ABSTRACT Bird population estimation over broad spatial and temporal scales is a key objective in ornithology. To date, bird ecologists mainly relied on standard point counts where the number

of detected individuals is interpreted as either the true abundance or proportionally related to it. However, providing accurate estimates of species abundance requires modelling the

observation process with temporally replicated data, which is not always possible with the increasing use of ever-bigger datasets from citizen science programs. Data integration methods

allow combining temporally replicated sampling at coarser spatial grains with data collected over larger spatial extents. Here, we developed an Integrated distance sampling (IDS) to combine

observation processes, without significant biases, our case study suggests that such model improvements will depend on specific sampling scenarios. Integrated models represent a promising

tool for ecological science, permitting the joint use of large unstructured datasets with scale-restricted structured surveys. SIMILAR CONTENT BEING VIEWED BY OTHERS JOINT ANALYSIS OF

STRUCTURED AND SEMI-STRUCTURED COMMUNITY SCIENCE DATA IMPROVES PRECISION OF RELATIVE ABUNDANCE BUT NOT TRENDS IN BIRDS Article Open access 24 November 2022 SUPPLEMENTAL STRUCTURED SURVEYS

AND PRE-EXISTING DETECTION MODELS IMPROVE FINE-SCALE DENSITY AND POPULATION ESTIMATION WITH OPPORTUNISTIC COMMUNITY SCIENCE DATA Article Open access 14 May 2024 RELIABILITY OF ENVIRONMENTAL

DNA SURVEYS TO DETECT POND OCCUPANCY BY NEWTS AT A NATIONAL SCALE Article Open access 25 January 2022 INTRODUCTION To estimate bird species abundance, ornithologists mainly rely upon

standardised point-count methods consisting of individual records of detected birds, either visual or auditory, over a given time period1. While standard models like GLMs (generalised linear

models) allow extrapolating these observed counts to novel unsampled conditions through covariates, they also imply that the number of detected individuals represent an accurate estimate of

true abundance, or corresponds to a constant proportion of the sampled population across space and time2,3. However, multiple studies have shown that this assumption is not always

viable2,4,5 because of variations in species detectability arising from observation errors3, or changes in species phenologies6. can affect the actual proportion of detected individuals. For

a set sampling effort, data collection faces a trade-off between (i) the sampling of a large quantity of unstructured data across a broad spatial scale, or (ii) sampling of highly

standardised data collected at a smaller scale7. Given the nature and volume of data collected by standard protocols, ecologists must address this issue relying increasingly on more or less

opportunistic or semi-structured Citizen Science (CS) programs8. However, reliable abundance estimates require additional information such as repeated visits, collection of detection

distances or data collected by multiple observers, to enable the combined modelling of the distinct ecological and observation processes9, see Box 1. While the ecological process corresponds

to species response to environmental covariates variations through space and/or time, the observation process depicts a probabilistic representation of mechanism underlying data

collection10. Nichols et al.,11 describe the observation process as being represented by four components; (i) the probability that individuals’ home ranges overlap the sampling units

\({p}_{s}\); (ii) given \({p}_{s}\), the probability that individuals are present on the sampling units during observers visits \({p}_{p}\); (iii) the probability that individuals’ are

available for detection (for instance, bird vocalizing during observer visits) denoted \({p}_{a}\) and (iv) the probability of detection given individuals presence and detectability

\({p}_{d}\). While \({p}_{s}\) is assessed through sampling design and \({p}_{d}\) can be inferred from specific data collection, such as detection distances; \({p}_{a}\) and \({p}_{p}\)

probabilities require temporal replicates to be estimated12. Ecological inferences, explicitly accounting for the ecological and observation processes, require flexible statistical tools

such as hierarchical models able to account for global model complexity by a succession of submodels of lesser complexity13. These models vary depending on the studied ecological process14

from species presence/absence – (occupancy models; Ref.15) to species abundance – (hierarchical distance sampling,16; N-mixture models,17) or demographic parameters estimation –

(Cormack-Jolly-Seber models, Ref.18). In the last two decades, Citizen Science has seen exponential growth19 thanks to the development of several online databases such as eBird

(www.ebird.org), iNaturalist (www.inaturalist.org) and GBIF (www.gbif.org) aiming to handle observation data collected by volunteers20 over increasingly longer temporal and larger spatial

scales21. These databases rely mostly on opportunistic data, information gathered without sampling design or focused taxa21. While the use of metadata and ad hoc filters can increase the

value of collected data22, Citizen Science tends to lack specificities of structured surveys, including intra- and inter-year repeated visits23,24. Data integration, or the simultaneous

joint analysis of an ecological process using multiple datasets25 developed a growing interest in recent years26,27. It is used, for instance, in the case of complex ecological inference

requiring different data sources, such as integrated population models (IPM;25). These models rely on count data as well as nest monitoring and/or banding to infer population spatiotemporal

variations and population growth parameters28,29; or to combine data collected at different spatial and/or temporal resolutions30. Here, we focus on data collected for breeding bird atlases,

depicting known distribution and population size estimates using data collected over a short timeframe. In France, the previous breeding bird atlas31 was based on a semi-quantitative method

to estimate national population size32. This approach extrapolated bird densities locally determined over a few local areas without accounting for the detection process. It resulted in

biased estimations of French breeding bird populations when compared to estimates inferred from a structured CS scheme EPOC-ODF (Structured Estimation of Common Bird Population Size, see33).

While structured schemes result in intensive data collection to collect high-quality data, they tend to be conducted over a rather limited spatial extent. In contrast, semi-structured

schemes aim at overcoming this issue to gather interpretable data while still enlisting the largest possible number of observers and associated field data34. For our study, we used datasets

from both the structured CS scheme EPOC-ODF and the semi-structured CS scheme EPOC (Estimation of Common Bird Population Size), where one scheme allows inference of the detection process

through repeated visits, while the other focuses on the collection of environmental data without repeated visits, akin to a double-sampling design35. Recent studies have shown the potential

of data integration on ecological inferences combining data from multiple data sources for occupancy modelling36,37 and species abundance estimates38. In this study, we relied on a joint

likelihood approach39 based on the integrated distance sampling (IDS) formulation from38. While Kéry et al.,38 formulated an IDS model integrating data from unreplicated distance sampling

data using point count and detection/non-detection data assessing species availability through list duration, we aim to calibrate an IDS model accounting for species availability through

temporal replicates. Availability, or temporary emigration11,12,17, can represent different biological processes, such as (i) random temporary emigration, when individuals display

conspicuous behaviours allowing increased detection rate during survey (birds vocalisations40, burrowing or diving41,42); (ii) spatial temporary emigration, where individuals remain

undetected due to being physically outside the sampled sites during survey period; and (iii) availability resulting from variation in population-level processes, such as recruitment,

survival, emigration or immigration13,43. Survey duration, addressed in38, accounts primarily for random temporary emigration where individuals could be present on site but remained

undetected due to a lack of emitted vocal or visual cues. In contrast, temporal replicates across broader time scales, used in this study, mainly account for spatial temporary emigration

instead. In this manuscript, we applied the developed IDS model to a structured and semi-structured dataset, EPOC-ODF and EPOC, collected over three French regions under distinct data

collection schemes. We compared ecological and observation parameters estimates from the IDS model to those obtained from a HDS model calibrated using only data collected by EPOC-ODF to test

if data integration could lead to improvement in the accuracy of estimated parameters, _i.e._ reduction of their uncertainties. In addition, we conducted a simulation study aiming (i) to

assess model identifiability, _i.e._, its capabilities to accurately estimate parameters; and (ii) to test potential improvement in estimated accuracy over multiple ranges of variation of

simulated species availability, detectability and sampling scenarios. MATERIAL AND METHODS HIERARCHICAL DISTANCE SAMPLING Hierarchical distance sampling (HDS) model aimed to estimate species

abundance while taking account of the observation process13. As conventional distance sampling assumes perfect detection44 at a null distance from the observers (i.e. \(f(x=0) = 1\), see

below), HDS can relax this assumption by assessing the probability that the individual is present and available for detection during survey occasions17 through lists duration or multiple

visits at the same site. Considering a population following Poisson distribution with mean \({\lambda }_{i}\), at each site i = 1,2,..,I we have the local population size \({M}_{i}\):

$${M}_{i}\sim Poisson({\lambda }_{i})$$ Given multiple visits _j_ (_j_ = 1,2, ... ,J), at site _i_, the number of individuals available for detection \({N}_{i,j}\) follows a binomial

distribution from the local population \({M}_{i}\) with a probability of being exposed to sampling, i.e. available for detection, \({\varphi }_{i,j}\): $${N}_{i,j}\sim

Binomial({M}_{i},{\varphi }_{i,j})$$ For each site _i_ and visit _j_, observers measure the distance of observation between themselves and detected individuals. A vector of cell

probabilities \({\pi }_{i,j}\) derived from a detection function _f_44, assigns probabilities to distinct distance bins. Observation \({y}_{i,j}\) can then be described as a multinomial

outcome given the number of individuals available for detection and its distance (\({x}_{i,j}\)): $${y}_{i,j}\sim Multinomial\left({N}_{i,j},{\pi }_{i,j}\right), with\ {\pi

}_{i,j}=f({x}_{i,j},\sigma )$$ In our study, we relied on point count data using observation distances between observers and detected individuals. We also considered a half-normal model,

with parameter (\(\sigma\)) for the detection function. SIMULATION STUDY 1: MODEL IDENTIFIABILITY For simulation study 1, we generated 1000 cases each consisting of a structured dataset,

with 9 temporal replicates, collected over 200 sites and a semi-structured dataset containing 1000 sites with single visits over one season (Fig. 1). For each case, we randomly generated

sets of parameters related to the ecological and observation processes, with (\({\beta }_{0}\)) species mean abundance, (\(\beta\)) effect of covariate \({X}_{i}\) on species abundance;

(\({\varphi }_{0}^{DSopen};{\varphi }_{0}^{DS}\)) depicting mean species availability estimated by, respectively the structured and semi-structured dataset; (\(\gamma\)) effect of covariates

\({U}_{i,j}\) and \({V}_{i}\) over species availability; (\({\sigma }_{0}\)) mean species detectability and (\(\alpha\)) effect of covariate \({Z}_{i,j}\) over species detectability, see

Box 1 and Eq. (1). We also included residual errors on species abundance and species detectability, respectively (\({\varepsilon }_{i}^{abund}\); \({\varepsilon }_{i}^{det}\)) generated from

a normal distribution of mean 0 and standard deviation (\({\sigma }_{{\varepsilon }^{abund}}\); \({\sigma }_{{\varepsilon }^{det}}\)).

$$\left\{\begin{array}{c}\begin{array}{c}\text{log}\left({\lambda }_{i}\right)={\beta }_{0}+\beta *{X}_{i}+{\varepsilon }_{i}^{abund} \\ logit\left({\varphi }_{i,j}^{DSopen}\right)= {\varphi

}_{0}^{DSopen}+\gamma *{U}_{i,j} \\ logit\left({\varphi }_{i}^{DS}\right)={\varphi }_{0}^{DS}+\gamma *{V}_{i}\end{array}\\ \text{log}\left({\sigma }_{i,j}\right)={\sigma }_{0}+\alpha

*{Z}_{i,j}+ {\varepsilon }_{i,j}^{det}\end{array}\right.$$ (1) We used an altered version of the function simHDSopen from _AHMbook_45 to simulate the datasets. All models were fitted using

JAGS 4.3.146 through the _jagsUI_47 R package, while MCMC samples were retrieved using _mcmcoutput_48. See appendix S1 for MCMC parameters and priors used for simulation and case study.

SIMULATION STUDY 2: ESTIMATES ACCURACY ACROSS DIFFERENT SAMPLING SCENARIOS In simulation study 2, we aimed to assess improvement in accuracy of estimated parameters through data integration

across multiple sampling scenarios. We used the same 1000 cases generated in simulation study 1, but varied the number of structured sites (ranging from 50 to 300) and semi-structured lists.

The latter was determined by the multiplication of the number of structured sites, using a ratio ranging from 1 to 6. We defined ranges of the number of structured sites and ratio of added

semi-structured lists based on the proportion of sampling schemes in our case study, see Fig. 2 and appendix S2. Inference improvement was associated with a reduction of uncertainty (_i.e_.

reduction of the posterior distribution spread of estimated parameters using the 95% credible intervals CRI). We calibrated a linear model of the log-transformed CRI width to assess if its

reduction was affected by factors such as the model formulation used (either HDS or IDS) or estimated parameters. As we expect that model formulation could benefit from the number of input

data, we included an interaction between model formulation and the simulated sampling design, _i.e._ the number of simulated structured sites and the ratio of added semi-structured sites. As

the response variable of our intended model is derived from simulation results, we conducted a bootstrap to assess variation of CRI reduction through resamples over simulated cases and

their associated parameters. Confidence intervals were estimated using 100 linear models, each based on resamples of 250 from converging IDS and HDS models. CASE STUDY We relied on EPOC-ODF

(Structured Estimation of Common Bird Population Size) and EPOC (Estimation of Common Bird Population Size) citizen science schemes data collected over 2021–2023 breeding seasons. These two

schemes consist of 5-min point count completed checklists, during which observers point locations of detected individuals using the mobile app NaturaList49. Observation distances between

observers and detected individuals are measured through GIS (Geographic Information System) using observers location determined by GPS. We used data from 31 bird species collected during

their breeding season over 2021–2023 across three French regions (Bourgogne-Franche-Comté, Nouvelle-Aquitaine and Normandie). These regional datasets differ in terms of data quantity

providing diverse distributions of structured and semi-structured data collections (Fig. 2.). The EPOC scheme does not constrain observers to pre-selected sites, nor require repeated visits

whereas, for EPOC-ODF, survey locations are randomly selected from a systematic grid and have to be visited three times during the breeding season, each session consisting of three

successive 5-min point counts. For the semi-structured dataset (EPOC), we applied a spatial filter to select EPOC lists collected at least two kilometres away from sites with temporal

replicates (EPOC-ODF) and other EPOC lists, see appendix S2. For each species, we calibrated a HDS model, using only data collected by the EPOC-ODF schemes and an IDS model using data

collected by both schemes. Bird species selection was based upon targeted species from the two schemes33 and had a sufficient number of observations, at least detected once at 20 distinct

EPOC-ODF sites, in each region. We applied a temporal filter that considered both observed bird activities during the breeding season and expert knowledge to define the breeding phenology of

each targeted species and exclude potential early or late migrants. For each species, we applied a right-side truncation of 5% over the observation distance to remove extreme distance

values for model robustness50. We modelled the population size of a site \({M}_{i}\) using a Zero-inflated Poisson with parameter \({\mu }_{i}\) (Fig. 3): $${M}_{i}\sim Poisson\left({\mu

}_{i}\right), with \ {\mu }_{i}={\lambda }_{i}*(1-{\omega }_{i})$$ The expected species abundance parameter (\({\lambda }_{i}\)) was modelled using reduced habitat51 and bioclimatic52

covariates obtained through PCA33. $$log\left({\lambda }_{i}\right) ={\beta }_{0}+{\sum }_{a=1}^{3}{\beta }_{a}*Habitat PCA{s}_{i}+{\sum }_{a=4}^{6}{\beta }_{a}*Bioclimatic

PCA{s}_{i}+{\varepsilon }_{i}^{abund}$$ The zero-inflation parameter (\({\omega }_{i}\)) corresponds to site suitability depicted by a Bernoulli process with the probability (\({\rho

}_{i}\)) of a site being considered unsuitable. We modelled \({\rho }_{i}\) in regards to site ecoregions, as a categorical variable53 and its spatial continuity54. We also included a site

random effect for abundance (\({\varepsilon }_{i}^{abund}\)). $${\omega }_{i}\sim Bernoulli({\rho }_{i})$$ $$logit\left({\rho }_{i}\right)={\rho }_{0}+{\sum }_{a=1}^{e}{\delta

}_{a}^{cat}*Ecoregio{n}_{i}+\delta *Spatial\ continuit{y}_{i}$$ From the sampling scheme and temporal intervals between EPOC-ODF sessions, we considered that species availability primarily

reflected spatial temporary emigration, due to migratory arrivals and departures during breeding seasons, potentially affecting the number of individuals potentially present on sites during

surveys. Consequently, we modelled the probability of an individual being available for detection (\({\varphi }_{i,j}\)) using covariates such as hour from sunrise and julian date with

quadratic effect to represent birds’ phenology across the breeding season. In the IDS model, we included a categorical covariate (\({\gamma }^{cat}\)) to account for variations in species

availability due to the difference of temporal sampling over breeding seasons of the two schemes. $$logit\left({\varphi }_{i,j}\right)={\varphi }_{0}+{\gamma }_{1}*Da{y}_{j}+{\gamma

}_{2}*Da{y}_{j}^{2}+{\gamma }_{3}*Hr.su{n}_{j}+{\gamma }_{4}*Hr.su{n}_{j}^{2}+{\varepsilon }_{i,j}^{avail}+ {\eta }_{i}^{avail}$$ For species detectability, we used a half-normal detection

function with parameter (\({\sigma }_{i,j}\)), where we modelled observers detection probabilities in regards to observed distances using categorical variables describing the habitat over

four categories (Agricultural, Forest, Open and Urban;33) as well as the distance between their GPS locations and the nearest road55. For the IDS model, we considered two distinct intercepts

allowing calibration of two separate detection functions, one for each dataset. $$log \left({\sigma }_{i,j}\right) ={\sigma }_{0}+\alpha *Dist.Roa{d}_{i}+{\sum }_{a=2}^{4}{\alpha

}_{a}^{cat}*Near\ habita{t}_{i}+{\varepsilon }_{i,j}^{det}+{\eta }_{i}^{det}$$ For species availability and detectability; we accounted for the study design of the structured dataset by

implementing random effects over each session (\({\varepsilon }_{i,j}^{avail}\) and \({\varepsilon }_{i,j}^{det}\)) while also adding observers random effect over surveyed sites or lists

(\({\eta }_{i}^{avail}\) and \({\eta }_{i}^{det}\)), as one observer can partake in both CS schemes, see appendix S1 for used priors. We fitted two linear mixed-effects models for assessing

CRI reduction and shift in means of estimated parameters between the IDS and HDS. For both linear models, we considered a fixed effect of estimated parameters and included an interaction

between model formulation and studied regions. We also added nested random effects over species and studied regions to account for specific species response for each region. Models were

fitted using _lme4_56. We used _emmeans_57 to estimate marginal means from the linear model and pairwise post hoc multiple comparisons. For the case study analysis, we removed (\({\gamma

}^{cat}\)) and (\({\delta }^{cat}\)) parameters from comparison as the \({\gamma }^{cat}\) is not estimated in the HDS formulation and \({\delta }^{cat}\) parameters varied across studied

regions. RESULTS SIMULATION STUDY SIMULATION STUDY 1 For simulation study 1, 861 out of 1000 simulated datasets resulted in converging models. Overall, the IDS model demonstrated its ability

to accurately estimate the parameters for both the ecological and observation processes. While \({\varphi }_{0}^{DSopen}\), \({\varphi }_{0}^{DS}\), \({\sigma }_{{\varepsilon }^{abund}}\)

and \({\sigma }_{{\varepsilon }^{det}}\) parameters appeared to have lower precision, all parameters had a coefficient of correlation (R2) above 0.85 between their simulated and estimated

values (Fig. 4; S4.1). We also see that estimation of \({\beta }_{0}\) were centered over the generated value across all simulation. See appendix S3 for an analysis of model convergence of

simulation studies. SIMULATION STUDY 2 For simulation study 2, out of 1000 simulated datasets, we had 892 converging models using the HDS formulation and 930 converging models using the IDS

formulation. There were no signs of major bias between simulated and the mean of parameter estimates considered (Fig. 5). Bootstrap resamples were based on 844 converging models for both the

HDS and IDS formulation. We obtained a considerable reduction of CRI width across all estimated parameters for the IDS model (Fig. 6a). Overall, the IDS and the HDS models produced narrower

CRI for available and more easily detectable species, however, the IDS model produced narrower CRI, for equivalent species availability-detectability profiles simulated than the HDS (Fig. 

6b). The number of structured sites, i.e. including temporal replicates, was correlated with a reduction of CRI width for both models (Fig. 6c), although the IDS model CRI reduction was also

correlated with an increasing proportion of semi-structured sites added to the calibration dataset (Fig. 6c). While the increasing proportion of semi-structured sites added to the

calibration dataset had no substantial effect on the HDS model, we found an important correlation to a CRI reduction for the IDS model (Fig. 6c). See Appendix S6 for a comparison of CRI

reduction in simulation study 2, where three temporal replicates were considered instead of nine. CASE STUDY Marginal effect plots from the linear model (Fig. 7) showed that Credible

Intervals (CRI) were slightly wider for Normandie (Nor), the region with fewer structured sites and semi-structured sites than Bourgogne-Franche-Comté (BFC), the region with a few numbers of

structured sites and a large number of semi-structured sites, and Nouvelle-Aquitaine (NvA), the region with a larger number of semi-structured sites (Fig. 2). While there were no

considerable differences (indicated by an overlap of marginal response confidence intervals) between the HDS and IDS CRI for NvA and Nor, CRI from the IDS model were considerably narrower

than the HDS ones for all estimated parameters in BFC (Fig. 7 and appendix S5). Pairwise comparison of marginal means showed no signs of significant differences (p-values > 0.05) between

the HDS and IDS mean estimated parameters across all monitored parameters and studied regions. Squared-GVIFs (Generalised Variance-Inflation Factor; Ref.58), measured using _car_ R

package59, were less than 4, showing no signs of multicollinearity for the terms used in each model. DISCUSSION The present work brings new evidence that Integrated Distance Sampling (IDS)

models can accurately identify parameters of a complex ecological process and expand their application accounting for species availability determined through repeated visits. Moreover, it

also shows that data integration improves ecological inference, through the reduction of credible intervals (CRI) width, for all parameters of the studied ecological process, across multiple

sampling design scenarios and species availability-detectability continuums. Results from the case study further strengthen the simulation study, by showing that this reduction of CRI span

without significant variations of estimated mean parameters depends on the ratio of structured and semi-structured data used for each case study. In recent years, there has been an increase

in the interest for integrated models26,27 due to their efficiency in reducing potential biases inherent to a single dataset60 and allowing reliance on automated and non-invasive data

collection methods61,62. Data integration through joint likelihood63 still has potential drawbacks when temporal and/or spatial mismatches, corresponding to discontinuity between dataset

timeframes and spatial heterogeneity, are unaccounted for. Such mismatches could lead to biased inferences where the sampled timeframes and/or regions do not correctly represent the

ecological process of interest63,64. In our simulation studies, we did not include spatial bias in data collection, which could potentially misrepresent citizen science spatial sampling

bias65. We accommodated this mismatch in the case study through a spatial filter over the semi-structured dataset based upon the 2 × 2 km systematic grid resolution of the structured scheme.

This resulted in an important decrease in available data from the semi-structured dataset (see appendix S2) that could be resolved through random effects in the model27. In our case, we

could consider distinct spatial subsets of the semi-structured dataset and implement them into a random effect structure encompassing the modelled sub-processes of the integrated model.

While hierarchical models offer a viable option to disentangle variations due to the observation process from the variations originating from the ecological process of interest9, the

trade-off between data specificities and data quantity can limit their applications. Data integration corresponds to a valuable option to increase the number of available data to help

calibrate such models. Data integration also needs to account for sampling schemes specificities and their potential effect on estimated parameters. For instance, the variation of species

availability in regards to list duration between standardised schemes and non-standardised schemes with varying durations38. Options to calibrate integrated hierarchical models exist in a

frequentist framework38 allowing fast computation. However, given the types of available data and ecological processes of interest, data integration is prone to rely on Bayesian frameworks.

Bayesian computation is based on Markov chain Monte Carlo (MCMC) techniques which are computationally intensive66. Novel approaches exist such as Integrated Nested Laplace Approximation

(INLA) or Bayesian emulation67,68 allowing efficient computation and facilitating implementation of spatial components69. Integrated models could represent an important tool for

macro-ecology related studies, spanning across large spatial scales27 or requiring multiple institutions to coordinate data collection70,71. It could be used, for instance, in the study of

bird populations across Europe from the pan-european common bird monitoring (PECBMS), which gathers data from point count, line transect, or territory mapping schemes across 28 countries and

varying numbers of fieldworkers72, while taking account of country discrepancies in sampling design, sampling effort or varying starting period that could alter estimation of long-term

trend73. The joint analysis of multiple data sources, notably through the use of data collected upon schemes lacking design-based methodology74, could represent a substantial increase in the

quantity of data available for the study of cryptic species75,76 and improve assessment of migratory patterns over large spatial scales77. It represents an influx of data for the estimation

of ecological processes of interest27, potentially reducing the sampling effort of robust designs. For instance, the number of temporal replicates considered in simulation studies and case

study exceeds that of most commonly used schemes. To assess data integration utility beyond our specific case, we conducted an additional simulation study considering a structured scheme

composed of three temporal replicates instead of nine (see appendix S6). Comparison of the HDS and IDS formulations over both temporal replicate quantities revealed that data integration had

a greater effect in parameter accuracy when applied to the less demanding structured survey. However, it remained less accurate than estimates derived from using only data collected from

the structured scheme with nine temporal replicates (Figure S6.1–3). Before their implementation, we highly recommend assessing whether ‘lessen’ structured sampling designs developed in a

data integration context are still capable of estimating the targeted ecological parameters or only partially, using power analysis78 and assessment of integrated models identifiability via

simulations79. Our results highlight the benefits of relying on statistical frameworks such as Integrated Models capable of improving estimates accuracy through expansion of usable data

collected from structured and semi-structured surveys. While our simulation results showed a constant reduction of estimates uncertainty, results from field surveys in three distinct French

regions, depicting distinct ratios in quantity of structured and semi-structured data, showed that this improvement is case-dependant and significantly reduced estimates uncertainty with a

low quantity of structured data and high quantity of semi-structured data. While we advocate for thorough planning before sampling, this suggests that Integrated Models could represent a

conceivable alternative in case of insufficient collection from structured surveys and could also greatly benefit from data collected by citizen science schemes. DATA AVAILABILITY Scripts,

BUGS model files and data for simulation and case studies replications are available online: https://doi.org/10.5281/zenodo.11452853 REFERENCES * Blondel, J., Ferry, C. & Frochot, B.

Point counts with unlimited distance. Stud. Avian Biol. (1981). * Yoccoz, N. G., Nichols, J. D. & Boulinier, T. Monitoring of biological diversity in space and time. _Trends Ecol. Evol._

16, 446–453 (2001). Article  Google Scholar  * Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. _PLoS One_ 9, e111436 (2014).

Article  ADS  PubMed  PubMed Central  Google Scholar  * Burnham, K. P. Summarizing remarks: Environmental influences. _Stud. Avian Biol._ 6, 324–325 (1981). Google Scholar  * Thompson, W. L.

Towards reliable bird surveys: Accounting for individuals present but not detected. _Auk_ 119, 18–25 (2002). Article  Google Scholar  * Lehikoinen, A. Climate change, phenology and species

detectability in a monitoring scheme. _Popul. Ecol._ 55, 315–323 (2013). Article  Google Scholar  * Devictor, V., Whittaker, R. J. & Beltrame, C. Beyond scarcity: citizen science

programmes as useful tools for conservation biogeography. _Divers. Distrib._ 16, 354–362 (2010). Article  Google Scholar  * Castagneyrol, B. et al. Can school children support ecological

research? Lessons from the Oak bodyguard citizen science project. Citiz. Sci. Theory Pract. 5 (2020). * Guillera-Arroita, G. Modelling of species distributions, range dynamics and

communities under imperfect detection: Advances, challenges and opportunities. _Ecography_ 40, 281–295 (2017). Article  ADS  Google Scholar  * Royle, J. A. & Dorazio, R. M. Hierarchical

modeling and inference in ecology: The analysis of data from populations, metapopulations and communities. https://pubs.usgs.gov/publication/5200344 (2008). * Nichols, J. D., Thomas, L.

& Conn, P. B. In Model. Demogr. Process. Mark. Popul. (eds. Thomson, D. L., Cooch, E. G. & Conroy, M. J.), 201–235 (Springer US, 2009). https://doi.org/10.1007/978-0-387-78151-8_9 *

Mizel, J. D., Schmidt, J. H. & Lindberg, M. S. Accommodating temporary emigration in spatial distance sampling models. _J. Appl. Ecol._ 55, 1456–1464 (2018). Article  Google Scholar  *

Kéry, M. & Royle, J. A. _Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 1: Prelude and Static Models_ (Academic

Press, 2016). Google Scholar  * King, R. Statistical ecology. _Annu. Rev. Stat. Appl._ 1, 401–426 (2014). Article  Google Scholar  * MacKenzie, D. I. et al. Estimating site occupancy rates

when detection probabilities are less than one. _Ecology_ 83, 2248–2255 (2002). Article  Google Scholar  * Sollmann, R., Gardner, B., Williams, K. A., Gilbert, A. T. & Veit, R. R. A

hierarchical distance sampling model to estimate abundance and covariate associations of species and communities. _Methods Ecol. Evol._ 7, 529–537 (2016). Article  Google Scholar  *

Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. _Ecology_ 92, 1429–1435 (2011). Article  PubMed  Google Scholar  *

Gimenez, O. et al. State-space modelling of data on marked individuals. _Ecol. Model._ 206, 431–438 (2007). Article  Google Scholar  * Sullivan, B. L. et al. eBird: A citizen-based bird

observation network in the biological sciences. _Biol. Conserv._ 142, 2282–2292 (2009). Article  Google Scholar  * Bonney, R. et al. Citizen science: A developing tool for expanding science

knowledge and scientific literacy. _Bioscience_ 59, 977–984 (2009). Article  Google Scholar  * Hochachka, W. M. et al. Data-intensive science applied to broad-scale citizen science. _Trends

Ecol. Evol._ 27, 130–137 (2012). Article  PubMed  Google Scholar  * Johnston, A. et al. Analytical guidelines to increase the value of community science data: An example using eBird data to

estimate species distributions. _Divers. Distrib._ 27, 1265–1277 (2021). Article  Google Scholar  * Bayraktarov, E. et al. Do Big Unstructured biodiversity data mean more knowledge? Front.

Ecol. Evol. 6 (2019). * Johnston, A., Matechou, E. & Dennis, E. B. Outstanding challenges and future directions for biodiversity monitoring using citizen science data. Methods Ecol.

Evol. (2022). * Schaub, M. & Abadi, F. Integrated population models: A novel analysis framework for deeper insights into population dynamics. _J. Ornithol._ 152, 227–237 (2011). Article

  Google Scholar  * Fletcher, R. J. et al. A practical guide for combining data to model species distributions. _Ecology_ 100, e02710 (2019). Article  PubMed  Google Scholar  * Zipkin, E. F.

et al. Addressing data integration challenges to link ecological processes across scales. _Front. Ecol. Environ._ 19, 30–38 (2021). Article  Google Scholar  * Besbeas, P., Freeman, S. N.,

Morgan, B. J. T. & Catchpole, E. A. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. _Biometrics_ 58, 540–547 (2002). Article

  MathSciNet  CAS  PubMed  Google Scholar  * Schaub, M. _Popul. Ecol. Pract._ 215–236 (Wiley-Blackwell, 2020). Google Scholar  * Keil, P., Wilson, A. M. & Jetz, W. Uncertainty, priors,

autocorrelation and disparate data in downscaling of species distributions. _Divers. Distrib._ 20, 797–812 (2014). Article  Google Scholar  * Issa, N. & Muller, Y. _Atlas des oiseaux de

France métropolitaine: Nidification et présence hivernale_ (DELACHAUX, 2015). Google Scholar  * Roché, J.-E., Muller, Y. & Siblet, J.-P. Une méthode simple pour estimer les populations

d’oiseaux communs nicheurs en France. _Alauda_ 81, 241–268 (2013). Google Scholar  * Nabias, J. et al. Reassessment of French breeding bird population sizes using citizen science and

accounting for species detectability. _PeerJ_ 12, e17889 (2024). Article  PubMed  PubMed Central  Google Scholar  * Kelling, S. et al. Using semistructured surveys to improve citizen science

data for monitoring biodiversity. _Bioscience_ 69, 170–179 (2019). Article  PubMed  PubMed Central  Google Scholar  * Mackenzie, D. I. & Royle, J. A. Designing occupancy studies:

General advice and allocating survey effort. _J. Appl. Ecol._ 42, 1105–1114 (2005). Article  Google Scholar  * Lauret, V., Labach, H., Authier, M. & Gimenez, O. Using single visits into

integrated occupancy models to make the most of existing monitoring programs. _Ecology_ 102, e03535 (2021). Article  PubMed  Google Scholar  * von Hirschheydt, G., Stofer, S. & Kéry, M.

“Mixed” occupancy designs: When do additional single-visit data improve the inferences from standard multi-visit models?. _Basic Appl. Ecol._ 67, 61–69 (2023). Article  Google Scholar  *

Kéry, M. et al. Integrated distance sampling models for simple point counts. _Ecology_ 105, e4292 (2024). Article  PubMed  Google Scholar  * Miller, D. A. W., Pacifici, K., Sanderlin, J. S.

& Reich, B. J. The recent past and promising future for data integration methods to estimate species’ distributions. _Methods Ecol. Evol._ 10, 22–37 (2019). Article  Google Scholar  *

Emlen, J. T. Estimating breeding season bird densities from transect counts. _Auk_ 94, 455–468 (1977). Google Scholar  * Andriolo, A. et al. The first aerial survey to estimate abundance of

humpback whales (_Megaptera_ _novaeangliae_) in the breeding ground off Brazil (Breeding Stock A). _J. Cetacean Res. Manag._ 8, 307–311 (2006). Article  Google Scholar  * Manning, J. A.

Factors affecting detection probability of burrowing owls in southwest agroecosystem environments. _J. Wildl. Manag._ 75, 1558–1567 (2011). Article  Google Scholar  * Kéry, M. & Royle,

J. A. _Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 2: Dynamic and Advanced Models_ (Academic Press, 2020). Google

Scholar  * Buckland, S. T., Rexstad, E. A., Marques, T. A. & Oedekoven, C. S. _Distance Sampling: Methods and Applications, Methods in Statistical Ecology_ (Springer International

Publishing, 2015). https://doi.org/10.1007/978-3-319-19219-2. Book  Google Scholar  * Kéry, M., Royle, A. & Meredith, M. AHMbook: Functions and data for the book ‘Applied Hierarchical

Modeling in Ecology’ Vols 1 and 2. https://cran.r-project.org/web/packages/AHMbook/index.html (2023). * Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs

sampling. In _Proc. 3rd Int. Workshop Distrib. Stat. Comput._ (2003). * Kellner, K. & Meredith, M. jagsUI: A wrapper around ‘rjags’ to Streamline ‘JAGS’ Analyses.

https://cran.r-project.org/web/packages/jagsUI/index.html (2021) * Juat, N., Meredith, M. & Kruschke, J. mcmcOutput: functions to store, manipulate and display Markov Chain Monte Carlo

(MCMC) Output. https://cran.r-project.org/web/packages/mcmcOutput/index.html (2022) * LPO France. Oiseaux de France: Fiche tutoriel.

https://oiseauxdefrance.org/get-involved/Tuto-EPOC-ODF.pdf * Buckland, S. T. et al. _Introduction to Distance Sampling: Estimating Abundance of Biological Populations_ (Oxford University

Press, 2001). Book  Google Scholar  * Thierion, V., Vincent, A. & Valero, S. Theia OSO Land Cover Map 2020. 10.5281/zenodo.6538861 (2022). * Fick, S. E. & Hijmans, R. J. WorldClim 2:

new 1-km spatial resolution climate surfaces for global land areas. _Int. J. Climatol._ 37, 4302–4315 (2017). Article  Google Scholar  * IGN. Sylvoécorégions - Cartographie des

sylvoécorégions. https://geo.data.gouv.fr/fr/datasets/a40c533b984bdcd33d8a38f2430a117672395bc0. (2011) * Guetté, A., Carruthers-Jones, J. & Carver, S. J. Projet CARTNAT Cartographie de

la Naturalité (2021). * Cote, C., Troncon, C., Troncon, C. & Troncon, C. ROUTE 500® Version 3.0 - Descriptif de contenu. 27 (2021). * Bates, D., Mächler, M., Bolker, B. & Walker, S.

Fitting linear mixed-effects models using lme4. _J. Stat. Softw._ 67, 1–48 (2015). Article  Google Scholar  * Lenth, R. V. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means.

https://cran.r-project.org/web/packages/emmeans/index.html (2024). * Fox, J. & Monette, G. Generalized collinearity diagnostics. _J. Am. Stat. Assoc._ 87, 178–183 (1992). Article  Google

Scholar  * Fox, J. et al. car: Companion to applied regression. https://cran.r-project.org/web/packages/car/index.html (2023). * Zipkin, E. F., Inouye, B. D. & Beissinger, S. R.

Innovations in data integration for modeling populations. _Ecology_ 100, 1–3 (2019). Article  Google Scholar  * Doser, J. W., Finley, A. O., Weed, A. S. & Zipkin, E. F. Integrating

automated acoustic vocalization data and point count surveys for estimation of bird abundance. _Methods Ecol. Evol._ 12, 1040–1049 (2021). Article  Google Scholar  * Pavanato Julião, H.

Development of integrated distance sampling models. https://ourarchive.otago.ac.nz/handle/10523/1249 (2021). * Isaac, N. J. B. et al. Data integration for large-scale models of species

distributions. _Trends Ecol. Evol._ 35, 56–67 (2020). Article  PubMed  Google Scholar  * Powney, G. D., Preston, C. D., Purvis, A., Van Landuyt, W. & Roy, D. B. Can trait-based analyses

of changes in species distribution be transferred to new geographic areas?. _Glob. Ecol. Biogeogr._ 23, 1009–1018 (2014). Article  Google Scholar  * Johnston, A., Moran, N., Musgrove, A.,

Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. _Ecol. Model._ 422, 108927 (2020). Article  Google Scholar  * Dorazio, R. M.

Bayesian data analysis in population ecology: Motivations, methods, and benefits. _Popul. Ecol._ 58, 31–44 (2016). Article  Google Scholar  * Rue, H. et al. Bayesian computing with INLA: A

review. _Annu. Rev. Stat. Its Appl._ 4, 395–421 (2017). Article  ADS  Google Scholar  * Fer, I. et al. Linking big models to big data: efficient ecosystem model calibration through Bayesian

model emulation. _Biogeosciences_ 15, 5801–5830 (2018). Article  ADS  CAS  Google Scholar  * Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. Spatial and spatio-temporal models with

R-INLA. _Spat. Spatio-Temporal Epidemiol._ 7, 39–55 (2013). Article  Google Scholar  * Navarro, L. M. et al. Monitoring biodiversity change through effective global coordination. _Curr.

Opin. Environ. Sustain._ 29, 158–169 (2017). Article  Google Scholar  * Silva del Pozo, M., Body, G., Rerig, G. & Basille, M. Guide on harmonising biodiversity monitoring protocols

across scales. 60 (Biodiversa+, 2023). <https://www.biodiversa.eu/wp-content/uploads/2023/10/Biodiversa_Best-practices_2023_v5_WEB.pdf. * Brlík, V. et al. Long-term and large-scale

multispecies dataset tracking population changes of common European breeding birds. _Sci. Data_ 8, 21 (2021). Article  ADS  PubMed  PubMed Central  Google Scholar  * Duchenne, F. et al.

Controversy over the decline of arthropods: A matter of temporal baseline?. _Peer Community J._ https://doi.org/10.24072/pcjournal.131 (2022). Article  Google Scholar  * Farr, M. T.,

Zylstra, E. R., Ries, L. & Zipkin, E. F. Overcoming data gaps using integrated models to estimate migratory species’ dynamics during cryptic periods of the annual cycle. _Methods Ecol.

Evol._ 15, 413–426 (2024). Article  Google Scholar  * Martin, M. E. et al. An integrated spatial capture–recapture approach reveals the distribution of a cryptic carnivore in a protected

area. _Ecosphere_ 14, e4634 (2023). Article  Google Scholar  * Twining, J. P. et al. Integrating presence-only and detection/non-detection data to estimate distributions and expected

abundance of difficult-to-monitor species on a landscape-scale. _J. Appl. Ecol._ https://doi.org/10.1111/1365-2664.14633 (2024). Article  Google Scholar  * Meehan, T. D. et al. Integrating

data types to estimate spatial patterns of avian migration across the Western Hemisphere. _Ecol. Appl._ 32, e2679 (2022). Article  PubMed  PubMed Central  Google Scholar  * Guillera-Arroita,

G. & Lahoz-Monfort, J. J. Designing studies to detect differences in species occupancy: power analysis under imperfect detection. _Methods Ecol. Evol._ 3, 860–869 (2012). Article 

Google Scholar  * Ogle, K. & Barber, J. J. Ensuring identifiability in hierarchical mixed effects Bayesian models. _Ecol. Appl._ 30, e02159 (2020). Article  PubMed  Google Scholar 

Download references ACKNOWLEDGEMENTS EPOC and EPOC-ODF schemes are supervised by LPO (Ligue pour la Protection des Oiseaux), the representative of Birdlife in France. We thank all

participants and local coordinators from regional instances for contributing to data collection through faune-france.org, a collaborative online database. We would like to express our thanks

to Benoit Fontaine for his comments on prior versions of the manuscript, we also would like to thank Marc Kéry, Niggel Yoccoz and an anonymous reviewer for their comments and suggestions

that greatly improved this work. The IDS and HDS models were calibrated on the SACADO MCMeSU platform at Sorbonne Université in Paris – France. FUNDING Funding for this work was provided

through OFB (Office Français de la Biodiversité), LPO and ANRT (Association Nationale Recherche Technologie; CIFRE grant, number: 2021/0305). The French Ministry of the Environment and OFB

support the LPO through multi-year objectives agreements, in particular, to consolidate several EBV relating to birds, based on citizen science monitoring. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * LPO-BirdLife France, Fonderies Royales, Rochefort Cedex, France Jean Nabias, Jérémy Dupuy & Laurent Couzi * CESCO, Muséum National d’Histoire Naturelle, CNRS,

Sorbonne-University, Paris, France Jean Nabias, Romain Lorrillière & Luc Barbaro * Centre de Recherches sur la Biologie des Populations d’Oiseaux (CRBPO), MNHN-CNRS-OFB, Paris, France

Romain Lorrillière * Dynafor, INRA-INPT, University of Toulouse, Auzeville, France Luc Barbaro Authors * Jean Nabias View author publications You can also search for this author inPubMed 

Google Scholar * Romain Lorrillière View author publications You can also search for this author inPubMed Google Scholar * Jérémy Dupuy View author publications You can also search for this

author inPubMed Google Scholar * Laurent Couzi View author publications You can also search for this author inPubMed Google Scholar * Luc Barbaro View author publications You can also search

for this author inPubMed Google Scholar CONTRIBUTIONS All authors contributed to the current work. Study conceptualization was led by JN, RL, LB. Data collection management was performed by

JD and LC. Analysis was carried out by JN and RL. Project administration was supervised by LB and LC. Writing was led by JN. Writing and reviews were conducted by JN and LB. CORRESPONDING

AUTHOR Correspondence to Jean Nabias. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains

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al._ Improving national-scale breeding bird surveys with integrated distance sampling. _Sci Rep_ 15, 18312 (2025). https://doi.org/10.1038/s41598-025-96787-w Download citation * Received: 27

June 2024 * Accepted: 28 March 2025 * Published: 26 May 2025 * DOI: https://doi.org/10.1038/s41598-025-96787-w SHARE THIS ARTICLE Anyone you share the following link with will be able to

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initiative KEYWORDS * Bird monitoring * Citizen science * Distance sampling * Data integration * Hierarchical modelling * Observation process