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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:30596 |
| Date | 06 September 2018 |
| Creators | Micheletti Ribeiro Silva, Tatiane |
| Contributors | Roth, Mechthild, Berger, Uta, Russell, James, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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