S rDNA real-time polymerase chain reaction of heart valves: a decisive tool in the diagnosis of infective endocarditis[END_REF][START_REF] Vondracek | 16S rDNA sequencing of valve tissue improves microbiological diagnosis in surgically treated patients with infective endocarditis[END_REF]. The efficiency of 16S rDNA PCR amplification is dependent on primer sequences. Ideally, there should be no primer-template mismatches. A number of nucleotide sequences of 16S rDNA are highly conserved, and they are therefore often chosen for primer design. However, even these conserved sequences still vary to some degree for different bacterial species. Recently, the bacterial coverage and phylum spectrum of 512 primer pairs were evaluated in silico, in order to reduce the bias in PCR-based microbial studies. One primer pair (341F/785R) seemed particularly promising according to Klindworth et al. [START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencingbased diversity studies[END_REF]. This primer pair showed excellent coverage of bacterial species, with few primer-template mismatches. However, this 341F/785R primer pair was only evaluated for PCR-based microbial studies and not for the identification of pathogenic bacteria directly from clinical samples.

The aim of the present work was to compare the sensitivity of 16S rDNA PCR amplification in heart valves using these new 341F/785R primers, versus the older ones (91E/13BS) [START_REF] Gauduchon | Molecular diagnosis of infective endocarditis by PCR amplification and direct sequencing of DNA from valve tissue[END_REF] already utilized in the diagnosis of IE.

Materials and methods

Clinical Specimens and Patients

Consecutive native heart valve specimens sent to the Clinical Bacteriology Laboratory between January 1, 2014 and December 31, 2014 were included. Eighty-two heart valves and four vegetations were collected from 74 patients. These fragments were aseptically removed from patients submitted to cardiac surgery for suspected IE. They were sampled and transported in sterile containers. The heart valves were processed in class 2 biosafety cabinet.

The clinical features (Duke criteria) and other biological parameters were prospectively collected as follows: blood cultures sampled before and during surgery, antimicrobial therapy prescribed, and serology for intracellular bacteria.

Conventional culture

The valve tissue was vortexed beforehand in 1 ml of brain-heart broth. It was then cultured onto 5% sheep blood, chocolate + isovitalex agar and in a paraffined brain-heart broth and a Todd-Hewitt broth. The plates were incubated both in an anaerobic atmosphere and under a carbon dioxide atmosphere (5%) for 10 days at 37°C. The broths were kept for 15 days. A fragment of the valve was deep frozen at -80°C. The isolated bacteria were identified by MALDI-TOF mass spectrometry (Bruker Daltonics, Wissembourg, France). Results of the May-Grünwald-Giemsa and Gram stains were reported as direct microscopic examination of imprints as follows: rare (≤ 1 leukocyte or bacteria per oil immersion [X 1000] field), 1+ (2-9 per oil immersion [X 1000] field), 2+ (10-50 per oil immersion [X 1000] field), and 3+ (> 50 per oil immersion [X 1000] field).

Molecular Microbiology

A sample of the heart valve was excised and added to a bead-containing tube with 200 µL of PCR-grade water for mechanical lysis with Magna Lyser (Roche Diagnostic, Mannheim, Germany). The NucleoSpin Tissue (Macherey-Nagel, Hoerdt, France) was used following the manufacturer's instructions. The resulting 100 µL of eluate was stored at -20°C until utilization. The following primers were used: PCR1 (old set): 91E (5'-TCAAAKGAATTGACGGGGGC-3') and 13BS (5'-GCCCGGGAACGTATTCAC-3'); PCR2 (new set) 341F (5'-CCTACGGGNGGCWGCAG-3') and 785R (5'-GACTACHVGGGTATCTAATCC-3'). Using Escherichia coli K12 as a reference template (Genebank accession number NR_102804.1), 91E/13BS produces a 478-bp amplicon wheareas 341F/785R produces a 465-bp amplicon.

PCR optimization of cycling conditions, primer concentration and MgCl 2 concentration was carried out using a Staphylococcus aureus DNA template and infected heart valve samples.

The employment of a purified Taq polymerase was necessary for 16S rDNA sequencing from clinical samples because Taq polymerases are often contaminated with bacterial DNA [START_REF] Hughes | Identification and elimination of DNA sequences in Taq DNA polymerase[END_REF].

The PCR amplification mixture (50 µL) was composed of: 5 µL of PCR Buffer 10X (Eurogentec, Seraing, Belgium), 3 µL of MgCl 2 (25 mM), 2 µL of dNTP (20 mM), 2.5 µL of forward primer (10 µM), 2.5 µL of reverse primer (10 µM), 0.25 µL of purified Hot Diamond Taq polymerase (5 U/µL) (Eurogentec, Seraing, Belgium), 29.75 µL of PCR-grade water, and 5 µL of extracted DNA. The following PCR conditions were used: for 91E/13BS -initial denaturation at 95°C for 3min, then 30 cycles of 94°C for 30s, 53°C for 30s, 72°C for 45s, and a final extension step at 72°C for 7 min; for 341F/785R -initial denaturation at 95°C for 3min, then 33 cycles of 94°C for 30s, 55°C for 30s, 72°C for 45s, and a final extension step at 72°C for 7min. Amplification of albumin was used as a positive extraction control.

Each amplification product was purified using Nucleospin Gel PCR Clean-Up (Macherey Nagel). Big Dye Terminator v3.1 kit was utilized for DNA sequencing. The same primers pairs were employed for sequencing. PCR conditions were 25 cycles of 94°C for 10s, 50°C for 5s, and 60°C for 240s. PCR products were purified onto NucleoSeq columns (Macherey-Nagel, Hoerdt, France). DNA was sequenced using a 3500 Dx Genetic Analyzer (Life Technologies, Cergy-Pontoise, France). Species identification was based on Genbank sequences yielding a >= 99% sequence similarity score, and genus identification on Genbank sequences yielding a >= 97% sequence similarity score [START_REF] Drancourt | 16S ribosomal DNA sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates[END_REF].

Results

A total of 74 patients (54 men, 20 female, median age = 61) were included in this study corresponding to 39 mitral valves, 39 aortic valves, 3 tricuspid valves, 1 pulmonary valve, and 4 vegetations. IE was diagnosed for 55 of these patients using modified Duke criteria.

Conventional culture identified a microbiological agent for 48/55 patients (87.2%): 43 (78.2%) for blood culture and 16 (29.1%) for heart valve culture (Table 1). An antimicrobial treatment that could hinder the valve culture was initiated before surgery in 50/55 cases (90.9%). Bacteria were seen on the Gram stain for 14/55 patients (25.5%) (Table I

and II).

16S rDNA could be amplified with at least one set of primers in 40/55 cases (72.7%). 91E/13BS primers were more sensitive than 341F/785R primers: 38/55 (69.1%) samples were positive using 91E/13BS primers against 28/55 (50.9%) with 341F/785R (Table 2). This difference was statistically significant using the McNemar test (p=0.013). 16S rDNA amplification was positive in all patients with bacteria present on the Gram stain (14/14, 100%), whereas it was positive in only 26/41 (63.4%) patients where no bacteria had been seen on the Gram stain.

For 19 patients, the diagnosis of IE was excluded using the modified Duke criteria, and they were included as controls. Streptococcus mitis/oralis DNA was amplified using 16 rDNA sequencing in 1 of these cases (5.3%). This patient was hospitalized for a recent valvular regurgitation and presented a 38.5°C fever, but blood cultures remained sterile. Even if the patient had not fulfill Duke's criteria, IE could not be ruled out as viridans-group streptococci are often associated with endocarditis.

Seven patients had culture-negative endocarditis. 16S rDNA was amplified for 5/7 (71%) of these patients. The empirical antimicrobial therapy was ineffective for patient number 45 (Table 2): the patient was treated with third-generation cephalosporins but Enterococcus faecalis DNA was found in his aortic heart valve using 16S rDNA PCR. On the other hand, the empirical antimicrobial therapy could be de-escalated for patients 13, 18 and 49. Patient 73 corresponded to a Bartonella endocarditis (Table III). Finally, no causative microbiological agent could be identified for 2 patients who had a definite diagnosis of IE.

Finally, when at least one of the two molecular methods was positive, the sensitivity and specificity of 16S rDNA amplification was 72.7% and 94.7%, respectively.

Discussion

The aim of this study was to evaluate a new set of primers (341F/785R) showing an excellent bacterial coverage in the in silico evaluation performed by Klindworth et al. [START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencingbased diversity studies[END_REF]. However, the sensitivity of this primer pair was lower than expected in the work presented here when working directly from heart valve samples. One possible explanation is that the 341F/785R primers are impacted by the background human DNA present in heart valves, reducing PCR efficiency. Indeed, the 341F DNA sequence contains a degeneracy of 8 (1 N and 1 W), and 785R a degeneracy of 9 (1 H and 1 V). A higher degeneracy increases bacterial coverage, but also increases non-specific amplification. The performance of PCR assays could also be affected by the length of the PCR products, but this length was similar for 91E/13BS and 341F/785R (478-bp and 465-bp, respectively). The 341F/785R primer pair did not seem particularly adapted in this study with samples containing a high human DNA background.

The classical set (91E/13BS) seems to be more sensitive in the case of IE, although the new set (341F/785R) allowed us to identify a microbiological agent in two cases not detected by the old primer set (patients 38 and 58).

The work presented here confirms the results of previous studies using conventional 16S rDNA PCR on large cohorts of valve samples (51 to 245) showing a sensitivity and specificity ranging from 41.2 to 67% and from 91 to 100%, respectively, whereas the sensitivity and specificity of valve cultures ranged from 7.8 to 23% and 93.3 to 100%, respectively [9, 11-13, 15, 20]. Recently, real-time PCR applied to 177 and 357 valve samples [START_REF] Marin | Molecular diagnosis of infective endocarditis by real-time broad-range polymerase chain reaction (PCR) and sequencing directly from heart valve tissue[END_REF][START_REF] Vollmer | 23S rDNA real-time polymerase chain reaction of heart valves: a decisive tool in the diagnosis of infective endocarditis[END_REF] showed sensitivities ranging from 80.6-96% and specificities ranging from 95.3-100%, respectively, whereas the sensitivities and specificities of valve culture were 13-33.4% and 96.6-100%, respectively. The 16S rDNA PCR successfully detected bacteria even after effective antimicrobial therapy.

The detection of bacterial DNA in cardiac valves does not mean that an active valve infection is evolutive in so far as 16S rDNA PCR cannot differentiate viable from non-viable microorganisms. On the contrary, the results may remain negative if the inoculum is too low, and do not exclude the IE diagnosis based on Duke's criteria (2 cases in this study). In the case of IE, even when BCs were positive, molecular techniques are advised to consolidate the existing diagnosis [START_REF] Gould | Guidelines for the diagnosis and antibiotic treatment of endocarditis in adults: a report of the Working Party of the British Society for Antimicrobial Chemotherapy[END_REF]. Various authors have proposed the addition of the molecular techniques results into the Duke classification as a major criterion [START_REF] Millar | Molecular diagnosis of infective endocarditis--a new Duke's criterion[END_REF]. Indeed, as previously described by Marin [START_REF] Marin | Molecular diagnosis of infective endocarditis by real-time broad-range polymerase chain reaction (PCR) and sequencing directly from heart valve tissue[END_REF] and by Vondracek [START_REF] Vondracek | 16S rDNA sequencing of valve tissue improves microbiological diagnosis in surgically treated patients with infective endocarditis[END_REF], molecular techniques identify a causal agent in 4/53 (8.5%) and 3/35 (7.5%) of culture-negative IE cases, respectively. In the prospective study described here, a causative agent was identified only by 16S rDNA PCR in 5/55 (9%) of culture-negative IE cases.

Here, the new primer pair 341F/785R was less sensitive for 16S rDNA amplification on heart valve samples than the older 91E/13BS primer pair. The selection of appropriate primer pairs for 16S rDNA amplification on clinical samples seems critical, and this study underscores a lack of standardization for this molecular method. Conventional microbiological culture (BC and valve culture) remains the gold standard to document the etiology of IE. 16S rDNA amplification may be proposed in second-line when the culture of the valve remains sterile.

Table II :

 II Value of conventional and molecular (16S rDNA) techniques for the diagnosis of IE

		Total number Gram stain	Heart valve	Blood culture	16S rDNA PCR	16S rDNA PCR	16S rDNA PCR
				culture		91E	341F	91E or 341F
	DEFINITE IE	55	14 (25.5%)	16 (29.1%) 43 (80.0%)	38 (69.1%)	28 (50.9%)	40 (72.7%)
	Culture Positive	48 (87.3%)	13 (27.1%)	16 (33.3%) 43 (90.0%)	33 (68.8%)	25 (52.1%)	35 (72.9%)
	Culture Negative	7 (12.7%)	1 (14.3%)	0	0	5 (71.4%)	3 (42.9%)	5 (71.4%)
	ABSENCE of IE	19	0	0	2 (10.5%)	1 (5.3%)	0	1 (5.3%)