Valentina Cima
email: valentina.cima@mnhn.fr

Benoît Fontaine

Isabelle Witté

Pascal Dupont

Martin Jeanmougin

Julien Touroult

A test of six simple indices to display the phenology of butterflies using a large multi-source database

Keywords: opportunistic data, citizen science, Lepidoptera, flight period, seasonality, bias correction, sampling effort

ouverte pluridisciplinaire HAL, est destinée au dépôt

A test of six simple indices to display the phenology of butterflies using a large multi-source database

Introduction

The rise of biological recording schemes including broad-scale citizen-science programs has brought new possibilities to conservation and ecological research over the last decades, producing large amounts of species occurrence data [START_REF] Dickinson | The current state of citizen science as a tool for ecological research and public engagement[END_REF][START_REF] Hochachka | Data-intensive science applied to broad-scale citizen science[END_REF][START_REF] Tulloch | Realising the full potential of citizen science monitoring programs[END_REF][START_REF] August | Emerging technologies for biological recording[END_REF], Pocock et al. 2015). When gathered into datasets that cover large temporal and spatial extents, these multi-source data (a combination of opportunistic and systematic records) may help unveiling important aspects of biodiversity state and changes [START_REF] Dickinson | Citizen Science as an Ecological Research Tool: Challenges and Benefits[END_REF][START_REF] Dickinson | The current state of citizen science as a tool for ecological research and public engagement[END_REF][START_REF] Hochachka | Data-intensive science applied to broad-scale citizen science[END_REF][START_REF] Isaac | Bias and information in biological records[END_REF][START_REF] Powney | Beyond maps: A review of the applications of biological records[END_REF], including species phenology. However, multisource data present different levels of standardisation (depending on the source of collection) and are by nature noisy and unstructured. They suffer from several biases [START_REF] Dickinson | Citizen Science as an Ecological Research Tool: Challenges and Benefits[END_REF][START_REF] Robertson | Getting the most out of atlas data[END_REF][START_REF] Isaac | Statistics for citizen science: Extracting signals of change from noisy ecological data[END_REF][START_REF] Isaac | Bias and information in biological records[END_REF] which primarily relate to variation in recording intensity [START_REF] Isaac | Statistics for citizen science: Extracting signals of change from noisy ecological data[END_REF]. In fact, sampling effort may vary throughout the year, between the years and among regions and this variability is usually unknown in opportunistic datasets [START_REF] Giraud | Capitalizing on opportunistic data for monitoring relative abundances of species[END_REF].

Phenology is the study of periodic biological events (such as plant flowering, insect emergence and bird migration) that are regulated by environmental factors. The simplest way to represent animal phenology is by counting the number of species occurrences per period, collating all years' data. This kind of representation is sometimes employed in broad distribution Atlases (some examples: [START_REF] Lumaret | Atlas des Coléoptères Scarabéides Laparosticti de France[END_REF] for dung beetles; the iNaturalist platform of crowdsourcing of data, for instance https://www.inaturalist.org/taxa/207977-Aglais-io; the Atlas of butterflies and zygens of Midi-Pyrénées, http://atlaspapillonsmidipyrenees.myspecies.info/) or in more specific studies (such as [START_REF] Bertone | Seasonal Activity and Species Composition of dung beetles inhabiting cattle pastures in North Carolina[END_REF][START_REF] Pozo | Seasonality and Phenology of the Butterflies (Lepidoptera: Papilionoidea and Hesperioidea) of Mexico's Calakmul Region[END_REF][START_REF] Archaux | Synthèse des observations récentes et anciennes de Libelloides coccajus (Denis & Schiffermüller) et L. longicornis (Linné) dans la moitié nord de la France (Neuroptera, Ascalaphidae)[END_REF]. Other works have reported phenology as weighted or mean counts [START_REF] Van Swaay | Monitoring butterflies in the Netherlands: how to get unbiased indices[END_REF][START_REF] Archaux | Papillons du Loiret: atlas des rhopalocères et zygènes du Loiret[END_REF][START_REF] Manil | Suivi Temporel des Rhopalocères de France (STERF) Bilan 2005-2014[END_REF] as well as modelled counts [START_REF] Dennis | Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern[END_REF][START_REF] Schmucki | A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes[END_REF], sometimes accounting for imperfect detection or uneven recording intensity [START_REF] Strebel | Studying phenology by flexible modelling of seasonal detectability peaks[END_REF].

Butterflies are relatively well known and well represented in citizen-science programs and national inventories. For this reason, they have frequently been the subject of studies based on monitoring and citizen-science data (see for example: [START_REF] Maes | Applying IUCN Red List criteria at a small regional level: A test case with butterflies in Flanders (north Belgium)[END_REF][START_REF] Dennis | Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern[END_REF][START_REF] Schmucki | A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes[END_REF]. Here, we focus on the representation of butterfly phenology patterns with multi-source opportunistic records while accounting for sampling effort. More precisely, this study aims to explore the potential of a national biodiversity reference system, the National Inventory of Natural Heritage of France (INPN -https://inpn.mnhn.fr), for displaying the phenology of butterflies at a national scale. The goal is to develop a phenology index with the following requirements: 1) the phenology is displayed using multi-source data, including opportunistic observations; 2) the index yields overall patterns that are consistent with standard knowledge on species phenology; 3) the index must be suitable for many species and different type of seasonality and voltinism (refer to [START_REF] Wolda | Insect Seasonality: Why[END_REF] for a precise classification of seasonality patterns); 4) the phenology charts will be presented to a general audience on a web portal, so the index must be simple, easily interpretable and fast-rendering.

Ideally, the index should inform on species activity over the year or display a lack of knowledge, inducing the community to collect more accurate occurrence data.

Some studies have highlighted the potential of opportunistic data to perform as well as standardised data (see for example, [START_REF] Van Strien | Siteoccupancy models may offer new opportunities for dragonfly monitoring based on daily species lists[END_REF][START_REF] Van Strien | Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models[END_REF]. This is generally true when opportunistic data are corrected for bias [START_REF] Van Strien | Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models[END_REF][START_REF] Isaac | Statistics for citizen science: Extracting signals of change from noisy ecological data[END_REF]. Measures that simply show the number of observations are sensitive to sampling effort and likely to reflect observer activity [START_REF] Dickinson | Citizen Science as an Ecological Research Tool: Challenges and Benefits[END_REF], while those that account for temporal and spatial variation in effort should better delineate the true phenology of a species. Under this assumption, six relatively simple indices, based on both non-corrected and corrected measures, were designed with butterfly records from the INPN. The indices were analysed against blind expert opinion and a set of monitoring data. We expected corrected indices to yield more realistic patterns than non-corrected ones. The results should lead to recommendations about selecting an accurate but simple index for displaying phenology with multi-source opportunistic data.

Materials and methods

General analysis process

The analysis started by selecting, with the help of a lepidopterologist (PD), a panel of butterfly species that are well known and well represented in the dataset. Their records come from different data sources (Appendix 1), were collected in several years and are assumed homogeneous over France. In order to assess the versatility of the indices, the selected panel had to be diverse in terms of phenology type, ecology and latitudinal range. Based on literature review, a series of non-corrected ("raw") and corrected indices were implemented on multi-source butterfly data and evaluated through two different approaches: a qualitative The INPN gathers multi-source data from scientific surveys, museum collections, citizen science programs, as well as opportunistic observations. These data are collected, standardised and synthesized in order to develop a national reference bank of biodiversity data. One record in the INPN corresponds to one species occurrence, collected by one observer whatever the number of individuals. A record contains a start and an end date of collection. The interval between the two dates ("temporal resolution") may vary from 1 day ("precise date of observation"), to several days (the duration of an inventory or sampling campaign) until several years, which is the case of observations derived from literature or museum collections with uncertain temporal information. On the other hand, data from several sources may have different spatial resolution. Nonetheless, when stored in the INPN, records are assigned to 10x10 km cells, according to a national reference grid and a standardised method, which allows for reliable spatial information.

Effort correction required data from a background or target group [START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF][START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF], Kéry et al. 2010[START_REF] Ruete | Displaying bias in sampling effort of data accessed from biodiversity databases using ignorance maps[END_REF]. Although we focused on butterfly phenology, we extracted all INPN records of diurnal lepidopterans (i.e. butterflies and diurnal moths, hereinafter referred as the "group", see Table 1) by assuming that survey methods and collector specialties within this group are similar, hence data share similar bias [START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF][START_REF] Ruete | Displaying bias in sampling effort of data accessed from biodiversity databases using ignorance maps[END_REF]. The group includes seven families: Pieridae, Papilionidae, Nymphalidae, Lycaenidae, Hesperiidae, Riodinidae, Zygaenidae. The French taxonomic repository TaxRef version 11.0 [START_REF] Gargominy | TAXREF v11.0, référentiel taxonomique pour la France : méthodologie, mise en oeuvre et diffusion[END_REF]) was employed for taxonomic references. Assuming that naturalists and collectors have similar knowledge and bias and use analogous survey methods for the group of butterflies and diurnal moths, we defined a "field visit" as a 10x10 cell surveyed by one observer on a date, regardless of the number of species he or she had observed. In this manuscript, we refer to a species "quadrat" as a 10x10 km cell where the species was recorded at least once in a given period. In the same way, one "group quadrat" is a 10x10 km cell where at least one of the species of the group was recorded. The vocabulary used for this study is summarised in Table 1.

Data preparation and index design

In the last decades, biological recording has intensified [START_REF] Isaac | Bias and information in biological records[END_REF]. In France, the mission of the MNHN to centralize information and managing a national reference bank of biodiversity started in 1979. Since those years, the collection of records at a national scale have become more frequent and rigorous, in particular with the development of national inventories and atlases [START_REF] Touroult | Inventaires et atlas nationaux de distribution: pour une approche plus itérative et un rééquilibrage taxinomique[END_REF]. On this basis, we presumed data from 1980 onwards to be more uniform and representative of current overall phenological patterns (e.g. number, position, sharpness of peaks). We restricted, therefore, the analysis to those data. Moreover, 1980-today corresponds to the time span of knowledge of the experts who took part in the survey.

In order to minimize errors, outlier data, such as records on January 1 st and December 31 th (probable by-default dates when the day or the duration of observation, for some reason, is not known) were discarded a priori. Ultimately, data for the whole group of butterflies and diurnal moths consisted in 772,307 records (Appendix 1).

Since records had different temporal resolution, we fixed a temporal unit of ten days for displaying phenological patterns. Assuming that non-precise records are still relevant for outlining overall phenological patterns, we kept all data with a temporal resolution (time interval between start date and end date, Tab. 1) up to 15 days, in order to keep as much data as possible. The uncertainty of dates was compensated with pro rata calculation. First, a record was duplicated or triplicated when its start and end date of collection overlapped two or three successive ten-day periods. Then a pro rata was calculated for each period, according to the number of days covered by the record collection dates. For instance, a record with start date 2015-07-21 and end date 2015-08-03 (temporal resolution: 14-days), is converted to one observation in the last ten-day period of July with a pro rata of 11/14 ≈ 0.78 and one record in the first ten-day period of August with a pro rata of 3/14 ≈ 0.22. Whenever start and end date are within a ten-day period pro rata is 1.

Equation 1

WEIGHTED NUMBER OF RECORDSk = ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 𝑛 𝑖=1

Where i = 1 ... n records of a species and a ten-day period k, all years combined.

The weighting adjustment was also applied for counting quadrats (weighted number of 10x10 km cells where a species or the group was detected at least once in a ten-day period k) and field visits. For simplicity, all weighted counts are henceforth mentioned as "number of …" (records, quadrats, field visits, etc.). The number of records of all species of the group ("group records"), the number of group quadrats and the number of field visits (section 2.2; Fig. 2) can be employed as proxy for sampling effort [START_REF] Lobo | Database records as a surrogate for sampling effort provide higher species richness estimations[END_REF][START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF][START_REF] Ruete | Displaying bias in sampling effort of data accessed from biodiversity databases using ignorance maps[END_REF] and used for normalising raw counts (see below).

Six indices (Tab. 2) were built using both species number of records (M1, M2, M3), and number of quadrats (M4, M5, M6). M1 and M4 are raw relative frequencies by ten-day period.

The other indices are less intuitive and data preparation requires more computing time to build them. Despite that, they should correct for bias due to uneven recording intensity over time or space (Tab. 2). They were built by normalising the number of species records or quadrats by: the number of field visits (M3); the number of group records (M2); the number of group quadrats (M5); the number of group quadrats within the species known distribution (M6), where "known distribution" is the set of 10x10 km cells where the species was recorded at least once since 1980. Mountains (ALMM), and Mediterranean (MD). For such species, we considered only biogeographic regions where at least 36 total records were available since 1980. The threshold was defined assuming that 36, an average of three records per month, was the minimum sample size for displaying patterns based on pooled counts. This threshold was also a tradeoff between avoiding unreliable, insufficient data and maximising information (i.e. keeping regions where some species are rarer). The indices were computed on pre-cleaned data (see previous section), yielding six different phenological patterns that were submitted in the form of bar plots to four experts. The bar plots displayed frequencies by ten-day periods and, in some cases, by biogeographic regions (Fig. 3 and Appendix 2). For every species, the four experts were asked to rate six charts, one per index, on a scale of 1 to 4 ("very poor", "poor", "fair", "good"), according to their quality in representing known seasonal activity (i.e. flight phenology, in the case of diurnal lepidopterans). In order to avoid conditioning and keep their judgment unbiased, experts were not aware of index design rules. In addition, the disposition of the bar plots was randomized, so that they could not individually identify the indices (Appendix 2). Every index was ranked by counting the number of times it had been rated as "fair" or "good" (i.e. "the phenology chart is representative enough") on the one hand, and as "very poor" (i.e. "the phenology chart is not representative at all") on the other hand. In addition, a Chi-squared (χ 2 ) test was performed on score frequencies to seek any significant difference between the phenology indices according to expert opinion.

Quantitative analysis: comparison with the STERF

A comparison with an independent sample was performed in order to quantitatively Since data collection before May and after August had not been systematic, only STERF records between May and August were kept. The analysis focused on the ATCONP biogeographic region, where phenology is supposed to be uniform and STERF data are more regular and abundant. As in the previous analysis (section 2.4), we fixed a threshold of 36 records in the ATCONP region.

For each species, annual phenologies were estimated using the rbms R package and the regional GAM (generalized additive model) method presented in [START_REF] Schmucki | A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes[END_REF]. For each species, weekly basis estimate counts were hence obtained for each week of each year.

These weekly and yearly count estimates were used to model the average phenology of each species across the period covered by the STERF. Thus, for each species, weekly count c recorded years i at week t were modeled using a GAM with a Negative-binomial distribution and log link function:

𝐸[𝑐 𝑖𝑡 ] = µ 𝑖𝑡 = 𝑒𝑥𝑝[𝑦 𝑖 + 𝑠(𝑡, 𝑓)]
where weekly count cit is a function of a year effect y and a penalized cubic regression splines smoothing effect over time (week) t with f degree of freedom. GAMs were computed thanks to the mgcv package [START_REF] Wood | Generalized additive models: an introduction with R[END_REF]) in R 3.5.2. The respective GAM for each species was finally used to predict ten-day period count estimates that could be compared to the phenology indices calculated with INPN data. The resulting flight curve were standardized to one (Σμt = 1). This curve could be calculated for 57 species. The others were excluded for lack of data and because the first GAM failed to yield annual phenologies.

STERF count estimates at ten-day periods were compared to the phenology indices in order to verify: 1) whether the patterns were similar, 2) which index produces a pattern that best matches STERF data and can be used for representing species phenology with INPN data.

Since STERF data are already integrated in the INPN database and start in 2005, the indices were calculated excluding all STERF-derived records and other records before 2005.

For each of the 57 species, STERF count estimates at ten-day periods were compared to each of the six phenology indices computed on INPN data, using the Pearson's correlation coefficient (rho). In order to highlight which index was most correlated to STERF pattern, all rho coefficients for the 57 species were analysed together and the indices were ranked by counting the number of times they obtained the maximum and minimum rho coefficient.

Table 3 illustrates with fictitious results how the indices were compared between each other according to the number of maximum and minimum coefficient obtained among several species: for example, M4 is the best correlated index to the STERF for species A and B, while it is the least correlated for species C. In this case, M4 obtains twice the maximum rho coefficient and once the minimum rho coefficient (Tab 3). In addition, the difference between rho distributions per index was tested by carrying out a one-way ANOVA (homoscedasticity assumption was met, according to a studentized Breusch-Pagan test: BP = 5.95, df = 5, pvalue = 0.31).

Results

For each of the 32 species, charts were presented to the experts as in Fig. 3a or Fig. 3b, depending on whether a biogeographic effect was expected or not on species phenology.

Unlike Fig. 3, the order and the design rules of the indices were concealed from the experts (see an example in Appendix 3). M6 obtained the highest number of positive scores and the smallest number of negative scores: out of the 4 experts assessments, it was rated as "fair" or "good" 62 times and as "very poor" 11 times, followed by M4 with 61 positive scores and 11 negative scores (Fig. 4). M2 obtained most negative responses (Fig. 4), which affected Chisquared test results (χ 2 = 43.73, df = 5, p-value < 0.001). In fact, when the M2-score distribution was excluded from the test, no other significant difference was found among the score distributions of the remaining five indices (χ 2 = 3.1, df = 4, p-value = 0.54). Most of the times M2 resulted as the least correlated to STERF count estimates (Fig. 6b). M2

rho distribution varied also considerably, with values below the first quartile that range from -0.34 and 0.56 (Fig. 6a). Nevertheless, the statistical analysis did not highlight any significant difference (ANOVA F= 2.19, df=5, p-value=0.05). With a similar approach, we applied GAMs on butterfly monitoring data for outlining phenology patterns to compare with patterns from INPN data. However, GAMs are not suitable for multi-source opportunistic data, because they do not account for uneven recording intensity [START_REF] Rothery | Application of generalized additive models to butterfly transect count data[END_REF]. Other approaches, such as correction for sampling effort or imperfect detection in occupancy models, could inspire research for more appropriate measures (references in Table 2). M1 (proportion of records per period; Tab.2) is the most simple and intuitive, but may reflect effort variability over time and over space. M4

(proportion of quadrats per period; Tab.2), which is also easily interpretable, may correct for some errors (duplicates, oversampled quadrats), but still reflects temporal and spatial bias.

Conversely, M2 (ratio of records to the group; Tab.2) should correct for uneven recording intensity. However, this index seems to be particularly sensitive to "reporting bias" (van ). Owing to the unusual event, the species is almost the only one reported at such dates, generating artefact peaks in the phenological pattern (i.e. the ratio "species records/group records" is close to 1). The same may occur to species that are not easy to observe in general during the flight season (i.e. the ratio decreases instead of displaying a peak) and to those that are paradoxically underreported because considered too common [START_REF] Dickinson | Citizen Science as an Ecological Research Tool: Challenges and Benefits[END_REF][START_REF] Van Strien | Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models[END_REF]. The proxy for observation effort used for building M3 (number of records per field visit; Tab. 2; Fig. 2c) should also correct for uneven recording intensity. This index, as the following ones, is less intuitive and may not be precise, since observer names are not always well standardized in the INPN. Such as M2 and M3, M5 (ratio of quadrats to the group; Tab. 2) is based on a proxy for observation effort, the number of group quadrats per ten-day period (Fig. 2b). As shown in Figure 2b, compared to the other proxy distributions, the number of group quadrats per ten-day period grows and shrinks slower between winter and summer periods and the shape of the distribution is wider. This should limit reporting bias and allow for a better correction of temporal bias, but it does not account for spatial effects (for example, observer activity in coastal and mountain areas is higher during summer than during spring, due to holiday habits of naturalists). Moreover, the proxies for observation effort (Fig 2)

Strien
include those records or quadrats where the target species was never seen and might be actually absent. Managing data by biogeographic zone may help reducing spatial bias and over-correction. Additionally, quadrats should be restricted to those included in the known distribution of the species. This approach was applied to M6 (ratio of quadrats to the group within the species known distribution; Tab. 2), which was expected to perform better, since it deals with both temporal and spatial bias.

Which is the highest-ranked index according to the two approaches?

According to expert responses, none of the indices, except for M2 (the worst rated) could illustrate the phenology of 32 butterfly species better than the simplest index, M1 (Fig. 4). In order to refine the results and discriminate among the six methods, a quantitative analysis was carried out by comparing the phenology indices based on INPN opportunistic data to the pattern calculated from a monitoring scheme, the STERF. All indices were well correlated to the STERF in this analysis (Fig. 6). Surprisingly, in the two tests, the indices that correct for observation effort (M2, M3, M5 and M6) are not statistically better than the noncorrected ones (M1 and M4). However, even if no statistical difference was highlighted, the distribution of rho coefficients for M2 showed large variability (Fig. 6a). This result suggests that this index may perform well for some species, but it is not suitable for a large panel of species with different phenologies. In substance, the expert approach and the comparison with the STERF both lead to reject M2 as a suitable phenology index.

Other studies have compared opportunistic data with monitoring data (Dennis et al. 2017a;[START_REF] Van Strien | Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models[END_REF]. It is possible that our comparisons lack power. The STERF provides systematic counts of adult butterflies, which should provide phenological patterns that are relatively close to reality. Nevertheless, STERF protocol and measures themselves may not be free from bias, notably because counts are recorded on a monthly basis. However, given the two approaches adopted, the number of species that have been taken into account, as well as the amount of data and the variability of expert opinions, it is not unfounded to believe that, there are no major differences between the tested indices. A possible explanation lies in the large amount of data and sources available for butterflies, which may help attenuate the bias and make raw frequencies of opportunistic observations converge towards overall realistic phenological patterns. Our method may not be suitable for other lepidopterans or other clades for which knowledge and data are much scarcer than for the butterflies analysed here. If data are opportunistic and multi-source, we suggest selecting species with many records, covered by several data sources. We fixed a threshold of 36 total records in the studied area and during the entire period of study (in our case, all data from France or from a biogeographic region, recorded since 1980), and 5 data sources, knowing that for most of the species total records and sources were more numerous (Appendix 1). Ideally, the minimum number of records and sources should be calculated and standardised. Further work is required to re-define these thresholds on a statistical basis and give more recommendations about the use of our indices for other groups than butterflies.

recognize and they are regularly used for detecting the geographic presence of the butterfly.

These occurrences are often reported as all others in datasets when achieving the INPN. This caused the appearance of improbable peaks in winter (Fig. 7). Correcting for observation effort did not provide any added value, it rather accentuated the aberrations. This underlines the need to support data producers towards a better standardisation of information.

Several studies have documented the relationship between climate and phenological shifts [START_REF] Roy | Phenology of British butterflies and climate change[END_REF][START_REF] Walther | Ecological responses to recent climate change[END_REF][START_REF] Parmesan | A globally coherent fingerprint of climate change impacts across natural systems[END_REF][START_REF] Stefanescu | Effects of climatic change on the phenology of butterflies in the northwest Mediterranean Basin[END_REF][START_REF] Menzel | European phenological response to climate change matches the warming pattern[END_REF][START_REF] Parmesan | Influences of species, latitudes and methodologies on estimates of phenological response to global warming[END_REF][START_REF] Altermatt | Climatic warming increases voltinism in European butterflies and moths[END_REF][START_REF] Prodon | A reversal of the shift towards earlier spring phenology in several Mediterranean reptiles and amphibians during the 1998-2013 warming slowdown[END_REF][START_REF] Bell | Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century[END_REF]. The impact of climate change on phenology may have major consequences on ecological systems and their conservation [START_REF] Schwartz | Phenology: An integrative environmental science[END_REF], hence the importance of longterm collection of observational and monitoring data. Phenological shifts of the order of some days may have occurred in the last decades [START_REF] Roy | Phenology of British butterflies and climate change[END_REF]. Admittedly, we could not point out phenological shifts due to coarse temporal resolution. A finer temporal resolution would entail the loss of useful data. Furthermore, we chose to collate all years' data in order to compensate for uneven recording intensity and species detectability across the years. This also precluded the study of phenological shifts. However, we believe that our methods are adequate to provide a simple measure for a general audience that bears overall phenological patterns for many species. Nevertheless, we will consider investigating with more accuracy the phenology of those butterflies for which precise dates of observation are abundant and

Conclusion

Multi-source national databases such as the INPN may contain bias or redundant information but they compile large volumes of data, centralize and spread knowledge on biodiversity distribution and activity. The study showed, against all odds, that raw frequencies can perform as well as corrected measures, probably due to the characteristics and the large amount of butterfly multi-source data. This is so far the first attempt at correcting large amounts of opportunistic records in order to illustrate species seasonality in France through a simple phenology index.

For groups with large amount of data and replicated visits, such as butterflies, noncorrected multi-source records (i.e. that combine standardised and non-standardised observations) probably provide sufficient information about overall phenological patterns.

The next question is whether raw frequencies would fit as well on species with less available data, or on those that are aseasonal (e.g. occur constantly around the year or variations are irregular and not season-related, see Wolda, 1998). Further work will help investigate whether the properties of non-corrected and corrected indices are affected by the amount of data, as well as the variability of data sources and the type of phenology. Further analysis may also include the use of statistical models for estimating phenological shifts in connection with climate change.

  analysis, based on expert evaluation of the phenological patterns drawn by the indices, and a quantitative analysis to seek for a match between the indices and the patterns based on monitoring data. The different steps of the analysis are developed in the next sections and summed up in Fig. 1. All analyses were carried out in R v.3.5.2 (R Core Development Team 2018).

Figure 1 .

 1 Figure 1. Scheme of the general analysis process for this study.

Figure 2 .

 2 Figure 2. Seasonal distribution by ten-day period of three proxies for observation effort in diurnal lepidopterans (after data preparation). The x-labels indicate months. (a) group records: number of records of all species of the group; (b) group quadrats: number of quadrats where whatever species of the group was seen; (c) number of field visits: number of quadrats surveyed by one observer on a date for the group.These values were used for normalising counts and building, respectively, index M2, M5 and M3.

  analyse the pertinence of the indices and complete the analysis based on expert opinion. This sample was provided by the French Butterfly Monitoring scheme (STERF), established from 2005 onwards by Vigie-Nature (http://vigienature.mnhn.fr/page/suivi-temporel-desrhopaloceres-de-france). The STERF provides systematic counts of adult butterflies, which should mirror true species phenology. Each observer performs at least four field visits per year: one visit per month from May to August (other visits during the year are possible). Each survey site is associated with one observer, and is either chosen by the observer or randomly selected. Butterflies are identified and individuals are counted along 5 to 15 transects selected by the observers inside the site, making sure that the habitat within each transect is uniform.

Fig. 5

 5 Fig. 5 shows an example of pairwise comparison between STERF count estimates and the six phenology indices for one of the species, while overall results of this comparison for 57 butterfly species are illustrated in Fig. 6. All indices showed a fair correlation with the STERF, rho coefficient being 0.77 on average (Fig 6a). Index M1 and M5 were the best ranked, with the highest frequencies of maximum rho coefficient and few minimums (Fig 6b).

Figure 3 .

 3 Figure 3. Phenology and distribution maps (based on INPN records after data preparation) of two butterfly species: (a) Provence chalk-hill blue (Lysandra hispana) and (b) Violet Fritillary (Boloria dia). Time unit is a ten-day period and the x-labels indicate months. Phenology is represented with six indices (see also Tab. 2): proportion of records per period (M1); ratio of records to the group (M2); number of records per field visit (M3); proportion of quadrats per period (M4); ratio of quadrats to the group (M5); ratio of quadrats to the group within the

Figure 4

 4 Figure4frequency of positive ("fair" or "good") and negative ("very poor") scores given by experts to six phenology indices. Index ranking according to the number of "fair or good" scores: M6, M4/M5, M1, M3, M2; index ranking according to the number of "very poor" scores: M2, M3, M5, M1, M4/M6. M2 is the only index that obtained more negative than positive scores. With M2: χ2 = 43.73, df = 5, p-value < 0.001; excluding M2: χ2 = 3.1, df = 4, p-value = 0.5.

Figure 5 .

 5 Figure 5. Comparison between the STERF and six phenology indices computed on INPN data by ten-day period for one of the species, the green-veined white (Pieris napi). A Pearson's correlation coefficient (rho) was calculated between every index (blue bar plots) and STERF count estimates from a GAM (red line). Indices and STERF count estimates were calculated with data collected from 2005, and displayed

Figure 6

 6 Figure 6 Comparison between the STERF and six phenology indices computed on INPN data by ten-day periods for 57 butterfly species (a) distributions and mean±se of rho coefficients by index. Dashed and dotted lines are respectively the overall median=0.83 and the overall mean=0.77 of all correlations. All phenology indices are equally correlated to STERF count estimates (ANOVA F=2.19, df=5, p-value=0.05); (b) number of times among 57 species that each index occurred to be the best or the least correlated to the STERF.

  et al. 2013). The number of group records increases sharply from near zero in winter months to near 80000 in summer months (Fig 2a), causing over-correction and making peaks shift from the centre to the edges of the distribution. This is particularly true for certain species, whose adults can emerge outside the habitual flight period, for instance on sunny winter days (such as Aglais io and Aglais urticae, Lafranchis and Geniez 2000; Lafranchis et al. 2015; phenological patterns of Aglais io are shown in Appendix 4

  consistent through the years by adapting methods that use statistical models, which account for phenological changes over time, observation effort, detection and reporting bias, such as those proposed by Dennis et al. (2017b) and Strebel et al. (2014).

Figure 7 .

 7 Figure 7. Phenology of the Brown Hairstreak (Thecla betulae). Egg and imago sightings are confused in the INPN, causing artificial peaks in winter, when the adult is not flying but eggs are easily detectable.

Table 1 .

 1 Vocabulary used in this study.RecordObservation or collection data of a species provided by one observer on a precise date or time interval, in one locality (a 10x10 cell), whatever the number of specimens Quadrat Spatial unit. A 10x10 km cell where the species was recorded at least once in a period. A group quadrat is a 10x10 km cell where at least one of the species of the group was recorded

		in a period
	Field visit	A unique survey event. One quadrat surveyed by one observer on a date for a group
	Species known distribution	Set of 10x10 km cells where the species was recorded at least once since 1980

Temporal resolution

Time interval between start date and end date of an INPN record. It may vary from one day (precise date) to several years Group A background or target group. Survey methods and collector specialties within a group are assumed to be similar and share similar bias

[START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF][START_REF] Ruete | Displaying bias in sampling effort of data accessed from biodiversity databases using ignorance maps[END_REF]

. Examples of target groups are orchids, ground beetles, dragonflies, bats. This study focuses on "butterflies and diurnal moths", a well-known group for which data are abundant and mostly reliable

Table 2 .

 2 Phenology indices designed for this study.

	178
	Index	Definition	Formula (for a ten-day period k)	Description	Reference work that inspired the design of the index	A priori properties of the index
	M1	Proportion of records per period	∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 / ∑ ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 𝑛 𝑖=1 36 𝑘=1 𝑛 𝑖=1	Number of records (1...n, see equation 1) for a species in a ten-day period k divided by the total number of records of the same species in the dataset	Lumaret, 1990 Archaux et al. 2011 ("number of observations") van Swaay (1990) ("number of records")	Advantages: very simple and intuitive. Disadvantages: probably biased by uneven recording intensity (spatial and temporal bias).
	M2	Ratio of records to the group	∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 / ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 𝑚 𝑖=1 𝑛 𝑖=1	Number of records (1...n) for a species in a ten-day period k divided by the number of records of all species of the related group (1...m) in the same ten-day period	van Swaay (1990) ("percentage")	Advantages: simple and intuitive; temporal bias correction. Disadvantages: may be sensitive to reporting bias, emphasizing artefact peaks.
	M3	Number of records per field visit	∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 / ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 𝑝 𝑖=1 𝑛 𝑖=1	Number of records (1...n) for a species in a ten-day period k divided by the number of field visits (1...p) in the same ten-day period	Archaux et al. 2015 ("mean abundance by field visit") Strebel et al. 2014 ("naïve detectability index")	Advantages: temporal bias correction. Disadvantages: does not correct for spatial bias; less simple and less intuitive.
	M4	Proportion of quadrats per period	𝑞 ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 / ∑ ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 36 𝑞 𝑖=1 𝑘=1		Archaux et al. 2015 ("number of occurrences")	Advantages: very simple and intuitive; may correct for some bias (e.g. duplicates, quadrat oversampling). Disadvantages: still reflects uneven
								recording intensity.
						Number of quadrats (1...q) where the species was seen
			𝑞	𝑔		at least once in a ten-day period k divided by the	van Swaay (1990) ("percentage of	Advantages: temporal and reporting bias
	M5	Ratio of quadrats to the group	∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 / ∑ 𝑝𝑟𝑜𝑟𝑎𝑡𝑎 𝑖 𝑖=1 𝑖=1	number of visited quadrats (1...g) in same ten-day period (i.e. quadrats where at least one species of the group was seen)	squares") Turin and den Boer (1988) ("corrected number of squares")	correction; should limit reporting bias. Disadvantages: does not correct for spatial bias; not too simple; less intuitive.
	M6	Ratio of quadrats to the group within the species known distribution	∑ 𝒑𝒓𝒐𝒓𝒂𝒕𝒂 𝒊 /[∑ 𝒑𝒓𝒐𝒓𝒂𝒕𝒂 𝒊 𝒈 𝒊=𝟏 𝒒 𝒊=𝟏	]skd	Same as M5, but takes into account only quadrats where the species was found at least once since 1980 (species known distribution: skd)	Kéry et al. 2010 ("detection history")	Advantages: may correct for temporal and spatial bias. Disadvantages: not too simple and not intuitive.

𝑖=1

Number of quadrats (1...q) where the species was seen at least once in a ten-day period k divided by the total number of quadrats where the species was observed

Table 3 .

 3 Fictitious Pearson's correlation coefficients (rho) table for three hypothetical species.

								Best	Least
		M1	M2	M3	M4	M5	M6	correlated	correlated
								index	index
	species A	0.94	0.8	0.95	0.98	0.92	0.97	M4	M2
	species B	0.77	0.5	0.82	0.93	0.75	0.77	M4	M2
	species C	0.89	0.85	0.78	0.68	0.82	0.91	M6	M4
	mean±SE	0.86±0.05	0.72±0.11	0.85±0.05	0.83±0.09	0.83±0.05	0.88±0.06
	Number of
	max rho	0	0	0	2	0	1

Nymphalidae:

Pieridae:

Papilionidae

Papilionidae:

Acknowledgments

We thank the respondents of the index quality questionnaire: Frédéric Archaux, Olivier Delzons, Eric Drouet and Antoine Leveque. We also thank all the respondents of a previous version of the questionnaire, which included more species and groups that are not treated in this manuscript. Thanks to Marie-Lyne Einspenner, Chiara Elettra Ferrario and Paula R. M. B. for constructive help on previous drafts of the manuscript.

Selecting a rigorous but simple index for large opportunistic data and a general audience: which compromise for butterflies?

Even though they are likely to reflect bias, in our case raw indices (M1 or M4) were not less convincing than corrected ones for butterflies. Following the results and considerations discussed above, and against expectations, it could finally be reasonable to consider the use of raw frequencies. Besides, the choice should head to the most parsimonious method. The best option would probably be to display phenology through raw indices (after data preparation and weighting adjustment), alongside a visual representation of recording intensity (Fig. 2b or 2c). Spatial bias may be attenuated by splitting data by biogeographic region. In this way, both experts and general public should be able to understand the graphics, while keeping a critical eye on them. Some authors have appealed to occupancy models for studying phenology and population trends with opportunistic data (see, for example, [START_REF] Kéry | Site-Occupancy Distribution Modeling to Correct Population-Trend Estimates Derived from Opportunistic Observations[END_REF][START_REF] Van Strien | Siteoccupancy models may offer new opportunities for dragonfly monitoring based on daily species lists[END_REF][START_REF] Strebel | Studying phenology by flexible modelling of seasonal detectability peaks[END_REF]. In fact, by estimating detectability, occupancy models help to correct for observation effort, detection and reporting bias [START_REF] Kéry | Site-Occupancy Distribution Modeling to Correct Population-Trend Estimates Derived from Opportunistic Observations[END_REF][START_REF] Van Strien | Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models[END_REF]). Although we drew inspiration from occupancy models for bias correction approaches, the aim of our study was not to assess occupancy of butterfly species. Nonetheless, we do not exclude the possibility of using a modelling approach in the future, should sufficient data be available, in order to get more unbiased estimates of phenology.

Other recommendations facing database limitations

Dates in the INPN are compulsory, so when the day of observation is not known a default date (such as January 1 st or December 31 st ) is assigned. We discarded outlier data (see section 2.3) in order to prevent the appearance of artefacts, although some small winter peaks persisted. In fact, as described in section, 4.1, some butterfly species may be recorded during winter days, even if the actual period of activity is later in the year. Unfortunately, we could not discriminate observations by stage of development or behaviour (activity versus hibernation), since this information is currently lacking in most INPN data sources. However, records of active adult butterflies in the INPN usually far exceed observations of young stage or wintering individuals, so the latter are unlikely to affect the displaying of major activity peaks. There are, however, some exceptions, such as those described in section 4.1 or the case of Thecla betulae (Linnaeus, 1758). This species is hardly detectable during its flight period (Lafranchis and Geniez 2000;Lafranchis et al. 2015). Conversely, the eggs are easy to

630

Appendix 2. Example of charts presented to the experts for rating the quality of the indices. The order of bar plots was randomized and the rules behind index building were kept from the experts so that they could not prejudge the index in itself or identify the same index among several species.


N Kalashnyk

S Noël

P Chrétien

A Brézard-Oudot

Th Leblanc

F Houzé

F Hauquier

V Derycke

T Berthelot

D Rousseau

Printed copper connections on flexible substrates

Keywords: component; copper interconnection, Metal Organic Decomposition ink, jet printing, Intense Pulsed Light treatment. I

This paper presents our first results on printed copper connections on flexible organic substrates obtained in a singlestep process. Many progresses have been made in the field of conducting inks and printing systems and potential applications are within reach. Nowadays the most common conducting inks involve silver nanoparticles. However this solution presents some drawbacks like the high cost of these inks due to the base metal and to the synthesis and also their tendency to clog nozzles in industrial inkjet printheads. For these reasons we have started a program on copper inks for the formation of conducting, homogeneous and adherent connections on polyethylene terephthalate films in view of applications in the field of flexible electronics. The main challenge is to print copper patterns on PET with an industrial process that would present a high time to readiness level. Multiple technical obstacles are addressed: i)-The ink needs to be nanoparticle free: we have chosen a metal organic decomposition ink (MOD) where copper salts are combined with ligands and additives, ii) it must be compatible with an inkjet system in terms of viscosity, rheology and iii) the printed copper must be conducting and adherent. This means that reduction of copper ions must be achieved using a postprinting treatment harmless to the substrate. Intense Pulsed Light was chosen. Printed patterns were characterized by various means: macroscopic electrical behaviour by 4 probe measurements, microscopic electrical behaviour by conducting probe Atomic Force Microscopy (AFM). Morphological and chemical analyses were also performed. It is shown that in optimised conditions good conductivity can be achieved. Much work is still needed to fully understand the mechanisms involved and realize electronics circuits.

INTRODUCTION

This work aims at understanding and finding the proper conditions to print copper interconnections on low temperature polymer substrates such as PET (polyethylene terephthalate). Many very complete literature reviews have been published lately on conductive inks for printed electronics. In 2014 Wunscher et al. compiled 245 references on sintering of metal inks [START_REF] Wünscher | Progress of alternative sintering approaches of inkjet-printed metal inks and their application for manufacuting of flexible electronic devices[END_REF]; in 2015 Abhinav et al. compiled 136 references on copper conductive inks [START_REF] Abhinav | Copper conductive inks: synthesis and utilization in flexible electronics[END_REF], in 2017 Lim et al. compiled 113 references on intense pulsed light curing [START_REF] Lim | Intense pulsed light for split-second structural development of nanomaterials[END_REF] and in 2018 Raut et al. compiled 200 ones on inkjet printing metals for flexible electronic [START_REF] Raut | Inkjet printing metals on flexible materials for plastic and paper electronics[END_REF]. This is a large set of references to refer to. Nevertheless little has been reported on IPL (Intense Pulsed Light) sintering of printed MOD inks on PET. Much of the work reported concerns print screening deposition process, nanoparticle inks, thermal post treatments at relatively high temperature in inert gas for 30 min or so but they are very valuable source of information.

II. SAMPLES AND EXPERIMENTAL TECHNIQUES

A. Samples

The substrate is a PET foil 125µm thick from Normandy Coating France. The metallic salt inks chosen are based on formulation of the metal organic decomposition (MOD) ink which have been reported to be reduced to metallic copper [START_REF] Rosen | Self-reducing copper precursor inks and photonic additive yield conductive patterns under intense pulsed light[END_REF][START_REF] Farraj | Self-reduction of a copper complex MOD ink for inkjet printeing conductive patterns on plastics[END_REF][START_REF] Shin | A self-reducible and alcohol-soluble copper-based metal-organic decomposition ink for printed electronics[END_REF][START_REF] Wang | Cu ion ink for flexible substrate and highly conductive patterning by intense pulsed light sintering[END_REF] Several formulations have been studied based on copper formate (C 2 H 10 CuO 8 ) and AMP (2-amino-2-methyl-1propanol) complexes (Cuf-AMP) as described in [START_REF] Farraj | Self-reduction of a copper complex MOD ink for inkjet printeing conductive patterns on plastics[END_REF][START_REF] Shin | A self-reducible and alcohol-soluble copper-based metal-organic decomposition ink for printed electronics[END_REF].

The paper does not present the multiple formulations tested but summarizes the main steps of a collaborative research project funded by Agence Nationale de la Recherche. The copper formate AMP complexe (denoted Cuf-AMP) inks were purchased from Gene'Ink -Rousset France). Various solvents, additives, binders were used for the preparation of several batches all having in common low surface tension values (27 mN/m<γ<28 mN/m), low viscosities at 20°C (9.6 cP<η< 12.5cP), densities around 1 and pH values in the range 7.4 to 8.4. These characteristics allowed to jet print patterns on PET sheets.

In a first step a Dimatix DMP-2831 printer was used with the following printing parameters: drop volume 10pL, drop spacing 35µm, bed temperature 50°C. Fig. 1 a) shows the ink while Fig. 1 b) shows an example of drops printed on the PET film. Several passes were needed to obtain better homogeneity of the films.

The films were then sintered with an intense pulsed light lamp (IPL) the Xenon S2200 (fitted with a B lamp 240nm -1100nm). When the sample was exposed to the pulse light a complex process took place resulting in local decomposition of the copper MOD ink without damaging the substrate [START_REF] Wang | Cu ion ink for flexible substrate and highly conductive patterning by intense pulsed light sintering[END_REF].

Several settings were possible, changing the pulse duration, the cooling off duration, the total energy and the number of pulses [START_REF] Joo | Highly conductive copper nano/microparticles ink via flas light sintering for printed electronics[END_REF].

The major difference of this work with the published literature is that its purpose was to propose a process for industrial manufacturing: therefore the inks are MOD type inks to avoid nanoparticules, the printing is direct and fast which has its drawbacks (thin films that can be uneven), the curing process is performed in air and is very fast as is expected for a production line.

Therefore one of the major difficulties was to assess the performances of the IPL sintering settings without interference of the quality of the printing. Thus a very large number of samples were prepared and analyzed.

B. Characterization techniques and experimental test

Several techniques were used to evaluate the inkjet printed patterns.

Electrical characterisation 1)

a) 4 point resistivity measurements : Electrical measurements were performed with a Signatone Pro4 4 point probe resistivity device; the four point probe head specifications are: 1.587 mm tip spacing, 0.254 mm tip radius and 45 gf normal load on each tungsten carbide tip. The aim was to quickly assess the macroscopic conductivity of the films.

b) Conductive probe Atomic Force Microscopy (AFM)

AFM topography and electrical maps were obtained with a Bruker MultiMode8 equipment fitted with a special homemade amplification and conversion device, called Résiscope (referred to as CP-AFM) in order to acquire simultaneously topographic and electrical images of the surfaces using doped diamond coated tips (radius ≈ 150 nm). The intrinsic resistance of these probes is about 5 kΩ. The aim was to have a local evaluation of the conduction in relation to the topography. c) V(I) curves and surface temperature measurements V-I measurements were performed with a 2450 Keithley Sourcemeter. During some of the runs the sample was filmed with a Jade MWIR camera (Cedip) calibrated with a black body for copper sample. The rate of acquisition was 1Hz to measure the increase of the surface temperature with the current flow. The spectral response of the InSb detector was in the range 3.5 µm -5.1 µm. The range of temperature was 5-150°C. The aim was to evaluate the possibility of an application.

Composition 2)

Transmission measurements were achieved by means of a modified Fourier Transform Infrared (FTIR) spectrometer that allows measurements in the range 350-1800 nm within a few seconds with a resolution of the order of a nanometer as described in [START_REF] Puspitosar | Modification of an FTIR spectrometer for optoelectronic characterizations[END_REF].

The surface chemical composition was analyzed with a PHI 5000 Versaprobe multi-technique XPS using a monochromatic Al Kα source. The system was calibrated with Cu 2p3/2 and Cu LMM of clean copper at 932.7 eV and 918.7 eV respectively. The take-off angle was 45°. Charging was compensated by a very low electron beam.

III. RESULTS AND DISCUSSION

A. Uniform film printing

The Cuf-AMP MOD inks were printed on the PET sheets to form square patterns (10mm x 10mm) for all the experiments. Fig. 2 shows samples patterns printed with 1 pass, 3 passes and 5 passes. The film thickness is observed to increase with the number of passes. For one pass the film is very transparent and side effects can be observed.

Formation of drops on the surface depends of the surface energy of the substrate, the surface tension of the ink, the atmosphere and the temperature of the substrate holder. Complex mechanisms can occur when there is a mixture of various solvents: edge effects, dewetting, Marangoni effect. The chosen inks had good wetting properties; some printing defects can nonetheless be observed on some of the samples. These patterns were almost liquid and the thickness was not easily measurable. Most of the characterizations were thus performed after the photonic treatment.

B. Sintering of the the ink patterns

In order to avoid degrading the PET substrate (softening temperature 70°C and melting point 250°C) IPL treatment was chosen to transform the Cu(II) ions into a metallic film.

The self-reduction of metallic copper salt inks has been reported to occur at temperatures around 200°C (though some work report heating the ink patterns as high as 350°C in reductive and inert atmosphere). The high energy light pulses can be used to control the chemical transformation of copper (II) salt into metallic copper.

The IPL device has a light emission spectrum in the UVvis-IR range as shown in Fig. 3. When the sample is exposed to the pulse light, complex physical process can induce thermal decomposition of the copper MOD ink. IPL sintering of nanoparticle inks has been described by many authors but far less papers deal with IPL transformation of MOD inks. From Rosen's work [START_REF] Rosen | Self-reducing copper precursor inks and photonic additive yield conductive patterns under intense pulsed light[END_REF] we know that decomposition of MOD ink with photonic curing occurs in two steps. First the copper ions absorb light (600 -800 nm range) causing the excitation of electrons and local heating, Afterwards the rise in energy is sufficient to enable the decomposition of the copper formate to copper, carbon dioxide and hydrogen along with a reduction of the copper ions to pure copper.

Transformation seems to be dependent to the absorbance properties of the inks. Transmission spectrum of the ink recorded was recorded as in [START_REF] Puspitosar | Modification of an FTIR spectrometer for optoelectronic characterizations[END_REF] and the absorption deduced as shown in Fig. 4. The reduction of the copper salt also depends on the light pulse characteristics. Unfortunately there is no data or practical examples on the optimal IPL parameters for the decomposition of copper MOD ink and the resulting morphology and conductivity. Many tunings had to be chosen for the Xenon S2200.

Fig. 5 shows the major parameters: number of pulses, energy per pulse, duration of the light pulse t p , duration of the cooling off t d . Too high a value of the power led to ablation and burning of the substrate and not enough power to partial transformation. Three transient curing conditions have been established by Schroder [START_REF] Schroder | Broadcast photonic curing of metallic nanoparticle films[END_REF][START_REF] Schroder | Mechanisms of photonic curing: processing high temperature films on low temperature substrates[END_REF]. Considering a film of thickness x f with thermal equilibration time τ f siting on a substrate with thickness e s and equilibration time τs these conditions can be written as:

-

x f << es , the thickness of the film must be much smaller than that of the substrate so that the substrate acts as a thermal mass cooling the film.

t p <<τ s , the film heating is completed before the substrate can heat up.

τ f <<t p , τ f is affected by the film porosity and remaining solvents. The energy sent to the sample was calibrated with a dedicated Nova II P/N 7201550 laser power and energy meter fitted with a L50(150)A probe with a 50mm diameter. For clarity and for the purpose of comparisons with literature, Table I gives the values of the energy as programmed on the Xenon S2200 (in Joule) and the value of the calibrated energy in J/cm 2 .

A few examples of the settings we used are given here. Sheet resistance values (Rs) were measured at several locations on the inkjet printed patterns submitted to various IPL treatments. Burned samples and incompletely transformed samples were discarded. Fig. 7 shows a statistical plot of the values obtained.

The first observation is that films with various conductivity properties were obtained. The mean values here range from 24 Ω/□ to 5552 Ω/□ and the standard deviations range from 9.6 Ω/□ to 12 553 Ω/□. Conductive films were successfully obtained, the lowest value of Rs being bellow 10 Ω/□. The second observation is that changing the energy, the number and duration of the pulses has an effect on the conductivity. For an assessment it is easier to look at Fig. 8 where the mean values of Rs are plotted for given energy values and the corresponding power density values.. The minimum mean sheet resistance value is obtained for lowest energy and for the maximum power density of 2.7 kW/cm 2 . For higher energies and lower power Rs values are higher. Also for the highest energy (70.14 J/cm 2 ) Rs values are very high and scattered. This is in agreement with authors reporting that curing depends on power density. Note that with the 4 point probe it was impossible to measure many samples, either because they were totally insulating or because proper contacts could not be established for all four probes due to the inhomogeneity of the film topography. Fig. 9 shows optical images of the 10 mmx 10mm samples with IPL curing conditions (a) and (d). Higher magnification images are also shown.

CP AFM 2)

We have then measured the electrical properties of the films at a more local scale with the CP AFM technique. In these experiments the conducting tip was pressed on the film with a low load (150 nN for a probe with a radius of 150 nm) and the connection to the measuring device was established by silver paste at a distance of about 5 mm. Note that the substrate is PET and totally insulating so that conduction occurs in the plane of the ink layer. Topographic and electrical images are shown here for the best (IPL (a)) and the worse (IPL (d)) above treatments. Fig. 10 shows the typical aspect of the surface when scanning on different locations of the sample treated with condition (a). The pink regions on the electrical image correspond to tip/surface resistances higher than 10 12 Ω. This is due to a carbon contamination film thicker than a nanometer. (see XPS analyses below). Surprisingly despite this carbon film conductive islands are measured (from a few 10 3 to 10 5 Ω) which indicate that a connecting path exists to the collecting electrode 5 mm away. The profiles of Fig. 11, extracted from the images of Fig. 10 show a correlation between emerging particles and conduction. This is a major indication that copper particles were formed during the IPL treatment and build a 3D network. x:µm z: nm

x:µm z: log(R)

x:µm z: nm

x:µm z: log(R) a) b) A)
B)

The topography of the second location is much more disturbed with peak to peak height over 1.5 µm. Also the surface exhibits spots with more various values of conductivity. Some spots have the lowest resistance measurable (in the range 10 3 to 10 4 Ω which is the tip resistance value) and some show intermediate values (≈10 7 Ω). Composition analyses 3) XPS analyses were performed on some of the IPL sintered samples in order to investigate the film uppermost composition. The main element on the surface is carbon (see TABLE II). It comes from the atmosphere contamination but also from by-products formed during the exposure to the flash light and a possible incomplete decomposition of the copper formate AMP complex. This is visible on the magnified optical images of Fig. 9. A copper signal was nonetheless measured and the 2p 3/2 and 1/2 peaks are shown in Fig. 16 a). The well-known shake-up satellite found indicating Cu(II) species is absent, attesting of the reduction of the copper formate ions. This is in good agreement with the macroscopic conductivity of the films (low sheet resistance) and the highly conducting spots seen on the electrical AFM images.

A proposed decomposition for the Cu 2p 3/2 peak is shown in Fig. 16 b). After correction for the charge (positioning the main C1s peak at 284.6 eV) the peaks binding energies are at 932.7 eV and 933.7 eV. These can be assigned to metallic copper or Cu 2 O for the first one and to Cu(OH) 2 for the second one. The corrected Auger parameter [START_REF] Wagner | The Auger parameter, its utility and advantages: a review[END_REF] which can be expressed as α'=E k (AES)+E B (ph), is the sum of the binding energy of a pic and the kinetic energy of the corresponding transition; it usually helps to determine the oxidation state of copper Cu.

Here the Cu Auger L 3 M 45 M 45 (not shown here) seems to be display extra structures [START_REF] Antonides | LMM Auger spectra of Cu, Zn,and Ge. II, Relationship with the L 23 photoelectron spectra via the L2L3M45 Coster-Kronig process[END_REF] deserving further investigations. The oxygen 1s peak Fig. 16c) has a component at 531.9 eV corresponding to O=C and a component at 533.5 eV and the carbon C1s peak (not shown here) indicates the presence of O-C=O. These two peaks do not correspond to the Cuf-AMP spectra and could correspond either to underlying PET and/or by-products formed during the IPL sintering exposure.

Binding Energy

used for the measurement. When increasing the current above a certain value, the increase of resistance gave a voltage value higher than the compliance of the Keithley; the sample was destroyed. Further runs on other equivalent samples were then performed with the simultaneous recording of the surface temperature with a thermal camera. A first run at low current attested of the good connection. During the second run the current was increased in steps of 10 mA (for 4 min) up to 100 mA with simultaneous measurements of I, V and the surface temperature. Fig. 17 shows good linearity, with R varying from 15.2 Ω to 15.8 Ω while. Fig. 18 plots the temperature integrated over a large central area of the sample. It shows that the surface temperature increases with the current but stabilizes during the current steps. The thermal images presented in Fig. 19 show that the surface temperature is rather constant over the whole pattern indicating a rather homogeneous film despite the presence of a possible byproduct matrix.

During the third run with the same sample, varying I from 80 mA to 150 mA, an increase in the resistance value was recorded at 140 mA. In the fourth run the current intensity was increased higher up to a value of 160 mA. The temperature evolution can be observed to stabilize during the current steps up to the same value of 140 mA (Fig. 20). Fig. 21 shows the temperature evolution during the 150 mA plateau. After applying the current for 2 min the temperature rises a first time and then a second time a few seconds later. Because of the local changes occurring, the temperature was integrated on the large central zone (labelled 2) but also on two smaller ones referred to as zones 6 and 7, the positions of which are indicated on the thermal images. These images Fig. 23 A) and B) show the inhomogeneity of temperature on the printed pattern. Some zones do not radiate any longer and some become very intense. The temperature rise since the beginning of the current step can be evaluated locally at 20°C on zone7. IV. CONCLUSION This work has shown that it is possible to directly print by inkjet process copper MOD ink patterns that can be sintered by intense pulse light to produce conductive patterns. This sintering process performs in air contrary to many of the reported work. At this point the conductivity levels (sheet resistance around 10Ω/□) can allow the process to be involved in low level devices or circuits. The demonstration that copper formate ions are reduced in metallic copper indicates that more work on the IPL sintering conditions could increase the conductivity. In a further step the patterns are now printed on an industrial printer able to work in sheet to sheet mode or roll to roll (Kelenn KSCAN-PE).

Figure 1 .

 1 Figure 1. Cu formate AMP (Cuf-AMP) complex dissolved in solvents; visualisation of printed drops incompletely transformed by IPL sintering.

Figure 2 .

 2 Figure 2. Ink jet -printed patterns of Cu formate MOD ink with increasing number of passes; a) 1 pass, b) 3 passes and c) 5 passes.

Figure 3 .

 3 Figure 3. Emission spectrum of the Xenon S2200.

Figure 4 .

 4 Figure 4. Absorption spectrum of a Cuf AMP ink printed on PET.

Figure 5 .

 5 Figure 5. IPL waveform showing the pulses: number duration and time off.

Fig. 6

 6 Fig.6shows a printed pattern before and after the IPL sintering of the copper MOD ink.

Figure 6 .

 6 Figure 6. Printed Cuf MOD before and after an IPL exposure. The blue color is indicative of Cu(II) ions and the brownish one of transformed films.

Figure 7 .

 7 Figure 7. Statistical plot of Rs (Ω/□) sheet resistance measurements on printed patterns after various IPL sintering conditions.

Figure 8 .

 8 Figure 8. Values of sheet resistance Rs at given power density and programmed energy conditions.

2 )Figure 9 .

 29 Figure 9. Optical images A) sample with IPL (a) and B) sample with IPL (d). The arrow on the left images is 10mm; the right images are higher magnifications.

Figure 10 .

 10 Figure 10. CP AFM images of copper MOD ink with IPL treatment (a)

Figure 11 .

 11 Figure 11. Height profil a) and electrical profile b) extracted from Fig.10. The scans of sample IPL (d) were very different. Two different locations of the sample are shown in Fig. 12 and 14 with typical profiles in Fig. 13 and 15.

Figure 12 .

 12 Figure 12. CP AFM images of copper MOD ink with IPL treatment (d) location 1.

Figure 13 .

 13 Figure 13. Height profil a) and electrical profil b) extracted from Fig.12.

Figure 14 .

 14 Figure 14. CP AFM images of copper MOD ink with IPL treatment (d), location 2.

Figure 15 .

 15 Figure 15. Height profile a) and electrical profile b) extracted from Fig.14.

Figure 16 .

 16 Figure 16. XPS spectra of IPL treated copper MOD printed pattern.. a) Cu 2p 3/2 and Cu 2p ½, b) Cu 2p 3/2 with decomposition, c) O1s with decomposition

Fig. 22

 22 Fig.22shows the 160 mA plateau; after a few seconds (14 s) the compliance of the Keithley being reached, the sample does not conduct anymore. The thermal images of Fig.23 C) and D) show very bright spots (with an estimated surface temperature of 120°C) and the rapid cooling down.

Figure 17 .

 17 Figure 17. Voltage current curves for IPL sintered Cuf MOD pattern.

Figure 18 .Figure 20 .

 1820 Figure 18. Evolution of the surface temperature (in black) with time at increasing levels of I (in red); run2. Frame A (954 s) and frame B) 2173 s)

Figure 21 .

 21 Figure 21. Evolution of the surface temperature during the during step I=150 mA integrated on area 2, 6 and 7 as shown on the thermal images of Fig.23; A (after 2055 s) and in B (after 2108 s).

Figure 22 .

 22 Figure 22. Evolution of the surface temperature during the step at 160 mA integrated on the same areas as in Fig. 21.

Figure 23 .

 23 Figure 23. Thermal images showing the temperature evolution during V(I) measurments of run 4, Fig.21. A) frame at 2055 s and B) frame at 2108 s. both for 150 mA. C) frame at 2150 s and D) frame at 2155 s just before breakdown both at I=160 mA.

TABLE I

 I

			.	IPL SETTINGS
		Sample IPL	(a)	(b)	(c)	(d)
		E* (total) (J)	3600	4100	5100	10300
	E (measured)
		(J/cm 2 )	24.5	27.9	34.7	70.14
		Pulse
		duration tp	1.8	1.2	1.5	0.6
		(ms)
		Time off
		duration toff	300	200	200	100
		(ms)
		n pulses	5	10	10	50
		Total light
		exposure	9	12	15	30
		(ms)
	Power density
		(kW/cm 2 )	2.72	2.32	2.31	2.33
		E* total as set on the Xenon S2200
	C. Characterizations of the sintered printed films
	1)	Sheet resistance

TABLE II .

 II COMPOSITION OF THE PRINTED FILM OF FIG.10.

	%at	C	O	Cu	N
		81.3	17.7	0.6	0.4

ACKNOWLEDGMENT

This work was partly financed by ANR project Photonin'. The authors wish to thank C. Longeaud for the transmission measurements and D. Alamarguy for the XPS measurements with the Versaprobe surface analysis system acquired with fundings by: Region Ile-de-France, Agence Nationale de la Recherche, Centre National de la Recherche Scientifique.