Manon Ducrettet

Pierre-Michel Forget

Juan Sebastian Ulloa

Benjamin Yguel

Philippe Gaucher

Karine Princé

Sylvain Haupert

Jérôme Sueur
email: sueur@mnhn.fr

Acoustic monitoring of the White-throated Toucan (Ramphastos tucanus) in disturbed tropical landscapes

Keywords: biodiversity monitoring, tropical forest, ecoacoustics, human activity

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Biodiversity monitoring is a key element of conservation as it provides essential information about species occurrence, population dynamics, community composition and ecosystem structures [START_REF] Foden | Indicators of Climate Change Impacts on Biodiversity[END_REF]. Monitoring can be done using several techniques including traditional counting [START_REF] Turner | Sensing biodiversity[END_REF], camera trapping [START_REF] Steenweg | Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors[END_REF], remote sensing [START_REF] Turner | Remote sensing for biodiversity science and conservation[END_REF], and environmental DNA [START_REF] Thomsen | Environmental DNA -An emerging tool in conservation for monitoring past and present biodiversity[END_REF]. During the last decade, passive acoustic sensors have opened new paths for biodiversity monitoring in both underwater [START_REF] Buscaino | Temporal patterns in the soundscape of the shallow waters of a Mediterranean marine protected area[END_REF][START_REF] Caruso | Long-Term Monitoring of Dolphin Biosonar Activity in Deep Pelagic Waters of the Mediterranean Sea[END_REF][START_REF] Parks | Assessing marine ecosystem acoustic diversity across ocean basins[END_REF][START_REF] Yack | Passive acoustic monitoring using a towed hydrophone array results in identification of a previously unknown beaked whale habitat[END_REF] and terrestrial habitats [START_REF] Aide | Real-time bioacoustics monitoring and automated species identification[END_REF][START_REF] Buxton | Acoustic indices as rapid indicators of avian diversity in different land-use types in an Indian biodiversity hotspot[END_REF][START_REF] Fristrup | Bioacoustical monitoring in terrestrial environments[END_REF][START_REF] Sugai | Terrestrial Passive Acoustic Monitoring: Review and Perspectives[END_REF]. Ecoacoustics seeks to track species changes over large areas and over long time periods using standardized protocols based on unattended and non-invasive recording techniques [START_REF] Sueur | Ecoacoustics: the Ecological Investigation and Interpretation of Environmental Sound[END_REF]. The success of ecoacoustics is particularly salient for monitoring tropical forests, giving the opportunity to study animal diversity [START_REF] Deichmann | It's time to listen: there is much to be learned from the sounds of tropical ecosystems[END_REF], landscape use [START_REF] Burivalova | Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea: Tropical-Forest Soundscapes[END_REF], biological invasion [START_REF] Gasc | Cricket calling communities as an indicator of the invasive ant Wasmannia auropunctata in an insular biodiversity hotspot[END_REF], mining [START_REF] Duarte | The impact of noise from open-cast mining on Atlantic forest biophony[END_REF], gas exploitation [START_REF] Deichmann | Soundscape analysis and acoustic monitoring document impacts of natural gas exploration on biodiversity in a tropical forest[END_REF], illegal hunting [START_REF] Astaras | Passive acoustic monitoring as a law enforcement tool for Afrotropical rainforests[END_REF], and logging [START_REF] Sharma | Acoustic Signal Classification for Deforestation Monitoring: Tree Cutting Problem[END_REF].

However, ecoacoustics has not yet been employed to study the maintenance of tropical forests functions which may depend mainly on plant-animal interactions [START_REF] Corlett | Tropical Rain Forests: An Ecological and Biogeographical Comparison: Second edition, Tropical Rain Forests: An Ecological and Biogeographical Comparison: Second edition[END_REF][START_REF] Redford | The Empty Forest[END_REF][START_REF] Wright | The myriad consequences of hunting for vertebrates and plants in tropical forests[END_REF]. Animals compensate for plant immobility by transporting plant propagules, gametes and seeds, partly ensuring plant dispersion. In return, plants provide nutritional resources to animals through fruit pulp, pollen or nectar [START_REF] Wunderle | The role of animal seed dispersal in accelerating native forest regeneration on degraded tropical lands[END_REF]. The structure and resilience of tropical forests is thought to be therefore tightly linked to animal movements and feeding behavior [START_REF] Holbrook | Using toucan-generated dispersal models to estimate seed dispersal in Amazonian Ecuador[END_REF]. Population growth impacts tropical regions where 50 % of human beings are projected to live by 2050 [START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF]. Human activities have an obvious effect on tropical rainforests through wood exploitation and also a significant impact on animal dispersers through habitat fragmentation, illegal trade, and defaunation [START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF][START_REF] Dirzo | Defaunation in the Anthropocene[END_REF][START_REF] Laurance | Impacts of roads and linear clearings on tropical forests[END_REF][START_REF] Yguel | Beyond species richness and biomass: Impact of selective logging and silvicultural treatments on the functional composition of a neotropical forest[END_REF]. There is therefore a need to better monitor the populations of seed dispersers which may play a major role in tropical rainforest dynamics, and whose presence is a valuable indicator of forest status [START_REF] Boissier | Modifications of the rainforest frugivore community are associated with reduced seed removal at the community level[END_REF][START_REF] Boissier | Rapid Assessment of Seed Removal and Frugivore Activity as a Tool for Monitoring the Health Status of Tropical Forests[END_REF][START_REF] Lewis | Increasing human dominance of tropical forests[END_REF].

Monitoring canopy seed dispersers is a challenging task due to the difficulty of accessing to the canopy [START_REF] Erwin | FOREST CANOPIES, ANIMAL DIVERSITY[END_REF][START_REF] Kays | Arboreal tropical forest vertebrates: current knowledge and research trends[END_REF]. Ecoacoustic techniques offer new solutions to monitor wildlife in large areas providing information about species behaviour and ecology while avoiding tree climbing or the construction of canopy access infrastructures [START_REF] Shonfield | Autonomous recording units in avian ecological research: current use and future applications[END_REF]. Here we took advantage of these emerging techniques to build a continuous survey of a major canopy seed disperser, the toucans [START_REF] Galetti | Frugivory by Toucans (Ramphastidae) at Two Altitudes in the Atlantic Forest of Brazil1[END_REF][START_REF] Holbrook | Home Range and Movement Patterns of Toucans: Implications for Seed Dispersal: Toucan Movement Patterns[END_REF]Loiselle, 2009, 2007). These charismatic birds, which represent flagship species for tropical forests, are widely distributed across Central and South America. However, anthropogenic disturbances are quickly increasing in this region including expanding energy infrastructure and road networks, exacerbated by illegal activities [START_REF] Barber | Roads, deforestation, and the mitigating effect of protected areas in the Amazon[END_REF]Laurance et al., 2001).

Toucans are categorized as vulnerable due to hunting, illegal trade and habitat modification (IUCN, 2016). Toucans are generalist frugivores that roost in tree cavities [START_REF] Galetti | Frugivory by Toucans (Ramphastidae) at Two Altitudes in the Atlantic Forest of Brazil1[END_REF][START_REF] Short | Toucans, barbets, and honeyguides: Ramphastidae, Capitonidae, and Indicatoridae[END_REF] and disperse seeds by moving in various types of forest habitats depending on fruit resources availability [START_REF] Holbrook | Home Range and Movement Patterns of Toucans: Implications for Seed Dispersal: Toucan Movement Patterns[END_REF][START_REF] Short | Toucans, barbets, and honeyguides: Ramphastidae, Capitonidae, and Indicatoridae[END_REF]. Despite these key roles as flagship species and seed dispersers, the population dynamics of toucans are poorly known, probably due to the difficulty to monitor them on the long term.

French Guiana, which contains important continuous tracts of neotropical forest, is currently facing a rise in human activity including hunting, as well as urban area and road expansion [START_REF] De Thoisy | Hunting in northern French Guiana and its impact on primate communities[END_REF][START_REF] Richard-Hansen | Hunting in French Guiana Across Time, Space and Livelihoods[END_REF]. Roads and human disturbances such as illegal mining, logging and hunting are intertwined as roads facilitate access to the forest [START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF][START_REF] Campbell | Roads to ruin[END_REF][START_REF] Coffin | From roadkill to road ecology: A review of the ecological effects of roads[END_REF][START_REF] De Thoisy | Hunting in northern French Guiana and its impact on primate communities[END_REF][START_REF] Robinson | Measuring the sustainability of hunting in tropical forests[END_REF]. These human activities are thought to increase forest fragmentation and simplify the access by hunters to seed disperser populations.

We aimed at testing the effect of habitat type and human activity on the vocal activity of the White-throated toucan, Ramphastos tucanus, as well as considering the time of the day and rainfall which are known to mediate tropical bird activity. In particular, we hypothesized that logging would decrease toucan vocal activity. To test this prediction, we deployed an acoustic monitoring system in the southeastern part of French Guiana where the road network has been recently expanded to directly connect French Guiana and Brazil.

Material and methods

Study site

The study was conducted in a lowland forest of the Guiana Shield, a key area for neotropical forest conservation due to a high level of diversity and endemism [START_REF] De Thoisy | Rapid evaluation of threats to biodiversity: human footprint score and large vertebrate species responses in French Guiana[END_REF][START_REF] De Thoisy | Hunting in northern French Guiana and its impact on primate communities[END_REF]. The climate is humid equatorial with an alternation of a rainy season with two dry seasons: the first dry season from February to March, and the second dry season from September to October. The study area has a mean annual precipitation rate of 3,364 mm and a mean annual temperature of 27°C. The site is an old-growth mature forest along the Route Nationale 2 (RN2) that connects the urban agglomeration of the Ile-de-Cayenne or Cayenne Peninsula (109,358 inhabitants in 2014) to the small city of Saint-Georges-de-l'Oyapock (4,020 inhabitants in 2015), and to the middle size Brazilian city of Oiapoque (25,514 inhabitants in 2017) through a bridge over the river Oyapock (Fig. 1). To favor road crossing by wildlife and to maintain connectivity, 13 forested corridors acting as overpasses for canopy animals have been established over the road by local authorities. These corridors mainly consist of keeping the canopy intact above the road.

Study species

Most bird species, in particular tropical species, communicate with sound in different contexts such as territory defense, courtship display, group cohesion or parent-offspring interactions [START_REF] Mathevon | Singing in the Rain Forest: How a Tropical Bird Song Transfers Information[END_REF]. Ramphastos tucanus produces sound when moving in groups maintaining contact between individuals. The vocalizations are loud, highly repeated, and stereotyped, all conditions necessary for developing an automatic recognition by template matching [START_REF] Bardeli | Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring[END_REF][START_REF] Ulloa | Screening large audio datasets to determine the time and space distribution of Screaming Piha birds in a tropical forest[END_REF]. Preliminary analyses revealed that the vocalizations of R. tucanus are made of two notes, a first note ranging from 1.4 kHz to 1.8 kHz and a second longer note ranging from 1.3 kHz to 1.5 kHz, leading to a call type "keeowyelp-yelp" (Fig. 2).

Acoustic survey

We conducted an acoustic survey to estimate the absence/presence of R. tucanus vocalizations near 9 of the 13 corridors (hereafter named A-I) along the Route Nationale 2 (RN2), so that the forest was regularly sampled along 43 km of the road with independent sampling units (Fig. 1). For each sampled forest corridor, we installed one automatic recording unit, Song Meter 4 (Wildlife Acoustics, USA), on a tree trunk near a Virola tree (V. kwatae, V. michelii, and V. surinamensis, Myristicaceae), known to be visited by R. tucanus. We oriented the integrated microphones to minimize the noise from the road. We placed each recorder on the tree trunk at 2 m above the ground. The distance to the road was between 200 and 300 meters avoiding a potential edge effect of the road. We set up the recorders to record 1 min every 10 min (1 min of recording, 9 min of pause) for 24 hours/days with a sampling frequency of 44.1 kHz, a digitization depth of 16 bit and a gain of +16dB. We used internal microphones that have a flat frequency response ± 6 dB between 0.06 and 20 kHz and a full flat frequency response between 1.4 and 1.8 kHz corresponding to the frequency range of R. tucanus. The sampling period covered one month from 2017-11-26 to 2017-12-24 (29 days), at the beginning of the rainy season. This sampling generated an audio dataset of 37,584 files. We could not estimate the space covered by each recorder due to the intricate structure of the forest and a high diversity of sound sources. However, a sound propagation law model for similar habitat estimated a recording radius of 120 m for a sound source of 80 dB SPL (unpublished results). This corroborates previous experiments indicating a radius of 100 m [START_REF] Rodriguez | Temporal and spatial variability of animal sound within a neotropical forest[END_REF][START_REF] Ulloa | Screening large audio datasets to determine the time and space distribution of Screaming Piha birds in a tropical forest[END_REF]. We therefore estimated that each recorder monitored an area of ca. 3 ha, i.e. a circle with a radius of approximately 98 m.

Automatic acoustic identification

Datasets

In order to build, tune and evaluate the detection system we selected from the full dataset the recordings from the day period, from 5 am to 8 pm, and randomly sampled 900 files. This sample was manually annotated with presence/absence of toucan vocalizations and included different weather conditions. These files were split into a training set and a validation set. The training set included 451 files of which 37 had presence of toucan vocalizations, and the validation set included 449 files of which 37 had presence of toucan vocalizations. All acoustic analyses were performed with seewave [START_REF] Sueur | Seewave, a free modular for sound analysis and synthesis[END_REF] and monitoR (Katz et al., 2016a(Katz et al., , 2016b) R packages. Classification was assessed by measuring the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) with the R package pROC [START_REF] Robin | pROC: an open-source package for R and S+ to analyze and compare ROC curves[END_REF].

Template selection

The presence of toucans was detected through a template matching process. Templates should be representative of the sound of interest, here toucan vocalizations, and of the acoustic environment, so it is recommended to select templates from local recordings. This local selection helps to maximize positive identification and minimize false identification due to background noise and/or the vocalizations of other species occupying the same bandwidth as the sound of interest (Katz et al., 2016a). Here, we selected the best toucan vocalizations (n = 33) to extract relative invariant temporal and spectral features of the call (Fig. 2). We applied a finite impulse response (FIR) band-pass frequency filter between 1 kHz and 2 kHz so that we focused on the fundamental frequency that was the most conserved element [START_REF] Priyadarshani | Automated birdsong recognition in complex acoustic environments: a review[END_REF]. We then tested the 33 templates on the training dataset following the same steps as described in section 2.4.4. The two templates with better classification performance combining the first ("keeow") and the second ("yelp") syllables returned an AUC of 0.9184 and were selected for further analysis (Fig. A1).

Spectrogram cross-correlation

The automatic detection consisted of a comparison of the spectral and temporal features of the template with the recording at different time lags through a non-overlapping moving window.

The template recording comparison was achieved with a cross-correlation at each time lag Ƭ.

The process was based on a short-term Fourier transform (STFT) between the template and the recording. A previous analysis showed that a Fourier transform (FT) of 512 samples tapered with a Hanning window returned the most appropriate results [START_REF] Ulloa | Screening large audio datasets to determine the time and space distribution of Screaming Piha birds in a tropical forest[END_REF]. The index of similarity R xy , or correlation coefficient was obtained with the following equation for two time-series x(t) and y(t) of 1 min recording [START_REF] Sueur | Sound Analysis and Synthesis with R, Use R![END_REF]:

R xy [Ƭ ]= ∑ t =1 N -T x [t ] y [t+T ] ( ∑ t=1 N -T x 2 [t ] ∑ t=1 N -T y 2 [t ]) 1 2
where R xy [Ƭ] is a series of correlation coefficients along time for each value of Ƭ.

The higher the score, the higher the similarity between template and audio, and hence a higher probability that a toucan vocalization is occurring in the recording. The score is then translated into binary information in reference to a threshold. Values above (respectively below) the threshold indicate a positive (respectively negative) detection (Katz et al., 2016b).

As we were estimating the absence/presence of R. tucanus vocalizations in each recording, we did not search for all vocalizations occurring in each recording but we only considered the most salient one, that is the vocalization that returned the highest correlation with the templates.

Evaluation of the automatic detection system

We tuned the automatic detection system on the training dataset by matching the ground truth dataset (manual classification) with the predictions (automatic classification). We obtained a confusion matrix with four categories of detections: true positive (TP), false negative (FN), false positive (FP) and true negative (TN). The metrics used were the true positive rate (TPR = TP/(TP+FN)) and the false positive rate (FPR = FP /(FP+TN)). The automatic detection system and the resulting binary classification involve a decision on a reference threshold on which the relative frequencies of the different categories depend so that the threshold adjusts the global error of the automatic system. Setting a low threshold increases the number of TP and FP detections and decreases the number of FN. Here, we chose a threshold of 0.54 to get the best compromise between TPR and FPR [START_REF] Priyadarshani | Automated birdsong recognition in complex acoustic environments: a review[END_REF]. This high value limits FP detections due to the occurrence of a complex interfering background made by vocalizations of other species. Most FP detections were due to the Great tinamou (Tinamus major), and the Screaming Piha (Lipaugus vociferans). FN detections were in most cases soft vocalizations of distant R. tucanus or vocalizations of close individuals but blurred by noise resulting from rainfall.

The tuned automatic system was validated on the validation dataset with the chosen threshold of 0.54. The automatic system correctly classified 15 toucan vocalizations (TP), missed 18 (FN), misclassified 22 as toucan vocalizations (FP) and correctly classified 386 recordings as vocalizations produced by other sound sources (TN). The automatic system therefore detected 45 % of the labeled vocalizations, with an FPR of 5 %. This implied a rather low true detection rate but minimized misclassification and ensured to detect only close by vocalizations.

Environmental data

We used a GIS analysis with QGIS desktop 2.18.17 and GRASS 7.4.0 to assess abiotic and biotic factors in the context of local human activity [START_REF] Morrison | Wildlife-habitat relationships: concepts and applications[END_REF]. We delimited a buffer zone of 64.5 ha similarly around each recorder. This larger zone corresponded to the median home range area of R. tucanus [START_REF] Holbrook | Home Range and Movement Patterns of Toucans: Implications for Seed Dispersal: Toucan Movement Patterns[END_REF]. We estimated three environmental variables inside the 64.5 ha area: (1) habitat type, (2) human activity, and (3) rainfall.

Habitat type was defined according to the geomorphodiversity and forest composition that appear as the most important factors in shaping the forest of French Guiana [START_REF] Guitet | Geomorphological landscape features explain floristic patterns in French Guiana rainforest[END_REF]. There were three types of forests: plateau forests, transitional forests along creeks and small ravines, and riparian forests. Plateau forests were well drained forests mainly composed of Burseraceae such as Protium spp, Fabaceae, Lecythidaceae and Mimosaceae. Transitional forests were relatively humid areas where vegetation composition was similar to both riparian forests and plateau forests. Riparian forests were found at a low elevation with waterlogged soil with, palm trees (Euterpe oleracea), Virola surinamensis (Myristicaceae) and Eperua falcata (Fabaceae). The three types of forest provide important resources to toucans as they all include trees with fruits. More specifically Virola spp provide early fresh fruits with a fructification period from September to March [START_REF] Mendoza | Inter-annual variability of fruit timing and quantity at Nouragues (French Guiana): insights from hierarchical Bayesian analyses[END_REF][START_REF] Ratiarison | The Role of Frugivores in Determining Seed Removal and Dispersal in the Neotropical Nutmeg[END_REF]. Furthermore, palm trees such as Euterpe spp. and Protium spp. were also a key alternative food resource during the dry season when fruits are scarce [START_REF] Thiollay | Effects of hunting on guianan forest game birds[END_REF]. We encoded the forest habitat types by creating an empirical classification of two habitat types ( 1and 2) based on the percentage of forest types (Table A1). The differences in forest types were numerically small (about 5%) but still meaningful as such forest types indicate different water and food resources that are particularly important for toucans during the dry season.

Human activity, either positive or negative, came from the data provided by government offices (Office National des Forêts; Direction de l'Environnement, de l'Aménagement et du Logement). Human activity variable integrated protected areas (i.e. with a low hunting pressure), agriculture and logging intensity encoded with different exploitation rates (medium, high), so that the variable included four levels (protected -corridors E,F; agriculturecorridor A; medium logging -corridors B, G, H, I; high logging -corridors C,D).

Rainfall data were obtained from the meteorological station of Saint-Georges-del'Oyapock (3.88°N, 51.80°W, 6 m a.s.l.). The data were downloaded as rainfall level in mm per a six min period and then summed up for each hour.

Statistical analysis

To investigate the effects of habitat type and human activity considering rainfall and day time on toucan vocal activity, we used a Generalized Linear Mixed Model (GLMM) assuming a Poisson error distribution and a log-link function to model the number of detections [START_REF] Bolker | Generalized linear mixed models: a practical guide for ecology and evolution[END_REF]. The response variable was the number of recordings in which vocalizations were detected per hour. Habitat type and human activity were categorical variables that were included as fixed effects, as well as rainfall and hour that may affect acoustic activity. To account for data non-independence according to both space and time, we included sites and day as crossed random effects. The full model was fitted using the function glmmTMB() from the R package glmmTMB (Brooks et al., 2017a).

Given the nature of the response variable (count), we tested for potential overdispersion in the model residuals [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. The dispersion statistic of the Poisson GLMM fitted to the data showed that the model was overdispersed. Hence, we fitted the next model in complexity with a negative binomial GLMM [START_REF] Lindén | Using the negative binomial distribution to model overdispersion in ecological count data[END_REF]). Since we did not have information about the relation between the variance and the mean, we fitted a first full model with a negative binomial of type I (that allows the variance to increase linearly with the mean), and another full model with a negative binomial error distribution of type II (that models a quadratic relation between the variance and the mean) (Brooks et al., 2017b).

The model with a negative binomial error distribution of type I was found the most parsimonious with the lowest Akaike's criterion (AIC) value (i.e. dAIC<2; [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]).

Before performing our model, we examined the correlations among explanatory variables using the variance inflation factor (VIF), with the function vif() from the R package car [START_REF] Fox | An R Companion to Applied Regression[END_REF] applied to the standard linear model without random effects.

All variables had a VIF < 3 indicating no problem of multicollinearity [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. We also inspected spatial correlograms based on the model residuals [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF] in order to detect any sign of spatial autocorrelation, which we did not find.

We checked that our final model provided a good fit after checking the assumptions on residuals independence, homogeneity and normality using a quantile-quantile plot, and the simulate residuals with the R package DHARMa [START_REF] Hartig | DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models[END_REF][START_REF] Steel | Applied statistics in ecology: common pitfalls and simple solutions[END_REF]. The response variable included many zeros, due to failure of detection, absence of R. tucanus, or a decrease in vocal activity. A high number of zeros can lead to the zero-inflation issue and bias the parameters estimation [START_REF] Bolker | glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling[END_REF]. Therefore, we verified that the fitted model was not zero-inflated with the function testZeroInflation() from the R package DHARMa.

We extracted the estimates and interpreted the models with the Incidence Rate Ratios (IRR) which clearly illustrate the links between model prediction and explanatory variables [START_REF] Hilbe | Negative Binomial Regression[END_REF]. For the fixed effects, the predictor levels taken in reference were the habitat type "1", the human activity "agricultural area", and the hour "5 am".

The model did not include a temporal link between rain events and vocal activity of R.

tucanus. As such, to further evaluate a potential relationship between vocal activity and rainfall over time (i.e. throughout the survey period) we ran a Dynamic Time Warping (DTW)

analysis. This method minimizes the Euclidean distance between two paths or time series through local compression or stretching [START_REF] Giorgino | Computing and Visualizing Dynamic Time Warping Alignments in R : The dtw Package[END_REF]. Since we wanted to evaluate the alignment between the rainfall that is a quantity in millimeters and the number of vocalizations detected, quantitative variables were Z-transformed beforehand.

All the analyses were performed with R software version 3.4.4 (R Core Team, 2018).

Results

The automatic detection system based on the cross-correlation with two templates detected, between 5:00 am and 8:00 pm a total of 1,748 files with at least one vocalization over the 22,490 audio files.

Vocal activity according to time of the day

According to the model (Tab. 1), there was a significant hour effect in the number of detections from 5:00 am to 10:00 am and from 4:00 pm to 7:00 pm (p < 0.05) with a marginal effect (p = 0.059) at 3:00 pm. There was therefore a diurnal activity pattern with a dawn chorus, afternoon rest, and a dusk chorus (Figs. 34).

Vocal activity according to habitat and human activity

Habitat type did not show any significant effect on the number of detections. The human activity had a significant effect only in the area of high logging exploitation with the number of detections 1.57 times higher than the number of detections in the agricultural sites (Tab. 1).

Vocal activity according to rainfall

According to the mixed model developed (GLMM), rainfall quantity did not significantly influence the number of detections (Tab. 1). However, when comparing the daily rainfall event time series with the number of detections time series across all sites -at the forest scale -using the DTW method (Fig. 5), we found that the vocal activity is on average higher and more variable before the 11 th of December related to a low rainfall. The DTW alignment between the two-time series showed that there was a temporal link for four days between the number of detections and the rain events, so that the number of detections increased before heavy rain events (Fig. 5).

Discussion

Ecoacoustics was used to evaluate how time of the day, rainfall, habitat and human activity may influence the acoustic activity of the White-throated toucan (R. tucanus), a canopy flagship species for neotropical forest conservation.

The passive acoustic monitoring (PAM) system was based on an array of nine recorders recording continuously over 29 days, returning a total of 391 hours of data, equivalent to nearly 48 days of human work. The audio files were screened out using an automatic system already tested [START_REF] Ulloa | Screening large audio datasets to determine the time and space distribution of Screaming Piha birds in a tropical forest[END_REF] and was based on a limited expertise as it was only necessary to identify few toucan vocalizations, a skill that can be acquired very quickly by exploring the forest or, even more simply, by visiting online sound libraries. The system can be therefore considered as simple, efficient and accessible to non-experts. Several studies have shown that similar PAM systems can be as informative as point count sampling technique traditionally used in conservation programs [START_REF] Haselmayer | A Comparison of Point Counts and Sound Recording as Bird Survey Methods in Amazonian Southeast Peru[END_REF][START_REF] Shonfield | Autonomous recording units in avian ecological research: current use and future applications[END_REF][START_REF] Stevens | Use of autonomous audio recordings for the rapid inventory of birds in the white-sand forests of the Peruvian Amazon[END_REF].

Animal vocal activity is known to change during the night/day cycle corresponding with environmental factors and behaviors such as courtship or territorial defense [START_REF] Boscolo | Efficiency of playback for assessing the occurrence of five bird species in Brazilian Atlantic Forest fragments[END_REF][START_REF] Digby | Temporal and environmental influences on the vocal behaviour of a nocturnal bird[END_REF][START_REF] Goymann | A tropical bird can use the equatorial change in sunrise and sunset times to synchronize its circannual clock[END_REF]. These cyclic variations, or phenological patterns, are a key element of animal ecology [START_REF] Forrest | Toward a synthetic understanding of the role of phenology in ecology and evolution[END_REF].

Particularly, birds have a developed circadian system in link with biological function as singing [START_REF] Cassone | The bird of time: cognition and the avian biological clock[END_REF]. Here, the vocalizations detected over all sites clearly showed that the vocal activity of R. tucanus was not even along the day but irregular with major peaks of activity at 6:00 am and 06:00 pm around sunrise and sunset. This behavioral phenology fits with the usual, but not universal, chorus behavior of birds at dawn and at dusk for which a total of twelve ecological and evolutionary hypotheses have been put forward [START_REF] Burt | Dawn chorus as an interactive communication network[END_REF]. Knowing this time pattern is crucial in terms of sampling efficiency in monitoring programs. Further conservation studies dealing with population structure and dynamics should now focus on dawn and dusk time to monitor R. tucanus in order to optimize detections and to save by a factor of six (four hours of monitoring instead of 24 hours) battery and data storage, two important limiting factors of sampling based on digital techniques.

Weather parameters such as temperature, wind speed and barometric pressure play an important role in animal acoustic phenology. Rain may have a particularly direct detrimental effect on the bird vocal activity through masking due to the sound produced by raindrops striking the vegetation [START_REF] Lengagne | The effects of rain on acoustic communication: tawny owls have good reason for calling less in wet weather[END_REF]. Here, we could observe that rainfall, a major element of tropical climate, may have a positive lagged effect on the vocal activity of R. tucanus that appeared to increase before rain. This unexplained behavior was actually already reported by local people and field observations [START_REF] Short | Toucans, barbets, and honeyguides: Ramphastidae, Capitonidae, and Indicatoridae[END_REF]. The daily pattern and the rainfall effect should now be tested over a longer period including dry and rainy seasons in order to have more information about seasonality. This should also open the possibility to document the seasonal migrations of R. tucanus over the year from the inner to the coastal forest, and possibly over several years in relation to climate change and so to possible seasonal shifts.

In addition to temporal variation, we looked for spatial patterns related to habitat types and human activity. There was no effect of the habitat type nor of human activity on the calling activity of R. tucanus. This lack of significant difference of habitat types was probably due to the fact that the nine sites were relatively homogeneous in terms of habitat characteristics with no obvious differences in relative proportion of plateau, transition, and riparian forests. All habitat types probably provide the food resources necessary for R.

tucanus in particular with the occurrence of Virola trees in the plateau forest, and, alternatively, of Euterpe palm trees in the riparian forest. In addition, toucans are quite mobile so the road might not represent an obstacle to their movement across the forest. There was no significant effect of human activity on toucan vocal activity suggesting that the toucans were not impacted by the local tree exploitation, either along the road or further inside. Local forestry does not seem to affect toucans contrary to what was observed for other species found in sites where logging was shown to significantly decrease the number of singing individuals and to shift the vocal activity because of chainsaw and road traffic noise [START_REF] Slabbekoorn | Birdsong and anthropogenic noise: implications and applications for conservation[END_REF]. Unexpectedly, we found a significant higher number of vocalizations in the sites which were the most logged. However, logging was sparse with only a few trees logged per hectare following local regulation so that canopy opening was not different compared to natural forest dynamics with regular treefall regime (i.e. one gap per ha per year) (van der Meer & Bonger, 1996). Therefore logging did not affect canopy opening so that different sound propagation conditions that might have an effect on detection were unlikely.

This result could be explained by a competition between R. tucanus and the Black Spider monkey, Ateles paniscus. The Black Spider monkey is also an important fruit consumer and seed disperser of Virola spp. [START_REF] Forget | Dynamics of the seedling shadow of a frugivore-dispersed tree species in French Guiana[END_REF][START_REF] Ratiarison | The Role of Frugivores in Determining Seed Removal and Dispersal in the Neotropical Nutmeg[END_REF]. In the context of a logged forest, toucans have an easier access to the canopy than monkeys, so a decrease in A. paniscus populations may benefit R. tucanus [START_REF] Felton | The role of timber tree species in the nutritional ecology of spider monkeys in a certified logging concession, Bolivia[END_REF][START_REF] Holbrook | Home Range and Movement Patterns of Toucans: Implications for Seed Dispersal: Toucan Movement Patterns[END_REF].

Toucans are highly visible species often used as icons for tourism and commercial use in all sorts of representations [START_REF] Correia | Familiarity breeds content: assessing bird species popularity with culturomics[END_REF] but they are also highly hunted [START_REF] Thiollay | Effects of hunting on guianan forest game birds[END_REF]. Being both icon and game species, R. tucanus and its sister species of the Ramphastidae family, have a dual importance in terms of culture and conservation. Thus, their monitoring is crucial. [START_REF] Astaras | Passive acoustic monitoring as a law enforcement tool for Afrotropical rainforests[END_REF][START_REF] Boissier | Modifications of the rainforest frugivore community are associated with reduced seed removal at the community level[END_REF][START_REF] Hill | AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment[END_REF] Ecoacoustic monitoring based on only nine recorders settled at human height and combining here vocal activity detection and habitat description revealed key cues for canopy management since population viability and habitat are closely linked [START_REF] Morrison | Wildlife-habitat relationships: concepts and applications[END_REF].

Toucans are essentially sedentary but movements towards the coastal area have been reported.

Providing information using ecoacoustics about the occurrence of R. tucanus was the first necessary step towards a potential monitoring and conservation program over a long term.

However, the habitats offered by the coastal area are facing increasing anthropogenic pressures due to human population growth. The resident zone we monitored might then be the only refuge for toucans and potentially other seed dispersers underlying the importance of their monitoring and protection. Over the next years, the bridge connecting French Guiana to Brazil is expected to increase human activity along the road and become a more important driver of the defaunation process. . This could lead to defaunation resulting from an increase in bushmeat consumption or habitat destruction with urban and agriculture area expansion [START_REF] Young | Patterns, Causes, and Consequences of Anthropocene Defaunation[END_REF]. Ecoacoustic methods can gather data not only on a target species but also on all other species sharing the same habitat. The recordings collected here could then be used to investigate similar ecological questions on other canopy species such as other birds or primates. Table 1.

Figure 1 .

 1 Figure 1. Study area and recording sites. The study site is located in the southeast of French Guiana in South America (left). The nine recording sites (A-I) were along the road RN2 connecting the cities of Cayenne and Saint-Georges-de-l'Oyapock. These sites were close to forest corridors ensuring a connectivity of the forest above the road. Picture by Michel Giraud-Audine.

Figure 2 .

 2 Figure 2. Spectrogram of a recording including Rhamphastos tucanus vocalizations. This section of a one-minute recording indicates R. tucanus vocalizations (red rectangles) within the complexity of the forest soundscape. Recording made at site D on 25 November 2017 at 07h20. Spectrogram specifications: Hamming window made of 512 samples with an overlap of 50%, amplitude dynamic of 60 dB. Full recording available as a sound voucher sample #MNHN-SO-2019-91(https://sonotheque.mnhn.fr/sounds/MNHN/SO/2019-91).

Figure 3 .

 3 Figure 3. Time and space distribution of R. tucanus vocalizations detected. Heatmap indicating the number of recordings with R. tucanus vocalizations detected automatically. The x-axis refers to time from early morning to night fall, sunrise and sunset occurring at 6:00 and 18:00 respectively. The y-axis corresponds to the nine recording sites (A-I). The grey scale ranges from 3 to 135. The margin barplots were obtained by summing up the columns (time distribution or phenology) and the rows of the heatmap matrix (space distribution).

Figure 4 .

 4 Figure 4. Plot of the model estimates using Incidence Rate Ratios. The x-axis refers to the Incidence Rate Ratios (IRR) values. The y-axis refers to the variable used in the model. The grey line is the limit for an IRR value of 1 meaning no effect. Contrast group for Habitat is 'Habitat 1'; contrast group for Human activity is "agricultural area"; contrast group for Hour is Hour5 (i.e. 5 am). (***) for p-values < 0.001, (**) for p-values < 0.01, (*) for p-values < 0.05.

Figure 5 .

 5 Figure 5. Comparison of the number of recordings with R. tucanus vocalizations detected and rainfall. The times series of the number of R. tucanus vocalization and the rainfall were compared using a dynamic time warping (DTW) process. The numbers indicate the time relation between the number of detections and the major rain events. The x-axis indicates the days of recording from 2017-11-26 to 2017-12-24. The left y-axis relates to the number of recordings with R. tucanus vocalizations detected. The right y-axis relates to the rainfall. The data were Z-transformed. The grey dotted lines connect the two times series indicating the compression and stretching operated by the DTW algorithm to find the best alignment. The numbers indicates the major events. The two-time series are shifted vertically for clarity.

  Estimated coefficients from the Generalized Linear Mixed Model (GLMM) relating toucan vocal activity to habitat structure, human activity, rainfall and hour of the day. Contrast group for Habitat is 'Habitat 1'; contrast group for HA (Human activity) is "agricultural area"; contrast group for Hour is Hour5 (i.e. 5 am). (***) for P-values < 0.001, (**) for P-values < 0.01, (*) for P-values < 0.05.

	Predictor	Estimate	Std. error	z value	P-value
	(Intercept)	-1.89863	0.20886	-9.090	< 2e-16 ***
	Habitat 2	0.04336	0.06582	0.659	0.510013
	HA high exploitation	0.42230	0.11748	3.595	0.000325***
	HA medium exploitation	-0.04652	0.11313	-0.411	0.680934
	HA protected	0.13421	0.12080	1.111	0.266583
	Rainfall	-0.06845	0.05195	-1.318	0.187633
	Hour6	1.71756	0.16813	10.216	< 2e-16 ***
	Hour7	1.28803	0.17524	7.350	1.98e-13 ***
	Hour8	1.27940	0.17545	7.292	3.05e-13***
	Hour9	0.88047	0.18480	4.765	1.89e-06***
	Hour10	0.39868	0.20190	1.975	0.048313*
	Hour11	-0.10066	0.22518	-0.447	0.654855
	Hour12	-0.37074	0.24333	-1.524	0.127613


Emilio Jorquera-Fontena

Michel Génard
email: michel.genard@avignon.inra.fr

Alejandra Ribera-Fonseca
email: alejandra.ribera@ufrontera.cl

Nicolás Franck
email: nfranck@uchile.cl

Alejandra Ribera Fonseca

A simple allometric model for estimating blueberry fruit weight from diameter measurements

Keywords: Regression, validation, non-destructive methods

Monitoring of fruit growth is a measurement widely used in physiology and agronomy studies. This is normally done from detached fruits, which can lead to erroneous results when fruits grow asynchronously in clusters as occurs in blueberry plants (Vaccinium spp.). The aim of this communication is to develop a simple allometric model for estimating blueberry weight from diameter measurements. In three growth seasons, weight and diameter of a total of 416 fruits were measured from four field-grown blueberry cultivars. The obtained data set was then randomly divided resulting in 200 data for calibrating model and 216 for validating. Prior to calibration procedure, the use of cultivar-specific models or a common model for four cultivars was evaluated by Analysis of Covariance between logarithm transformed diameter and cultivar/season variables.

From analysis, no interactive effect was observed, so a common allometric model was constructed, giving a power functional relationship between berry diameter and weight. The allometric model was validated with the independent data, giving errors ranging from 4.7% to 6.9%. Residuals showed a random dispersion pattern. Our results showed that a common model was adequate for providing a reliable means of estimating blueberry fruit weight of different cultivars.

Introduction

Monitoring of fruit growth is a measurement widely used in studies of physiology and evaluation of management practices (e.g. [START_REF] Godoy | Analysis of highbush blueberry (Vaccinium corymbosum L.) fruit growth with exponential mixed models[END_REF][START_REF] Minchin | Competition between fruit and vegetative growth in Hayward kiwifruit[END_REF]. Thus, its knowledge has been an important parameter in understanding source-sink relationships, water and nutrient use efficiency, crop quality and yield potential (Génard and Lescourret, 2004[START_REF] Grossman | Maximum fruit growth potential and seasonal patterns of resource dynamic during peach growth[END_REF][START_REF] Spreer | Yield and fruit development in mango (Mangifera indica L. cv. Chok Anan) under different irrigation regimes[END_REF]. Habitually, fruit growth curves are developed from detached samples; however, this method can increase variance and lead to erroneous results for species in which fruits grow in clusters (Coombe, 1976). This is because several development stages of the fruit can be observed on a plant at a given time, as occur typically in several fruits species including blueberry (e.g. [START_REF] Tuccio | Rapid and non-destructive method to assess in the vineyard grape berry anthocyanins under different seasonal and water conditions[END_REF][START_REF] Godoy | Analysis of highbush blueberry (Vaccinium corymbosum L.) fruit growth with exponential mixed models[END_REF]. A common method for non-destructively estimating biomass of plant organs is to fit simple equations to data relating biomass to some dimensional characteristic of the plant such as diameter, height, length and width (e.g. Antunes et al., 2008;[START_REF] Roxburgh | Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest?[END_REF]. In this line, estimating fruit weight from equations using simple measurements of fruit diameter can represent an inexpensive and rapid alternative for accurately assessing fruit weight. In blueberry industry, fruit segregation on the basis of weight and size, is crucial to obtain higher export market value. The aim of this communication is to develop a simple allometric model for estimating blueberry weight from diameter measurements.

Materials and methods

Fruit data

In three growth seasons, a total of 416 healthy berries from three northern highbush blueberry cultivars (V. corymbosum cultivars 'Bluegold', 'Brigitta' and 'Elliot') and one southern blueberry cultivar (V. corymbosum x Vaccinium darrowi Camp. cultivar 'Legacy') were collected from three commercial orchards located in the Región de La Araucanía, Chile (Table 1).

The cultivars used differing in fruiting season; Bluegold and Elliot are early-and late-season cultivars, respectively, whereas Legacy and Brigitta are mid-season cultivars. Plants of the orchards ranged from three to five-year-old and were established at a spacing of 3 × 0.9m, in north-south oriented rows on an Andisol. In each orchard, the average fertilization was 70 kg N ha -1 , 40 kg P2O5 ha -1 and 75 kg K2O ha -1 , the irrigation was applied as needed, and pests and diseases were controlled according to the locally recommended practices. Winter pruning was annually done in each orchard, removing old wood, damaged canes, unproductive branches, and short shoots that develop from the base of the plant.

Fruits were weekly picked from fruit coloring phase (stage in which berries are changing from green to pink to blue in the cluster) up to beginning of commercial harvest for each season. After picking, samples were placed in a cooler and then rapidly transported to the laboratory for evaluating equatorial diameter and fresh weight by using digital caliper (Mitutoyo, Japan, accuracy ±0.01mm) and technical balance (Gibertini Elettronica S.r.l., Italy), respectively.

The data set was randomly divided to obtain data for both calibrating (n= 200) and validating (n= 216) model (external validation).

Allometric model calibration

Preliminary regression analysis showed that the relationships between fruit weight and diameter followed a power fit typical of allometric models, for all situations in which fruit was obtained (Eq. where FW is berry weight, FD is berry diameter, a and b are allometric coefficients.

In order to discriminate the use of cultivar-specific models (including the season effect) or a common model for four cultivars, an Analysis of Covariance was performed on data linearized by natural logarithm transformation (Eq. 2) ln = ln + × ln (Eq. 2)

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. (2017). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009

5

Thus, when interaction between logarithm transformed diameter (continuous variable) and cultivar/season (categorical variable) is not significant, the slopes among groups are similar, so a common model for four cultivars can be constructed [START_REF] Warton | Common slope tests for bivariate errors-in-variables models[END_REF]Chen et al., 2010).

Model goodness-of-fit and validation

Model results were compared with independent data through graphical procedures. The Relative Root Mean Squared Error (RRMSE, Eq. 3), a common criterion to quantify the mean difference between estimated data and observed data [START_REF] Kobayashi | Comparing simulated and measured values using mean squared deviation and its components[END_REF] was calculated:

= ∑ / (Eq. 3)
where ni, xi, and yi are the number of observed data, the estimated data, and the observed data at the measurement date i, respectively, N is the total number of observed data and the mean of observed values. The smaller the value of RRMSE, the closer to the measurement the estimation is [START_REF] Grechi | Assessing the peach fruit refractometric index at harvest with a simple model based on fruit growth[END_REF].

Model residuals were also calculated and plotted against data estimated by the model to check their normal distribution (i.e. homoscedastic behaviour).

Data analysis

In order to characterize the data set for calibrating model, results obtained from cultivars/season were subjected to Multiple Comparison of Mean (MCM) by using Scheffé's test (α = 0.05). This test was selected because the sample size among groups was different [START_REF] Ruxton | Time for some a priori thinking about post hoc testing[END_REF]. The MSM, analysis of covariance and model fitting were performed with R software version 2.15.0 (R Development Core Team, 2012).

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. ( 2017 2).

Evaluation of regression slopes and allometric model

Diameter and weight of the fruits were regressed on logarithm transformed data in order to evaluate whether the interactive effect of berry diameter (continuous variable) and cultivar/season (categorical variable) is significant for estimating berry weight (Table 3). Regression slopes of logarithm transformed data were no statistically different among cultivars/season, thereafter whole data set for calibration procedure was used for constructing a common model for four cultivars.

Then, fruit weight data were plotted against fruit diameter data, giving a power functional relationship as shown in the Fig. 1. The relation between these variables was highly significant with p-values of the coefficients lower than 2 x10 -16 .

Model goodness-of-fit and validation

For model validation, estimated versus actual berry weights were plotted (Fig. 2). As observed, the values were closely distributed alongside the 1:1 line, although there was some tendency to overestimate the weight, which can be noticed on regression slope, being slightly higher than one (Fig. 2A). The external validation of the model showed that the model goodness-of-fit (RRMSE) decreased from cv. Bluegold (4.7%) to cv. Elliot (6.9%), with cv. Legacy and Brigitta showing intermediate values (Fig. 2A). Residual were randomly distributed along estimated data, which indicates absence of heteroscedasticity (Fig. 2B).

Discussion

Empirical models are required for the non-destructive estimation of biomass of a plant organ of interest from easily obtainable measurements. These models, which are based on allometric theory, use the statistic relations between a variable of interest and one or several variables used as predictor [START_REF] Niklas | Plant Allometry: The scaling of form and process[END_REF].

Many abiotic and biotic factors may potentially affect allometric relationships, including genetics, ontogeny, size, age, structure, site, climate, and their interactions [START_REF] Misle | Leaf area estimation in muskmelon by allometry[END_REF][START_REF] Niklas | Plant Allometry: The scaling of form and process[END_REF], however our results indicated that the relationship between fresh weight and diameter was sufficiently stable irrespectively to the genotype and growth seasons (Table 3). From this, a common allometric model was calibrated, giving a power functional relationship as showed in the Fig. 1. On the other hand, according to the principle of parsimony and the objective of proposing easy-to-use models, this finding was convenient. In the model, as a fruit grows, its dimensions change in ways that maintain their functional balance, but not in the ways that maintain constant ratios between dimensions (Castellan-Estrada et al., 2002). Similar allometric relationships have been reported in studies of fruit physiology. In most of these, fruit diameter was included as predictor of fresh and dry weight (e.g. [START_REF] Gautier | Modulation of competition between fruits and leaves by flower pruning and water fogging, and consequences on tomato leaf and fruit growth[END_REF][START_REF] Gilbert | Modelling the effect of fruit growth on surface conductance to water vapour diffusion[END_REF][START_REF] Díaz-Pérez | Fruit size and stage of ripeness affect postharvest water loss in bell pepper fruit (Capsicum annuum L.)[END_REF][START_REF] Grechi | Assessing the peach fruit refractometric index at harvest with a simple model based on fruit growth[END_REF]. Our allometric model was validated with independent data, giving higher goodness-of-fit for evaluated cultivars (Fig. 2A). This showed that a common model was adequate for providing a reliable means of estimating blueberry fruit weight of different cultivars. On the other hand, residuals follow a normal distribution, with data randomly distributed, which indicates absence of heteroscedasticity (Fig. 2B). When the dispersion pattern of residuals follows a

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. ( 2017). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009 8 heteroscedastic behavior, the estimated standard errors of the regression coefficients are often underestimated, giving an incorrect perception of accuracy (Chatterjee and Hadi, 2006).

In conclusion, blueberry fruit weight was satisfactorily estimated on the basis of diameter measurements via allometric approach. Despite that model showed to be reliable for estimating berry weight of four blueberry cultivars, it must be validated when applied to other genetic materials. Further studies must be done in blueberry fruits to establish allometric equations for relating other fruit quality traits to easily obtainable non-destructive measurements

Acknowledgments

The first author acknowledges to the Convenio Marco 2016, Ministerio de Educación, Chile -Universidad de La Frontera, Chile.

Antunes

14Fig. 1 .FWFig. 2 .

 12 Fig. 1. Allometric relationship between berry diameter (FD) and berry weight (FW). Fitted model,

  Table 2). As observed, the highest values were measured for cv. Legacy in the season 2015-2016, while the lowest values for cv. Brigitta. When comparing cultivars, the mean diameter and weight of the fruit was significantly higher in Legacy and Bluegold than in Elliot and Brigitta (Table

	3. Results
	3.1. Berry diameter and weight
	The mean fruit diameter and weight from different cultivars/seasons ranged from 13.35 to
	17.75 mm and from 1.19 to 2.58 g, respectively (
	6

). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009

Table 1 .

 1 Franck, N. (2017). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009 Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. (2017). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009 Cultivars, season and orchard locations

	Biology, 153: 33-40.
	Castelan-Estrada M., Vivin P. Gaudillere J.P. 2002. Allometric relationships to estimate seasonal
	above ground vegetative and reproductive biomass of Vitis vinifera L. Annals of Botany, 89:
	401-408.
	Chatterjee S., Hadi A.S. 2006. Regression analysis by example. Hoboken, NJ, USA, Wiley.
	Chen H., Felker S., Sun S. 2010. Allometry of within-fruit reproductive allocation in subtropical
	dicot woody species. American Journal of Botany, 97: 611-619.
	Coombe B.G. 1976. The development of fleshy fruits. Annual Review of Plant Physiology, 27:
	207-228.

W.C., Pompelli M.F., Carretero D.M., DaMatta F.M. 2008. Allometric models for nondestructive leaf area estimation in coffee (Coffea arabica and C.

canephora). Annals of Applied

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A.,

Table 2 .

 2 Mean fruit diameter and weight for four field-grown blueberry cultivars on data set used for calibrating model. Standard deviation (in brackets) and letters indicating statistic differences by

	Scheffe's test (α = 0.05) are shown.
	Cultivar	Season	Berry diameter (mm)	Berry weight (g)	N
	Bluegold	2015-2016	16.00 (±2.25) b	1.95 (±0.74) b	50
	Brigitta	2010-2011	13.35 (±3.24) d	1.19 (±0.85) c	50
	Elliot	2008-2009	15.52 (±1.30) bc	1.65 (±0.36) bc	21
	Elliot	2015-2016	15.83 (±1.31) cd	1.14 (±0.31) c	27
	Legacy	2010-2011	14.97 (±1.81) bcd	1.55 (±0.57) bc	16
	Legacy	2015-2016	17.75 (±2.09) a	2.58 (±0.79) a	33
	Comparison among cultivars
	Bluegold		16.00 (±2.25) A	1.95 (±0.74) A	53
	Brigitta		13.35 (±3.24) B	1.19 (±0.85) B	50
	Elliot		14.57 (±1.54) B	1.36 (±0.41) B	48
	Legacy		16.84 (±2.39) A	2.24 (±0.86) A

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. (

2017

). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009 13

Table 3 .

 3 Linear regression between log-transformed weight and log-transformed diameter data, and ANCOVA for log-transformed diameter and cultivar/season variables. Determination coefficient (R 2 ) and significance values are shown. Abbreviations: ln = natural logarithm, FW = fresh weight,

	FD = fruit diameter.
		Linear regressions on
	Cultivar / season		R 2
		logarithmic transformed data
	Legacy / 2015-16	ln(FW) = 2.68 ln(FD) -6.79	0.98
	Legacy / 2008-09	ln(FW) = 2.77 ln(FD) -7.10	0.97
	Bluegold / 2015-16	ln(FW) = 2.74 ln(FD) -6.97	0.98
	Elliot / 2015-16	ln(FW) = 2.77 ln(FD) -7.17	0.97
	Elliot / 2008-09	ln(FW) = 2.97 ln(FD) -7.68	0.98
	Brigitta / 2010-11	ln(FW) = 2.87 ln(FD) -7.43	0.99
	ANCOVA
	Variable	p-value
	ln (FD)	2.20 x10 -16
	Cultivar / season	2.67 x10 -15
	ln (FD) x cultivar / season	0.07

Comment citer ce document : Jorquera-Fontena, E., Génard, M., Ribera-Fonseca, A., Franck, N. (2017). A simple allometric model for estimating blueberry fruit weight from diameter measurements. Scientia Horticulturae, 219, 131 -134. , DOI : 10.1016/j.scienta.2017.03.009