Cette thèse s’intègre dans un contexte où les méthodes utilisées pour la mise en place de suivis environnementaux sont souvent problématiques et peuvent mener à des résultats controversables. L’objectif est de proposer une méthodologie adaptable à la plupart des suivis environnementaux qui permettra aux utilisateurs de produire des suivis scientifiques efficaces ou d’optimiser des suivis déjà en place. Nous avons développé une méthodologie qui permet à l’utilisateur de fixer la précision qu’il veut sur ses résultats d’estimation et qui lui renvoie un protocole d’échantillonnage optimal associé à un nombre d’unités statistiques à échantillonner. Une fois le nombre de points connu, il est simple d’estimer le coût de mise en place dela procédure d’échantillonnage sélectionnée sur le terrain.Nous sommes partis de la définition même de la performance d’un protocole d’échantillonnage pour élaborer une méthodologie sous forme de procédure séquentielle qui permet de tester, puis de choisir, le protocole le plus performant pour chaque étude. Plus un protocole d’échantillonnage est performant, moins il nécessite d’unités statistiques pour atteindre une précision voulue. La méthodologie présentée permet donc, pour une (ou des) précision(s) désirée(s) sur les résultats, de déterminer puis de comparer le nombre optimal d’unités statistiques à échantillonner pour différents protocoles. La première étape de la procédure développée nécessite de recréer mathématiquement la population statistique la plus représentative possible de la population étudiée. Ensuite, les différentes combinaisons protocole d’échantillonnage / nombre d’unités statistiques sont simulées puis comparées. Cela permet d’obtenir le meilleur rapport coût-efficacité pour une étude nécessitant un échantillonnage dans un objectif d’inférence, autrement dit, de baisser son prix tout en garantissant une précision adéquate.Les objectifs de cette thèse ont été atteints : la méthode à été développée puis testée sur trois cas d’études. Le premier est la mise en place d’un suivi efficace lorsqu’il n’existe pas de données disponibles. L’exemple utilisé est celui de la mise en place du suivi du moustique tigre sur l’agglomération de Bayonne-Anglet-Biarritz. L’espèce est en début d’invasion dans cette zone et il n’existe donc quasiment pas de données de suivi. Nous avons récupéré les données de détection dans des villes méditerranéennes, les avons modélisées et avons appliqué le modèle à l’agglomération d’intérêt pour ensuite y définir un protocole de suivi optimal.Le second cas d’étude est l’optimisation d’un suivi lorsque seulement une saison de données est disponible. L’exemple est celui du suivi de la palourde dans le bassin d’Arcachon. Ce suivi est effectué tous les 2 ans depuis 2006, nous avons travaillé sur une seule année de données et prouvé qu’il était possible d’optimiser ce suivi. C’est-à-dire baisser son coût de 30% en gardant une précision assez bonne sur les résultats pour être en capacité de mettre en place des mesures de gestion adaptées. Nous avons ensuite travaillé sur toutes les données depuis 2006 pour proposer une optimisation de ce suivi pérenne dans le temps. / Developing robust, and reliable, environmental surveys can be a challenge because of the inherent variation in natural environmental systems. This variation, which creates uncertainty in the survey results, can lead to difficulties in interpretations. The objective of this thesis was to develop a general framework, adaptable to environmental surveys, to improve scientific survey-results. We have developed a method that allows the user, by defining their desired level of accuracy for the survey results, to develop an efficient sampling design with a minimal sample size.Once the sample size is known, calculating total cost of the survey becomes straightforward. We start from the definition of sampling design performance and build a method for comparison and assessment of an optimal sampling design. As a rule of thumb, the more efficient a sampling design is, the fewer statistical units are needed to achieve the desired accuracy. With less sampling effort the sampling procedure becomes more cost effective. Our method assists in identifying cost-efficient sampling procedures.In this PhD thesis a general methodology is developed, and it is assessed with three case studies. The first case study involved design of an efficient survey when no prior data are available. Here we used the example of tiger mosquito in the Bayonne-Anglet-Biarritz agglomeration (south-west France). This species has only now started invading this area and therefore there are no site-specific data available. We used data from other French Mediterranean’s cities to model the probability mosquito are present in the Bayonne-Anglet-Biarritz area of interest. We used this modelled-population to assess, compare and select an effective sampling procedure.The second case study was for survey optimization when only one season of data are available. The chosen example was from Arcachon bay’s manila clam survey in western France. This clam-monitoring has been done biennially (i.e. once every two years) since 2006. We applied our general methodology on one-year data and demonstrated that survey costs can be reduced by 30% a year with no loss of accuracy or reduction in resource management information. The third case study was based on optimization of a survey when several seasons of data are available. We used the clam surveyed but here as a multi-year dataset. We proposed a long-term spatial and temporal sampling design for monitoring the clam resource.

It appears to be unanimous that alien species in island environments tend to cause considerably more negative than positive impacts. To assess the potential level of threat aliens may pose to the native environment, understanding a species’ population structure and dynamics is of ultimate importance. Assessing both impacts and consequences of management interventions to alien species is likewise only possible through the comprehension of its population structure and dynamics. This can be achieved by estimating the number of individuals in the study site, as well as other population parameters through time, applying population models such as capture-recapture to the collected datasets. Nonetheless, alien species that have low capture rates, such as small mammals, might present a considerable obstacle for conservation, as available capturerecapture models need a relatively large dataset to precisely and accurately estimate population parameters. To improve accuracy and precision of estimates that use sparse datasets, the present study developed an integrated concurrent marking-observation capture-recapture model (C-MOM). The model proposed here, contrary to the commonly available mark-recapture and mark-resight models, allows for two different datasets (i.e. a capture-recapture and a population count) to be integrated, as well as for marking and observation (recapture) data to be collected simultaneously. While few models can integrate different datasets, no model is known to allow for concomitant capture-markobservation activities. To assess the performance of the C-MOM when estimating population parameters for sparse datasets, a virtual ecology study was carried out. The population dynamics of a small rodent, the rock cavy (Kerodon rupestris), as well as capture-recapture and population count datasets, were simulated under different scenarios. The sampled datasets were then analyzed by the C-MOM, and by two other established statistical models: a classical mark recapture (CMR) (based on the Jolly-Seber model), and a zero-truncated Poisson log-normal mixed effects (ZPNE), the only integrated mark-resight model that allows for recapture sampling with replacement. Estimates of population parameters provided by the three models were then compared in terms of bias, precision and accuracy. C-MOM and ZPNE models were afterwards applied to real data collected on a rock cavy colony in the island of Fernando de Noronha. The estimated parameters were used to extrapolate the number of individuals in the rock cavy colony to the whole population in the island. Subsequently, these results were used to develop a risk assessment for the species by modelling historical and management scenarios, simulating both the establishment of the species in the island, and the consequences of different management interventions applied to it. The virtual ecology study showed that, in comparison to the CMR and the ZPNE, the C-MOM presented improved accuracy without overestimating the precision of population parameter’s estimates. The last also presented reduced amplitude of the calculated credible interval at 95% when applied to real data in comparison to the ZPNE. While the extrapolation of C-MOM estimates suggests that the rock cavy population in Fernando de Noronha is 6,652 ± 1,587, ZPNE estimates are of 5,854 ± 3,269 individuals. In the risk assessment, historical simulation models demonstrated that even though different combinations of uncertainty in reproductive parameters of the rock cavy might be possible for the species, these did not interfere significantly in either establishment or spread of the rock cavy population in the island. Moreover, historical yearly mortality has most likely been under 30%. Regarding the species’ management simulations, the most effective management interventions to achieve population extinction were spaying and neutering of both sexes, although harvest effort presented the highest influence on this populations’ extirpation. Nonetheless, the relative influence of female and both sexes’ based interventions did not differ significantly regarding the frequency of extinction of stochastic replicates’. Moreover, none of the management interventions guaranteed the population extinction within the time span and harvest effort proposed for the management program. Neutering of both sexes was most inversely influential on time to extinction of this population, followed by removal of both sexes. Briefly, the C-MOM has proven to be a resourceful and precise model to estimate population parameters when low capture rates result in sparse datasets. Moreover, the rock cavy is well established in the island and likely at carrying capacity. In general, the risk assessment showed that the management interventions in the time span and harvest effort simulated in the present study were ineffective to extinguish the rock cavy population in Fernando de Noronha. Considering this, as well as the importance of investigating other vital factors to decide in favour of or contrary to the management of this species, it is recommended that both an impact assessment of the rock cavy and a cost-effectiveness analysis of the management interventions should be performed to complement the current study.:Acknowledgement III Abstract IV Zusammenfassung VI Resumen IX Table of Contents XII List of Tables and Figures XIV List of Abbreviations XIX 1. Introduction 1 1.1. Invasive alien species and their consequences 1 1.2. Population dynamics analysis 2 Capture-recapture models 3 Observation models 4 Integrated population models 5 Software 7 Model analysis 8 1.3. Fernando de Noronha and the rock cavy 10 1.4. Objectives 12 Overall Objectives 12 Specific Objectives 13 2. Study Framework 15 3. Methods 19 3.1. Study area 19 3.2. Study case species 21 3.3. Research Steps 24 RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 24 RESEARCH STEP II: C-MOM application to a real case study 40 RESEARCH STEP III: The rock cavy population in Fernando de Noronha 45 RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 47 4. Results 63 4.1. RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 63 4.2. RESEARCH STEP II: C-MOM application to a real case study 72 4.3. RESEARCH STEP III: The rock cavy population in Fernando de Noronha 73 4.4. RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 74 Sensitivity analysis 74 Simulation experiments 80 5. Discussion 83 5.1. Bias, precision and accuracy of population dynamic models for sparse datasets 85 Simulated data 85 Study case 90 5.2. Advantages and disadvantages of the C-MOM approach 93 5.3. Development and applications of the integrated models and the C-MOM 96 5.4. The reversed use of the PVA software Vortex to simulate AS and IAS populations’ extinction 97 5.5. Status of the rock cavy population in the island of Fernando de Noronha 100 The colonization of the rock cavy in Fernando de Noronha 101 Management of the rock cavy in Fernando de Noronha 104 Study case limitations and future researches 112 6. Conclusion 116 References 118 Appendices 124 APPENDIX I – Assessment of biological invasions 124 APPENDIX II – Population dynamics simulation and dataset sampling 125 APPENDIX III – CMR and C-MOM model codes in R 134 APPENDIX IV – ZPNE model code in R 138 APPENDIX V – C-MOM model used for real datasets 143 APPENDIX VI – Rock cavy colony sizes and number of individuals in Fernando de Noronha 145 APPENDIX VII – Parameter’s ranking of C-MOM, CMR and ZPNE models 148 APPENDIX VIII – Bias, precision and accuracy table 149