Understanding demography in an advective environment: Modelling Calanus finmarchicus in the Norwegian Sea
Abstract1. Attempts to understand the demography of natural populations from time-series can be hampered by the fact that changes due to births and deaths may be confounded with those due to movement in and out of the sampling area. 2. We illustrate the problem using a stage-structured time-series o...
| Published in: | Journal of Animal Ecology |
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| Main Authors: | , , , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2004
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| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/13825/ https://doi.org/10.1111/j.0021-8790.2004.00857.x https://hdl.handle.net/10013/epic.24192 |
| Summary: | Abstract1. Attempts to understand the demography of natural populations from time-series can be hampered by the fact that changes due to births and deaths may be confounded with those due to movement in and out of the sampling area. 2. We illustrate the problem using a stage-structured time-series of the marine copepod Calanus finmarchicus sampled in the vicinity of a fixed location but where the demography is shown to be inconsistent with the assumption of a closed population. 3. By combining a realistic simulation of the hydrodynamic environment with a model of phenology we infer the time and location at which the stages observed in each sample were recruited as eggs. This yields a spatial and temporal map of the recruitment history required to produce the observed densities. 4. Using an empirical relationship between C. finmarchicus egg production and the abundance of phytoplanktonic food, the spatio-temporal patterns in chlorophyll a can be inferred. The distributions during the spring bloom are spatially heterogeneous, and we estimate that the phytoplankton patches are of the order of 30 km across. This result is robust to substantial variations in the assumed stage-dependent mortalities. 5. We conclude that information on advective transport can be used to make testable predictions about the scale of spatial heterogeneities. These, in turn, imply the appropriate spatial scale over which time-series might be replicated in order to obtain more information about unknown processes such as mortality. |
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