Szabolcs Lengyel
email: lengyel.szabolcs@okologia.mta.hu

Beatrix Kosztyi

Dirk S Schmeller
email: dirk.schmeller@ufz.de

Pierre-Yves Henry
email: henry@mnhn.fr

Mladen Kotarac
email: mladen@ckff.si

Yu-Pin Lin
email: yplin@ntu.edu.tw

Klaus Henle
email: klaus.henle@ufz.de

Evaluating and benchmarking biodiversity monitoring: metadata-based indicators for sampling design, sampling effort and data analysis

Keywords: Benchmarking biodiversity monitoring 2020 target, assessment, biodiversity observation network, biodiversity strategy, citizen science, conservation funding, environmental policy, evidence-based conservation, statistical power, surveillance

1. The biodiversity crisis has led to a surge of interest in the theory and practice of biodiversity monitoring. Although guidelines for monitoring have been published since the 1920s, we know little on current practices in existing monitoring schemes.

2. Based on metadata on 646 species and habitat monitoring schemes in 35 European countries, we developed indicators for sampling design, sampling effort, and data analysis to evaluate monitoring practices. We also evaluated how socio-economic factors such as starting year, funding source, motivation and geographic scope of monitoring affect these indicators.

3. Sampling design scores varied by funding source and motivation in species monitoring and decreased with time in habitat monitoring. Sampling effort decreased with time in both species and habitat monitoring and varied by funding source and motivation in species monitoring.

4. The frequency of using hypothesis-testing statistics was lower in species monitoring than in habitat monitoring and it varied with geographic scope in both types of monitoring. The perception of the minimum annual change detectable by schemes matched spatial sampling effort in species monitoring but was rarely estimated in habitat monitoring.

Policy implications:

Our study identifies promising developments but also options for improvement in sampling design and effort, and data analysis in biodiversity monitoring. Our indicators provide benchmarks to aid the identification of the strengths and weaknesses of individual monitoring schemes relative to the average of other schemes and to improve current practices, formulate best practices, standardize performance and integrate monitoring results.

INTRODUCTION

The global decline of biodiversity and ecosystem services led to the adoption of several ambitious goals by the international community for 2010 and then again for 2020. Monitoring of biodiversity is instrumental in evaluating whether these goals are met. Although literature on how monitoring systems should be organized has been published since at least the mid-1920s [START_REF] Cairns | A history of biological monitoring using benthic macroinvertebrates[END_REF], interest in the theory and practice of biodiversity monitoring has surged since 1990 [START_REF] Noss | Indicators for monitoring biodiversity: a hierarchical approach[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF] and culminated in comprehensive, theory-based recommendations for monitoring [START_REF] Balmford | Measuring the changing state of nature[END_REF][START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Mace | A User's Guide to Biodiversity Indicators[END_REF][START_REF] Pocock | Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities[END_REF].

Despite this growing knowledge, significant concerns regarding current practices remain [START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Walpole | Tracking progress toward the 2010 biodiversity target and beyond[END_REF]. A consistently voiced concern is that monitoring is not adequately founded in theory because many schemes are not designed to test hypotheses about biodiversity change even though their primary objective, almost exclusively, is to detect changes in biodiversity [START_REF] References Balmford | The 2010 challenge: data availability, information needs and extraterrestrial insights[END_REF][START_REF] Nichols | Monitoring for conservation[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF]. Although not all monitoring schemes require hypothesis-testing given the variety of their objectives [START_REF] Pocock | Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities[END_REF], there is also a general concern over the ability of monitoring schemes to adequately detect changes in biodiversity due to biased sampling designs, inadequate sampling effort, or low statistical power to detect changes [START_REF] Di Stefano | Power analysis and sustainable forest management[END_REF][START_REF] Mihoub | Setting temporal baselines for biodiversity: the limits of available monitoring data for capturing the full impact of anthropogenic pressures[END_REF]. [START_REF] Legg | Why most conservation monitoring is, but need not be, a waste of time[END_REF] and [START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF] warned that these shortcomings may lead to poor quality of monitoring, and, ultimately, to a waste of valuable conservation resources.

There is little information, however, on the prevalence of these potential methodological weaknesses in current practices of biodiversity monitoring. Descriptions of current practices are available for monitoring schemes in North America [START_REF] Marsh | Current trends in plant and animal population monitoring[END_REF], and for European schemes of habitat monitoring (Lengyel et al., 2008a) and bird monitoring [START_REF] Schmeller | Bird-monitoring in Europe -a first overview of practices, motivations and aims[END_REF], however, these descriptions do not evaluate strengths or weaknesses in monitoring. Monitoring schemes are rarely known well enough for a comprehensive evaluation of current practices (Henle et al., 2010a;[START_REF] Schmeller | Advantages of volunteer-based biodiversity monitoring in Europe[END_REF], partly because monitoring schemes are designed for many different objectives at different spatial and temporal scales [START_REF] Geijzendorffer | How much would it cost to monitor farmland biodiversity in Europe?[END_REF][START_REF] Jarzyna | Detecting the multiple facets of biodiversity[END_REF][START_REF] Pocock | Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities[END_REF].

Therefore, the performance of biodiversity monitoring in terms of the criteria regarded by the critiques as insufficiently considered in monitoring has not yet been assessed. Consequently, little is known about whether and how performance varies among programs by spatial and temporal scales or socio-economic drivers. Moreover, it is rarely known whether and how programs evaluate their performance, either by expert judgement on their ability to detect trends or by estimating their statistical power to detect changes [START_REF] Geijzendorffer | How much would it cost to monitor farmland biodiversity in Europe?[END_REF][START_REF] Nielsen | Capacity of large-scale, long-term biodiversity monitoring programmes to detect trends in species prevalence[END_REF]. Hence, there is a need to provide monitoring coordinators with standard indicators of performance so that they can evaluate their programs and revise their practices to address potential weaknesses. A clear understanding of performance in existing monitoring schemes also provides crucial information to the institutions running and funding monitoring schemes as well as to policy-makers using information from biodiversity monitoring.

Here we present an overview of current practices in biodiversity monitoring in Europe by focusing on properties that have been frequently mentioned in critiques of biodiversity monitoring. We used metadata on monitoring schemes to develop indicators for sampling design, sampling effort and type of statistical analysis. While monitoring schemes have been established for many different purposes, these three properties are regarded as generally relevant in determining the scientific quality of the information derived from biodiversity monitoring [START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Nichols | Monitoring for conservation[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF]. Sampling design, an indicator of how well the spatial and temporal distribution of data collection is founded in sampling theory [START_REF] Balmford | Measuring the changing state of nature[END_REF], is essential for accuracy, i.e., closeness of measured trends and real trends in biodiversity. Sampling effort, the number of measurements made, is central to precision, i.e., the ability to measure the same value under identical conditions. Finally, to translate collected data into information relevant for further use, such as conservation or policy, appropriate statistical analysis of data is required to detect changes or trends with a given level of uncertainty, and confidence in the estimates should be based on the ability of the scheme to detect changes [START_REF] Legg | Why most conservation monitoring is, but need not be, a waste of time[END_REF].

Although these three indicators are generally relevant in any type of monitoring, monitoring schemes differ in their objectives and many different types of monitoring schemes exist [START_REF] Pocock | Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities[END_REF]. For example, schemes in Europe have been started as early as the 1970s, are motivated by different reasons, funded by different sources, and their geographic scope ranges from local to continental (Lengyel et al., 2008a;[START_REF] Schmeller | Bird-monitoring in Europe -a first overview of practices, motivations and aims[END_REF]. To account for these socio-economic differences and to increase the useability of our indicators in different monitoring schemes, we evaluated the variation in indicators as a function of starting year, funding source, motivation, and geographic scope. Finally, we show how our indicators can be used by coordinators as benchmarks to assess their schemes relative to the average practice and to identify options for improvement of their monitoring schemes. We present different benchmark values for the three indicators to be meaningful for schemes monitoring different species groups and habitat types.

METHODS

Definition and dataset

We used [START_REF] Hellawell | Development of a rationale for monitoring[END_REF] definition of "biodiversity monitoring" as the repeated recording of the qualitative and/or quantitative properties of species, habitats, habitat types or ecosystems of interest to detect or measure deviations from a predetermined standard, target state or previous status in biodiversity. We collected metadata on biodiversity monitoring schemes in Europe in an online survey (Henle et al., 2010a). The online questionnaire contained 8 general questions and 33 and 35 specific questions on species and habitat monitoring schemes, respectively (Table S1,S2). We sent more than 1600 letters with requests to fill out the questionnaire to coordinators of monitoring schemes, government officials, national park staff, researchers and other stakeholders at institutions involved in biodiversity monitoring. The information entered was quality-checked and organized into a meta-database (http://eumon.ckff.si/monitoring).

The survey response rate was 40% (646 schemes for 1600 letters), which was comparable to the only other questionnaire-based study of biodiversity monitoring (48%) [START_REF] Marsh | Current trends in plant and animal population monitoring[END_REF]. Response rate varied among countries and we evaluated this bias based on the logic of [START_REF] Schmeller | Advantages of volunteer-based biodiversity monitoring in Europe[END_REF] (Supporting Information S1.1). Our metadatabase is not, and cannot be, exhaustive to involve all monitoring schemes because the universe of all schemes is not known, however, it provides a cross-section of geographic scope (Supporting Information S1.1). The final dataset contained metadata on 470 species schemes and 176 habitat schemes, or a total of 646 schemes from 35 countries in Europe. Assessment of country bias showed no substantial differences from the usual publication bias for 25 (or 71%) of the 35 countries, overrepresentation for three countries and underrepresentation for seven countries (Fig. S1).

Indicator development

To compute an indicator of sampling design, we scored seven design variables in both species and habitat monitoring schemes (Table 1). Scores were chosen to be higher for sampling designs that were better founded in sampling theory and/or that obtained more or better, e.g. quantitative rather than qualitative, information on species and habitats (further details: Supporting Information S1.3). Scores were determined for each scheme as a consensus among DSS, KH and SL. As a final output, we calculated a 'sampling design score' (SDS) indicator as the sum of the seven scores (range: 0-13 in species schemes, 0-10 in habitat schemes).

For sampling effort, we derived both a temporal and a spatial indicator. We used the following formula for the "temporal sampling effort" indicator:

SE temp = log(F by (T 2 -1)(T*F wy -2)), ( eqn 1)
where F by is the between-year frequency of sampling (value of 1 indicating monitoring in every year, 0.5 for monitoring every other year, etc.); T is the duration of monitoring in years;

and F wy is the number of sampling occasions (site visits) within a year. A derivation of equation 1 is given in Supporting Information S1.4.

For the "spatial sampling effort" indicator (SE spatial ), we used information on the number of sampling sites and the total area monitored. Assuming that more sampling sites in equal-sized areas indicate higher sampling effort, we calculated the residuals from an ordinary leastsquares regression of the number of sites (log-transformed response) over the total area monitored (log-transformed predictor). Positive values (above the fitted line) indicate higherthan-average effort, whereas negative values (below the fitted line) indicate lower-thanaverage effort for equal-sized areas.

Each of these three indicators (SDS, SE temp , SE spatial ) is negatively proportional to at least one source of variation (temporal, among-site, or within-site) that increases the variance of the trend estimate from monitoring. Hence the higher the values of the indicators, the better the sampling design, the higher the sampling effort, and the higher the precision of the trend estimate. The three indicators cannot be readily integrated but have the advantage that coordinators of monitoring schemes can easily calculate them based on Eq. ( 1) or the regression equations and can use them as benchmarks (see Results).

For the "type of data analysis" indicator, we used information on the analytical method as

given by the coordinators. The single-choice options were (i) descriptive statistics or graphics, (ii) simple linear regression, (iii) advanced statistics, e.g. general linear models etc, (iv) other analyses, (v) data analyzed by somebody else, or (vi) data not analyzed. We considered options (i) and (vi) as evidence for the lack of inferential statistics and hypothesistesting and considered all other options as signals for hypothesis-testing. Although the option 'data analyzed by someone else' could also involve descriptive statistics or graphics, i.e., no hypothesis-testing, this option was chosen for only 26 species schemes (<6% of 439 responses) and four habitat schemes (<3% of 154 responses), and pooling these into either group did not influence our results.

Finally, to evaluate the coordinators' expert judgement of the ability of their schemes to detect changes, we asked coordinators to estimate the precision of their scheme as the minimum annual change per year in the monitored property (e.g. population size, habitat area) that is detectable by their scheme (1%, 5%, 10%, 20%, or more). We then correlated these "precision estimates" with our temporal and spatial indicators of sampling effort to test whether coordinators correctly estimated the sampling effort of their schemes. We arbitrarily took 30% for responses of 'more than 20%'. We found that using different percentages (40%, 50% etc.) did not qualitatively affect our conclusions.

Socio-economic effects

We analyzed the variation in each indicator caused by four socio-economic factors: (i) starting year, (ii) main funding source (European Union [EU], national, regional, scientific grant, local), (iii) motivation (EU directive, other international law, national law, management/restoration, scientific interest, other), and (iv) geographic scope (pan-European, international, national, regional, local). These factors were chosen because they are fundamentally important in biodiversity monitoring and because knowledge of how these factors impact the indicators (e.g. "sampling designs are more advanced in schemes funded by certain types of donors") will influence how monitoring coordinators and institutions interpret and use the indicators.

To detect changes in certain time periods, we classified schemes by starting year in four time periods of European biodiversity policy: (i) period 1: years until the adoption of the Birds Directive in 1979, (ii) period 2: from 1980 until the adoption of the Habitats Directive in 1992, (iii) period 3: 1993 until 1999, and (iv) period 4: since 2000 or the preparations of the 2010 biodiversity targets. For funding source, motivation, and geographic scope, we used the single-choice responses as given by the coordinators.

Data processing

The three indicators had heterogeneous variances and/or non-normal distributions, and the scales of the predictor and the response variables could differ so that comparisons based on parametric test statistics (e.g. means) would have an unclear meaning. Therefore, we present results using boxplots to illustrate differences and use Kruskal-Wallis tests to compare medians. Sample sizes differ because not all information was available for all schemes.

3. RESULTS

Sampling design and effort

In species monitoring, SDS was similar through time and geographic scope (Fig. 1; Kruskal-Wallis test, n.s.) but varied by funding source (H = 15.156, df = 5, P = 0.010) and motivation (H = 17.029, df = 5, P = 0.004). SDS was higher in schemes funded by scientific grants than in other schemes, and lower in schemes motivated by national laws than in other schemes (Fig. 1). SE temp decreased with time (H = 261.088, df = 3, P < 0.0001) and varied by funding source and motivation (Fig. 2). SE temp was higher in schemes funded by private sources than in other schemes (H = 32.173, df = 5, P < 0.0001) and was lower in schemes motivated by EU directives than in other schemes (H = 82.625, df = 5, P < 0.0001). SE spatial decreased with time (H = 12.817, df = 3, P = 0.005) and was lower in schemes motivated by international laws and higher in schemes motivated by 'other reasons' than in other schemes (Fig. 3, H = 11.554, df = 5, P = 0.041). SE spatial did not vary significantly by funding source and geographic scope (Fig. 3).

In habitat monitoring, SDS decreased with time (H = 7.974, df = 3, P = 0.047), but did not differ by funding source, motivation, or geographic scope (Fig. 4). SE temp also decreased with time (H = 51.324, df = 3, P < 0.0001), but did not vary by funding source, motivation, or geographic scope (Fig. 5). Finally, SE spatial did not vary by any of the four predictors (Fig. 6).

Data analysis

The proportion of schemes using hypothesis-testing statistics was significantly lower (48%) in species schemes (n = 439) than in habitat schemes (69%; n = 157; χ 2 = 20.838, df = 1, P < 0.0001). In species monitoring, this proportion did not differ by starting period (range: 40-52%) or funding source (36-53%; χ 2 -test, n.s.). However, hypothesis-testing statistics were more frequent in schemes motivated by scientific interest (56%, n = 172) than in schemes motivated by EU directives (28%, n = 67), other reasons (31%, n = 26), or international law (33%, n = 15), national laws (43%, n = 107), management/restoration (43%, n = 82; χ 2 = 18.267, df = 5, P = 0.003). Hypothesis-testing statistics were also more frequent among schemes of European or international scope (63% each, n = 8 and 16, respectively) than in local schemes (32%, n = 114) (national: 49%, n = 203; regional: 45%, n = 128; χ 2 = 16.007, df = 4, P = 0.003).

In habitat monitoring, hypothesis-testing statistics were more frequent in schemes started in period 2 and 3 (71% of n = 17 in period 2 and 74% of n = 77 in period 3) than in schemes started in period 1 (50%, n = 8) or period 4 (49%, n = 72) (χ 2 = 12.967, df = 3, P = 0.005). In addition, these statistics were more frequent in schemes whose geographic scope was national (60%, n = 35) and local (72%, n = 87) rather than regional (44%, n = 48; European and international schemes excluded due to low sample size; χ 2 = 11.855, df = 2, P = 0.003). The frequency of hypothesis-testing statistics did not differ by funding source (range 40-67%) or motivation (range 53-86%; χ 2 -test, n.s.).

Precision estimates vs. sampling effort

Coordinators estimated the minimum annual change detectable by their schemes in 74% of species schemes (n = 470) and in only 36% of habitat schemes (n = 176). In species schemes, SE spatial correlated negatively with precision estimates, as expected (Spearman rho = -0.128, n = 309, P = 0.024), whereas SE temp was not related to precision estimates. In habitat schemes, there were no correlations between SE temp or SE spatial and precision estimates.

Benchmarking: how do single schemes perform?

Our indicators provide benchmarks against which single schemes can be compared.

Coordinators can compute these indicators for their own schemes in three steps. First, the SDS indicator is calculated by selecting the response options of their own scheme for each of the seven variables in Table 1, reading the corresponding score value, and summing the seven score values, which can then be compared to the reference mean SDS value given in Table 2 for major species groups and habitat types. Second, the SE temp indicator is calculated by substituting the values of a given scheme into Equation 1, which then can be compared to the reference values given in Table 2. Finally, SE spatial is obtained by calculating the difference between the number of sampling sites in a given scheme and the mean number of sites predicted for schemes that monitor similar areas. The mean predicted number is determined by regression equations based on intercepts and regression coefficients in Table 3. For example, the mean number of sampling sites predicted for schemes monitoring higher plants in an area of 100 km 2 is given as log(Y) = 0.47 + 0.34*log(100) = 1.15 (where 0.47 and 0.34 are from Table 3), resulting in Y ≈ 14. If the given scheme monitors higher plants at 20 sites in an area of 100 km 2 , the value of SE spatial (scheme value -predicted value) is 6, indicating a higher-than-average effort than in other schemes. The regression equation for SE spatial in habitat schemes is log(Y) = 0.51 + 0.36*log(X), where X is the area monitored in km 2 and Y is the predicted number of sites. Separate regressions for habitat types were not meaningful due to low sample size in several habitat types (Table 2).

DISCUSSION

General patterns in monitoring

This study is the first to provide a comprehensive evaluation of sampling design, sampling effort and data analysis in biodiversity monitoring based on indicators calculated from metadata on existing schemes. Despite limitations in the data (see Supporting Information), our evaluation is based on the most comprehensive dataset currently available on existing schemes. A full validation of the indicators is not yet possible due to the absence of quantitative estimates of statistical power and accuracy derived from monitoring data in existing schemes, which could provide an independent reference. For a correct interpretation, we note that our metadatabase showed overrepresentation for 9% of the countries and underrepresentation for 20% of the countries relative to the usual publication bias, therefore, not all our results apply equally to all 35 countries represented in the metadatabase.

Our results provide evidence that biodiversity monitoring varies with the socio-economic background. We found decreasing trends in SE temp in species schemes and in SDS and SE temp in habitat schemes over time. Hypothesis-testing statistics were also less frequently used in more recent species schemes than in earlier (1980s-1990s) ones despite several calls for Third, precision estimates were much less frequently specified in habitat schemes (36%) than in species schemes (74%). On one hand, this is plausible as it is probably easier to specify precision in schemes that monitor one or a few species than in schemes that monitor entire habitat types, i.e., species communities. On the other hand, many habitat monitoring schemes use standardized methods to document spatial variation, e.g. field mapping or remote sensing, which should facilitate the evaluation of precision.

Finally, hypothesis-testing statistics were used in less than half of the species schemes and more than two-thirds of the habitat schemes. Thus, our results support previous concerns over the lack of a hypothesis-testing framework in biodiversity monitoring [START_REF] Legg | Why most conservation monitoring is, but need not be, a waste of time[END_REF][START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF]. The infrequent use of hypothesistesting statistics and the large number of schemes for which no precision estimate was given by the coordinators also suggest that the ability of schemes to detect changes in biodiversity (statistical power) is rarely considered in monitoring design [START_REF] Di Stefano | Power analysis and sustainable forest management[END_REF][START_REF] Marsh | Current trends in plant and animal population monitoring[END_REF].

Recommendations

The variation in indicators can potentially have serious consequences regarding the ability of monitoring schemes to detect trends or the reliability of the trend estimates detected, which can thus easily provide misleading information on changes in biodiversity. Our results provide insight into potential areas of improvement that can help to avoid such potential consequences. Generally, sampling design can be improved by applying levels associated with higher scientific quality to one or more of the variables listed in Table 1. An ideal habitat monitoring scheme should apply both remote sensing and field mapping to document spatial changes because the two approaches work best at different scales (Lengyel et al., 2008b). The introduction of an experimental approach in monitoring, with adequate controls, was proposed as the greatest potential for improvement as it provides an opportunity to establish causal relations between trends and possible drivers of the trends [START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF]. Because experiments may have limited external validity due to limitations in the scale at which experiments can be performed, they should be complemented by observational studies addressing the same issues at the relevant larger scale [START_REF] Lepetz | Biodiversity monitoring: some proposals to adequately study species' responses to climate change[END_REF] or by studies using natural experiments that are not controlled for scientific or monitoring reasons [START_REF] Henle | Lessons from Europe[END_REF].

In principle, sampling effort can be improved by increasing either the number of sites, site visits, samples, or the frequency of sampling. In contrast to sampling design, where there is often a trade-off between options, the spatial and temporal intensity of sampling can be increased simultaneously and independently. It is fundamental to have accurate (unbiased) and precise (low-variance) estimates for the trend of the habitats of interest by ensuring adequate spatial and temporal replication [START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF]. Estimating the adequate number of replicates should be based on a quantitative evaluation of the ability of monitoring schemes to detect trends in explicit analyses of statistical power [START_REF] Nielsen | Capacity of large-scale, long-term biodiversity monitoring programmes to detect trends in species prevalence[END_REF][START_REF] Taylor | The uses of statistical power in conservation biology -the Vaquita and the Northern Spotted Owl[END_REF].

To address the alarmingly rare use of hypothesis-testing statistics, we recommend that responsible international institutions and national agencies as well as funding agencies establish mechanisms, including procedural requirements and training opportunities, to facilitate a better use of the data collected. Because several schemes used other, unspecified statistics, it needs further study to determine the type of these analyses and to evaluate whether such unspecified statistics are appropriate for integration across monitoring schemes [START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF][START_REF] Mace | A User's Guide to Biodiversity Indicators[END_REF]. Using advanced statistics to analyze data from otherwise well-designed sampling is a straightforward way to improve the quality of information derived from monitoring data [START_REF] References Balmford | The 2010 challenge: data availability, information needs and extraterrestrial insights[END_REF][START_REF] Di Stefano | Power analysis and sustainable forest management[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF].

Benchmarking: practical help for implementing recommendations

Although scientifically desirable, it may not be realistic to expect that monitoring schemes improve or change everything to have state-of-the-art practices given the many goals they pursue and the many constraints under which they operate [START_REF] Bell | What counts? Volunteers and their organisations in the recording and monitoring of biodiversity[END_REF][START_REF] Marsh | Current trends in plant and animal population monitoring[END_REF][START_REF] Schmeller | Advantages of volunteer-based biodiversity monitoring in Europe[END_REF]. It is more realistic to provide the monitoring community with guidelines on how to improve schemes relative to the average practice [START_REF] Henle | Priorities for biodiversity monitoring in Europe: A review of supranational policies and a novel scheme for integrative prioritization[END_REF]. Our study provides a basis for such practical guidance in two ways. First, by revealing the impact of socio-economic factors on biodiversity monitoring, our study provides knowledge on the impacts of starting time, funding source, motivation and geographic scope on three general properties of biodiversity monitoring, which should ideally be explicitly considered in decisions made by monitoring coordinators and institutions.

Second, our study provides three indicators and presents different indicator values for use in monitoring schemes that differ in their monitored object (Tables 2 and3). Coordinators can thus identify the strengths and weaknesses in sampling design, effort and data analysis in their schemes relative to the average of existing schemes in a benchmarking approach. It will in turn enable coordinators to design and implement changes that may improve the ability of their schemes to collect more broadly useable data. By modifying the values of the indicators, coordinators can further assess which of the alternative options available to them would more efficiently increase the performance of their scheme.

Although the benchmarking proposed here does not provide a quantitative assessment of statistical power, its relative ease of use compared to a rigorous assessment of statistical power can make it widely applicable in many different monitoring schemes. We note that our benchmarking method is relative, i.e., the outcome for a single scheme will depend on the values of the other schemes. We aimed to minimize this variation by presenting different benchmark values for schemes monitoring different groups of species or types of habitat (Table 2 and3 Ongoing efforts, both to build monitoring schemes from scratch and to improve existing schemes, such as regional and global Biodiversity Observation Networks [START_REF] Wetzel | The roles and contributions of Biodiversity Obervation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study[END_REF], can benefit from the insight gained from comparing their plans with characteristics of existing schemes. Furthermore, the evaluation and benchmarks may be used in the integration of monitoring results in large-scale assessments of biodiversity and ecosystem services, e.g.

under the Convention on Biological Diversity, assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services or in citizen-science programs.

CONCLUSIONS

We acknowledge that a direct and full application of scientifically credible criteria to biodiversity monitoring practice may be overzealous and inadequate and that other Abbreviations: (B): EU -European Union, nat -national, reg -regional, sci -scientific grant, priv -private source, oth -other; (C) dir -directive, intl -international law, nlaw -national law, sci -scientific interest, mgmt -management/restoration, oth -other reason; (D) EU -European, intl -international, nat -national, reg -regional, loc -local.

  ). In addition, cooordinators and institutions should also look at how the four socio-economic factors modify the values of the indicators to develop a joint interpretation of the indicator values relative to the average practice and of the indicator values in schemes with similar socio-economic background. These two types of information will help coordinators and institutions to fine-tune the benchmarking of their monitoring schemes, to identify areas of strengths and weaknesses relative to the average practice and to address options for improving their own practice.

Figure 1 .

 1 Figure 1. Sampling design score (SDS) in species monitoring schemes vs. starting period

Figure 2 .Figure 3 .Fig. 1 )Figure 4 .Figure 5 .Fig. 1 )Figure 6 .Fig

 2314516 Figure 2. Temporal sampling effort (SEtemp) in species monitoring schemes.

  Fig. 2

Fig. 4

 4 Fig. 4

Fig. 5

 5 Fig. 5

TABLES 562 Table 1 .

 5621 Scores allocated to different levels of variables describing the sampling design used 563 in species and habitat monitoring schemes in Europe. Please see Supporting Information for

	564
	justification of score values.

Table 2 .

 2 Means ± standard deviations (S.D.) of sampling design score (SDS) and the 567 temporal sampling effort index (SE temp ) in species and habitat monitoring schemes; N:

	568
	569	number of schemes with metadata.
				SDS			SE temp
		Monitored object	Mean	S.D.	N	Mean	S.D.	N
		Taxon group in species monitoring
		Lower plants	4.9	1.63	22	3.3	0.77	20
		Higher (vascular) plants	4.8	2.14	41	3.4	1.07	39
		Arthropods (mainly insects)	5.1	2.00	34	3.6	1.05	27
		Butterflies	5.0	1.97	38	4.1	1.26	34
		Fish and macroinvertebrates	5.3	1.93	27	3.2	0.99	23
		Amphibians and reptiles	5.2	1.83	43	4.0	0.91	40
		Birds in general	5.2	1.74	59	4.2	1.15	54
		Birds of prey	5.8	2.19	21	4.4	1.00	20
		Waterbirds	4.8	1.66	53	4.5	1.03	52
		Songbirds	5.4	1.82	27	4.3	0.78	27
		Bats	4.1	2.07	23	3.3	0.77	22
		Small mammals	4.6	1.91	28	3.7	0.93	27
		Large mammals	4.5	1.69	40	3.7	1.03	34
		Multiple taxon groups	5.7	1.77	14	3.9	0.79	10
		All taxon groups combined	5.0	1.89	470	3.9	1.08	429
		EUNIS category in habitat monitoring
		A marine only	5.3	1.92	12	3.4	0.16	3
		AB marine and coastal	5.6	1.75	11	3.7	1.31	2
		B coastal only	6.5	2.83	16	3.0	0.92	10
		C wetlands	4.2	2.09	11	3.7	1.20	4
		D heaths and fens	5.7	3.01	13	3.3	0.64	10
		E grasslands	5.5	2.37	16	3.2	0.62	15
		F scrubs	6.8	2.48	6	4.0	0.37	3
		G forests	5.2	1.66	41	3.4	1.01	25
		H caves	6.5	0.71	2	5.4	-	1
		I arable land	5.5	0.71	2	3.7	0.45	2
		X habitat complexes	6.0	2.14	8	3.2	0.80	7
		All habitat types in an area	5.0	2.35	38	3.3	1.37	22
		All EUNIS habitat categories combined	5.4	2.23	176	3.3	0.98	104
	570
	571
	572

Table 3 .

 3 Parameters estimated from an ordinary least-squares regression of the number of sampling sites over the area monitored in species monitoring schemes targeting major taxonomic groups

	Taxon group	Intercept Slope S.E. slope	R 2	t	p
	Lower plants	1.40	0.15	0.149	0.056 0.977 0.343
	Higher (vascular) plants	0.47	0.34	0.083	0.336 4.148 0.000
	Arthropods (mainly insects)	0.46	0.30	0.070	0.397 4.292 0.000
	Butterflies	0.52	0.35	0.077	0.411 4.579 0.000
	Fish and macroinvertebrates	0.89	0.15	0.099	0.108 1.515 0.146
	Amphibians and reptiles	0.82	0.22	0.105	0.119 2.139 0.040
	Birds in general	1.42	0.13	0.090	0.050 1.465 0.151
	Birds of prey	0.84	0.12	0.177	0.024 0.669 0.512
	Waterbirds	1.55	0.04	0.101	0.005 0.420 0.677
	Songbirds	0.45	0.20	0.079	0.216 2.516 0.019
	Small mammals	0.33	0.25	0.070	0.351 3.601 0.001
	Bats	0.88	0.15	0.112	0.091 1.339 0.197
	Large mammals	0.21	0.34	0.088	0.343 3.895 0.001
	Multiple groups	0.49	0.59	0.137	0.696 4.284 0.003

ACKNOWLEDGEMENTS

This study was funded by the "EuMon" project (contract 6463, http://eumon.ckff.si), the "SCALES" project (contract 226852, http://www.scales-project.net) (Henle et al., 2010b), and by two grants from the National Research, Development and Innovation Office of Hungary (K106133, GINOP 2.3.3-15-2016-00019). We thank our EuMon colleagues and monitoring coordinators for their help in data collection, and two reviewers for their comments on an earlier version of the manuscript.

hypothesis-testing [START_REF] References Balmford | The 2010 challenge: data availability, information needs and extraterrestrial insights[END_REF][START_REF] Lindenmayer | Adaptive monitoring: a new paradigm for long-term research and monitoring[END_REF][START_REF] Nichols | Monitoring for conservation[END_REF][START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF]. Similar results were reported by [START_REF] Marsh | Current trends in plant and animal population monitoring[END_REF], who found a recent increase in the percentage of North American species schemes that did not decide on statistical methods.

We also found higher SDS in schemes funded by scientific grants and higher SE temp in schemes funded by private sources than in other schemes. The influence of motivation in species schemes was less expected, with lower SDS in schemes motivated by national laws, lower SE temp in schemes motivated by EU directives, lower SE spatial in schemes motivated by international laws, and lower frequency of hypothesis-testing statistics in schemes motivated by EU directives and other international laws than in other schemes. Finally, the use of hypothesis-testing statistics increased with geographic scope in species monitoring, whereas it decreased from national to regional schemes in habitat monitoring. Each of the four socioeconomic variables was associated with substantial variation in at least one of the indicators, suggesting that biodiversity monitoring is influenced by socio-economic factors [START_REF] Bell | What counts? Volunteers and their organisations in the recording and monitoring of biodiversity[END_REF][START_REF] Schmeller | Advantages of volunteer-based biodiversity monitoring in Europe[END_REF][START_REF] Vandzinskaite | Cultural diversity issues in biodiversity monitoring -cases of Lithuania, Poland and Denmark[END_REF].

Promising developments

Our results draw attention to several promising developments in current biodiversity monitoring. First, SDS did not change substantially over time, indicating that despite the continuous growth in the number of schemes (e.g. Lengyel et al., 2008a), the quality of the sampling design used in schemes is not deteriorating. Second, we found less variation in indicators in habitat schemes than in species schemes. This is probably related to the fewer habitat schemes present in our sample. In addition, habitat monitoring is methodologically less heterogeneous, based mostly on field mapping and remote sensing (Lengyel et al., 2008a), than species monitoring, where different species groups are monitored with different methods even in single taxonomic groups, such as birds [START_REF] Schmeller | Bird-monitoring in Europe -a first overview of practices, motivations and aims[END_REF]. Finally, the precision estimates given by monitoring coordinators corresponded with spatial sampling effort in species monitoring schemes as expected (i.e., more sites relative to area = higher precision).

Reasons for concern

Our survey also confirmed several concerns. First, while the number of schemes increases as general interest in biodiversity conservation increases [START_REF] Henle | Priorities for biodiversity monitoring in Europe: A review of supranational policies and a novel scheme for integrative prioritization[END_REF], we found that sampling effort decreased over time, mainly because the number of temporal replicates per unit area decreased, both in species and in habitat schemes. This is especially alarming in species schemes where repeated observations over shorter time periods (i.e., within a season) are essential to estimate the probability of detecting individuals [START_REF] Schmeller | Towards a global terrestrial species monitoring program[END_REF].

Second, we identified lower-than-average values for several indicators in species monitoring: in national schemes (SDS), and in schemes motivated by EU directives (SE temp ) and other international laws (SE spatial ). Furthermore, we found that data are less frequently analyzed in species schemes motivated by EU directives and other international laws and in habitat schemes that are local or regional. These results support the view that the policies guiding monitoring and the institutions providing funding should develop standard criteria for initiating/funding different schemes [START_REF] Legg | Why most conservation monitoring is, but need not be, a waste of time[END_REF]. These criteria should include minimum requirements for sampling design and effort that ensure that the performance of the individual schemes moves towards the average of all existing schemes. approaches may be more appropriate. Our study, however, suggests that while there are many promising developments in biodiversity monitoring that do not deserve the critique sometimes voiced against monitoring, there is also a need to improve current practices in sampling design, sampling effort and data analysis. Such concerns have been voiced in several previous studies based mostly on anecdotal data or personal observations. Our study provides the first comprehensive evaluation of actual practices to back up these concerns and to show where these are little justified and offers a practical framework based on benchmarking to address several of these concerns.


Yang Liu

Zhongyi Mao

Alexandre Pradal

Pei-Qiang Huang

Julie Oble

Giovanni Poli

Palladium-Catalyzed [3 + 2]-C-C/N-C Bond-Forming Annulation

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Palladium-Catalyzed [3+2]-C-C/N-C Bond Forming Annulation

Yang Liu, † Zhongyi Mao, † Alexandre Pradal, † Pei-Qiang Huang, § Julie Oble, †,* and Giovanni Poli †,* † Sorbonne Universités, Faculté des Sciences et Ingénierie, CNRS, Institut Parisien de Chimie Moléculaire, IPCM, 4 place Jussieu, 75005 Paris, France. § Department of Chemistry and The Key Laboratory for Chemical Biology of Fujian Province, iChEM (Collaborative Innovation Center of Chemistry for Energy Materials), College of Chemistry and Chemical Engineering, Xiamen University, Fujian 361005, P. R. China.

Supporting Information Placeholder

ABSTRACT:

The synthesis of bi-and tri-cyclic structures incorporating pyrrolidone rings are disclosed, starting from resonancestabilized acetamides and cyclic ,β-unsaturated--oxy-carbonyl derivatives. This process involves an intermolecular Tsuji-Trost allylation / intramolecular nitrogen 1,4-addition sequence. Crucial for the success of this bis-nucleophile / bis-electrophile [3+2] annulation is its well-defined step chronology in combination with the total chemoselectivity of the former step. When the newly formed annulation product carries a properly located ortho-haloaryl moiety at the nitrogen substituent, a further intramolecular keto -arylation can join the cascade, thereby forming two new cycles and three new bonds in the same synthetic operation.

The efficient assembly of complex polycyclic structures in a single synthetic operation is a major endeavor for chemists, and domino reactions 1 represent one of the most interesting ways to attain this objective with high step economy. 2 In particular, annulation reactions, the most efficient methods for the generation of cyclic molecules, pioneered by O. Diels and K. Alder, 3 and Sir R. Robinson, 4 allow building up a cyclic structure through the concerted or stepwise creation of two new bonds from two separated components. 5 In the frame of our current studies on η 3 -allylpalladium chemistry and domino sequences, 6 we recently reported the synthesis of hexahydroindole derivatives 7 through Pd-catalyzed pseudodomino sequences 8 using β-amido-ester as bis-nucleophiles and cyclic bis-allylic substrates as bis-electrophiles (Scheme 1, eq 1). 9 Cyclic ,β-unsaturated--oxy-carbonyls represent another interesting family of bis-electrophiles, which can be engaged in synthetically interesting Pd-catalyzed cascades, as reported by A. Fürstner, J. E. Harvey, S. S. Ramasastry and R. Tong (Scheme 1, eq 2). 10,11 Despite these works, the knowledge of the behavior of these bis-electrophiles is still far from mature. Herein, we disclose a Pd-catalyzed cascade between resonance-stabilized acetamides and various cyclic ,β-unsaturated--oxy-carbonyls (Scheme 1, eq 3), which allows the achievement of a [3+2]-C-C/N-C bond forming annulation. 12 Importantly, the desired transformation is possible only if the intermolecular C-C bond formation at the electrophilic γ position (umpoled position with respect to the carbonyl function) precedes the intramolecular N-C bond formation at the electrophilic β position. Furthermore, the use of acetamides bearing a properly located ortho-haloaryl moiety at the nitrogen substituent sets the stage for an additional intramolecular keto -arylation at the end of the cascade, thereby allowing the selective formation of two new cycles and three new bonds in the same synthetic operation (Scheme 1, eq 4).

Scheme 1. Domino reactions with bis-electrophiles and bisnucleophiles

We started the investigation of this [3+2]-C-C/N-C bond forming annulation using the N-tosyl amido-ester 1a 13 and 2cyclohexenone 4-benzoate 2a 14 as bis-nucleophile and biselectrophile model substrates, respectively (Table 1). The first test was performed using the conditions previously developed with cyclic bis-allylic substrates (Scheme 1, eq 1) 7,9 [Pd(OAc) 2 (5 mol %), dppb (10 mol %), NaH (3 equiv) in CH 3 CN at 50 °C]. However, we obtained only a very low amount of the expected fused pyrrolidone 3a (18% yield), along with a significant amount of phenol, arising from 2a through the elimination of the benzoate anion under basic medium. To suppress the formation of this by-product, the optimization of the reaction was performed in the absence of base, at room temperature, in THF (0.1 M) and with a slight excess of biselectrophile 2a (1.3 equivalents of 2a relative to 1a; see Supporting Information for detailed optimization). Subsequent screening of the palladium sources, such as: Pd(PPh 3 ) 4 (entry 1), Pd(OAc) 2 (Table 1, entries 2-3), Pd 2 (dba) 3 (entries 4-5) or [Pd(η 3 -C 3 H 5 )Cl] 2 (entry 6) and the phosphine ligand (PPh 3 or dppf), allowed us to reach the optimal conditions [Pd(η 3 -C 3 H 5 )Cl] 2 (5 mol %), dppf (15 mol %) in THF at rt: protocol A], which led to the desired bicyclic pyrrolidone 3a in 90% yield (Table 1, entry 6). With the optimized conditions in hand, the scope and the limitations of this [3+2]-C-C/N-C bond forming annulation between the bis-electrophile 2a and a range of resonancestabilized N-sulfonyl acetamides 1a-f, modified at the sulfonyl or at the carbonyl substituents, were next investigated (Scheme 2). All the N-sulfonyl acetamides tested behaved satisfactorily (65-90% yield), including acetoacetamide 1e, which afforded the expected annulated product as a 65:35 keto/enol mixture, whereas the N-triflyl derivative 1d did not show reactivity. In particular, in the case of 1c and 1e, the reaction temperature had to be raised to 60 °C, due to their manifest lower reactivity with respect to the other bisnucleophiles. It is worth noting that the annulation works satisfactorily when starting from the benzylated bisnucleophile 1f, too.

Different N-protections on the resonance-stabilized acetamides were next evaluated. Surprisingly, application of the above-optimized protocol to the N-carbamate bisnucleophile 1g did not afford the expected annulation product. After several trials and in deep contrast with our previous outcomes, we found that this type of substrate requires the presence of a base. A re-optimization effort established the following new conditions as optimal [Pd(η 3 -C 3 H 5 )Cl] 2 (5 mol %), dppf (15 mol %), DBU (2.0 equiv) in THF (0.1 M) at rt: protocol B] (Scheme 3).

Scheme 2. Variations of the resonance-stabilized N-sulfonyl acetamide in the reaction with 2a Scheme 3. Variations of the N-protection on the resonancestabilized acetamide in the reaction with 2a

Under these conditions, the Boc derivative 1g afforded the expected product 3g in 50% yield. Good yields of annulated products were also obtained with N-allyl (1h), -benzyl (1i, 1l), -o-bromobenzyl (1j, 1k), -phenyl (1m), -o-bromophenyl (1n), -o-chlorophenyl (1o), -o-iodophenyl (1p) derivatives, carrying a methoxycarbonyl, a benzoyl or a phenylsulfonyl moiety as the resonance stabilizing group for the acetamide. In the case of N-allyl amido-ester 1h, the reaction must be performed at 0 °C for maximum yield. Interestingly, the o-bromo and ochloro (but not the o-iodo) -phenyl substituted amides 1n and 1o can react smoothly also in the absence of DBU, albeit in lower yields. This behavior is very likely due to the N-H acidity enhancement brought about by the highly electronegative bromine and chlorine atoms, which activate the nitrogen atom. 16 The above results suggest that the acidity of the two mobile hydrogen atoms of the bis-nucleophile have to fit in an appropriate pK a window to allow the annulations.

Encouraged by these results, we next turned our attention to explore the [3+2]-annulation involving five-and sevenmembered cyclic ,β-unsaturated--oxy-carbonyls (Scheme 4). Treatment of cyclopentenone 4-benzoate 2b 17 with 1a under the conditions of protocol A gave the expected annulated product 4a in a very low yield. However, after substantial experimentation, we found that performing the reaction in toluene at 110 °C (protocol C) led to the annulation product 4a as a mixture of diasteoisomers (62% yield). Under these new conditions and in the presence of DBU (protocol D), the N-benzyl bis-nucleophile 1i furnished the corresponding annulated product 4i, though in 49% yield. The seven-membered bis-electrophile 2c 18 reacted smoothly under the original optimized conditions in THF. However, while reaction with 1a required heating at 60 °C, reaction with 1g and 1i called for the presence of DBU at rt. As the Tsuji-Trost step is expected to be equally favored in all the cases studied, these experiments suggest that the intramolecular 1,4-addition step is easier for the 6-exo-trig and 7-exo-trig than the 5-exotrig ring closures.

Scheme 4. Variations of the cyclic ,β-unsaturated--oxycarbonyl

The use of cyclic ,β-unsaturated--oxy-carbonyls bearing a heteroatom was then investigated. We initially considered the formal incorporation of an oxygen or a nitrogen atom at position 6 of the parent cyclohexenone 4-benzoate (cyclohexenone numbering). However, preliminary experiments showed that  3 -allyl complexes deriving from substrates of this type have a strong tendency to β-eliminate leading to 2-pyrone or -pyridone, the driving force being very likely the aromatic character of these heterocycles (see SI). We therefore decided to consider the heteroatom at position 5 through the use of the 5-oxo-5,6-dihydro-2H-pyran-2-yl benzoate 2e 19 in the presence of 1a. After several relatively fruitless trials, we found that the use of the above-optimized system in the presence of 2.2 equivalents of 2e, provided the adduct 7a (30% yield), which resulted from a second allylation taking place besides the "normal" allylation / conjugate addition sequence (Scheme 5, top). This result suggests that in this particular case, the initially formed adduct is more activated toward over-allylation than the starting bisnucleophile 1a. We speculate that an intramolecular H-bond in the first generated intermediate A may be responsible of such original behavior. As expected, reaction of the benzylated β-amido-ester 1f gave, under identical condition, the regular adduct 7f in satisfactory yield (Scheme 5, bottom).

Scheme 5. Reactivity of cyclic ,β-unsaturated--oxycarbonyls bearing an oxygen at position 5

Finally, we reasoned that by using a bis-nucleophile bearing a juxtaposed halo-aryl moiety, the allylation / conjugate addition sequence could be coupled to an additional Pdcatalyzed intramolecular arylation. 20 The success of such an ambitious plan depends, inter alia, on the possibility of intramolecularly trapping the enolate issued from the nitrogen 1,4-addition the catalytically generated arylpalladium(II) moiety, without premature proton transfer from the acid carbon atom of the bis-nucleophile component. With the above caveat in mind, [START_REF]). (23) We found the annulation very sensitive to proton transfer factors. Thus, while the N-sulfonyl bis-nucleophile based annulations could be achieved only in the absence of base, most of the N-alkyl based ones were successful only in the presence of base. In line with this pronounced proton transfer sensitivity, it is also remarkable that the formal introduction of an o-bromo or o-chloro (but not an o-iodo) substituent to the N-phenyl amido-ester permits base-free conditions. (24) In the cases in which the base is not required, it is likely that the benzoate anion, counter-ion of the  3 -allylPd complex[END_REF] we initially investigated the reaction between the bis-nucleophile 1s, bearing a methyl group on the α-position, and bis-electrophile 2a (Scheme 6). After considerable experimentations (see SI), we found that treatment of the brominated bis-nucleophile 1s with cyclic ,β-unsaturated--oxy-carbonyl 2a in the presence of the system [Pd(OAc) 2 (10 mol %), dppb (15 mol %), DBU (2 equiv)] in toluene at rt for 10 min, followed by addition of K 3 PO 4 and further 2 h heating (145 °C oil bath in sealed tube), gave the desired cis/cis fused tricyclic compound 10s in 85% yield as major diastereoisomer, along with 7% yield of the minor diastereoisomer 10s' (Scheme 6). [START_REF]The relative stereochemistry of the compound was ascertained by 1 H NMR NOESY and coupling constant analysis. (16) N-methyl substitution and nitrile or nitro as resonance stabilizing group in the bis-nucleophile did not allow the desired annulation reaction[END_REF] Scheme 6. Triple domino allylation / N-conjugate addition / enolate -arylation Following this satisfactory result, the triple domino process was tested using the unsubstituted amides bis-nucleophiles 1n, 1q, 1r, and 1j (Scheme 7). To our delight, the targeted tricycle compounds 10n, 10q and 10r 22 were successfully isolated in good yields. In the case of 1n, the tricycle was accompanied by a minor amount (6% yield) of the demethoxycarbonylated product 11n. The homologated bromo-benzyl derivative 1j led to the desired triple domino product 10j, too. However, in this case, a prolonged reaction time was necessary and the major product consisted in the demethoxycarboylated product 11j. It thus appears that the intramolecular arylation is sensibly easier for the formation of 6/5/5 fused tricycles than 6/5/6 ones.

Scheme 7. Triple domino allylation/N-conjugate addition/enolate -arylation

A plausible mechanism for this annulation is presented in Scheme 8 for the reaction between N-o-bromophenyl amidoester 1n and 2-cyclohexenone 4-benzoate 2a. The sequence starts with an oxidative addition of the bis-electrophile 2a onto the Pd(0) complex to generate the corresponding transient  3allyl-complex B through the  2 -alkene complex A (steps a and b). The amidinium-enolate of the bis-nucleophile 1n, 23,24 formed in the meantime (step c), enters the catalytic cycle through C-allylation. Then, a new deprotonation by DBU and an oxidative addition of its aryl halide to Pd(0) generates amidate C (step d). Pd(0) decoordination (step e), to form D, opens the way to the intramolecular aza-conjugate addition to afford bicyclic enolate E (step f). Subsequent enolatepalladation (step g) followed by reductive elimination generates the final tricyclic product 10n and the Pd(0) complex. A notable feature of this process is the fact that the catalytic generation of the  3 -allyl-complex B completely wins against the alternative stoichiometric conjugate addition reactivity, a conditio sine qua non step chronology for the annulation. Indeed, a premature (N or C) conjugate addition reactivity would impede generation of the  3 -allyl-complex.

In conclusion, through the reaction between resonancestabilized acetamides as bis-nucleophiles and cyclic ,βunsaturated--oxy-carbonyl derivatives as bis-electrophiles, we have successfully developed a new domino transformation consisting of an intermolecular allylation / intramolecular nitrogen 1,4 addition The success this [3+2] C-C/N-C bond forming annulation is due to the total chemoselectivity of the former step (C-allylation) as well as to the well-defined chronology of the following steps. When the newly formed annulation product contains an appropriately located ortho-haloaryl moiety at the nitrogen substituent, a further intramolecular keto -arylation can follow the cascade, thereby forming two new cycles and three new bonds in the same synthetic action. In view of the several synthetically interesting structures incorporating the bicyclic 4,5-fused pyrrolidine motif, such as lycorine-type alkaloids, [START_REF] Chen | [END_REF] daphniphyllum alkaloids [START_REF] Shvartsbart | For a recent example[END_REF] and aeruginosins [START_REF] Scherer | For a recent example[END_REF] just to mention a few examples, and the many methods developed for the chemoselective and direct transformation of amides, 28 the current method is expected to find wide application in organic synthesis. Further work is ongoing to develop enantioselective versions of this new transformation.

  [START_REF]The relative stereochemistry of the compound was ascertained by 1 H NMR NOESY and coupling constant analysis. (16) N-methyl substitution and nitrile or nitro as resonance stabilizing group in the bis-nucleophile did not allow the desired annulation reaction[END_REF]

Table 1 . Optimizations on the model bis-nucleophile/bis- electrophile pair 1a / 2a

 1

	entry	[Pd] (x mol%)	ligand (y mol%) a)	yield (%) b)
	1	Pd(PPh3)4 (10)	-	67
	2	Pd(OAc)2 (10)	PPh3 (30)	73
	3	Pd(OAc)2 (10)	dppf (15)	60
	4	Pd2(dba)3 (5)		PPh3 (30)	81
	5	Pd2(dba)3 (5)		dppf (15)	82
	6	[Pd(η 3 -C3H5)Cl]2 (5)	dppf (15)	90
	a	dppb:	1,4-bis(diphenylphosphino)butane;	dppf:
	bis(diphenylphosphino)ferrocene.

b

Isolated yield.

ACKNOWLEDGMENT

The authors would like to acknowledge Horizon 2020 ERANet-LAC project CelluloseSynThech for financial support, as well as CNRS, UPMC and Labex Michem (Investissements d'Avenir programme under reference ANR-11-IDEX-0004-02). Support through CMST COST Action, CA15106 (CHAOS) is also gratefully acknowledged. We also thank L-M. Chamoreau and G. Gontard for the XRD analyses (IPCM) Y. L. thanks the China Scholarship Council for financial support.

ASSOCIATED CONTENT Supporting Information

The Supporting Information is available free of charge on the ACS Publications website. Further optimizations, experimental procedures, compound characterization (PDF).

AUTHOR INFORMATION Corresponding Author

* E-mail: julie.oble@sorbonne-universite.fr, giovanni.poli@ sorbonne-universite.fr ORCID Julie Oble: 0000-0002-4002-255X. Giovanni Poli: 0000-0002-7356-1568 Pei-Qiang Huang: 0000-0003-3230-0457 Scheme 8. Plausible mechanism of domino Tsuji-Trost allylation / aza-Michael / keto -arylation