Facilitating political decisions using species distribution models to assess restoration measures in heavily modified estuaries

The European Water Framework Directive requires a good ecological potential for heavily modified water bodies. This standard has not been reached for most large estuaries by 2015. Management plans for estuaries fall short in linking implementations between restoration measures and underlying spatial analyses. The distribution of emergent macrophytes - as an indicator of habitat quality - is here used to assess the ecological potential. Emergent macrophytes are capable of settling on gentle tidal flats where hydrodynamic stress is comparatively low. Analyzing their habitats based on spatial data, we set up species distribution models with 'elevation relative to mean high water', 'mean bank slope', and 'length of bottom friction' from shallow water up to the vegetation belt as key predictors representing hydrodynamic stress. Effects of restoration scenarios on habitats were assessed applying these models. Our findings endorse species distribution models as crucial spatial planning tools for implementing restoration measures in modified estuaries.

Density-dependence in space and time: opposite synchronous variations in population distribution and body condition in the Baltic Sea sprat (Sprattus sprattus) over three decades

Spatio-temporal density-dependent processes are crucial regulatory factors for natural populations. However, there is a lack of studies addressing spatial density-dependence in fish growth. A previous investigation has suggested spatio-temporal density-dependence in body condition of Baltic sprat. Here, we used different techniques, such as centre of gravity, distance, and homogeneity indices, to better characterize the spatial and temporal variations in sprat density and body condition in the Baltic Proper. Our results evidenced a negative spatio-temporal co-variation between the centres of gravity of density and maximum condition. In the 1980s-early 1990s both centres were located in the middle of the Baltic Proper. From the mid 1990s the centres progressively separated in space, as the sprat population moved towards the north-eastern Baltic Proper, and the centre of maximum condition towards the south-western areas. Moreover, at low abundances, sprat density and condition were homogeneously distributed in space, whereas at high abundances both density and condition showed pronounced geographical gradients. The ecological processes potentially explaining the observed patterns were discussed in the light of the Ideal Free Distribution theory. We provide evidence that the shift in the spatial distribution of cod, the main predator of sprat, has been the main factor triggering the overall spatial changes in sprat density, and thus condition, during the past thirty years. The spatial indices shown here, synthesizing the spatio-temporal patterns of fish distribution, can support the implementation of the EU Marine Strategy Framework Directive.

Temporal variation in the carrying capacity of a perennial grass population

Density dependence and, therefore, K (carrying capacity, equilibrium population size) are central to understanding and predicting changes in population size (N). Although resource levels certainly fluctuate, K has almost always been treated as constant in both theoretical and empirical studies. We quantified temporal variation in K by fitting extensions of standard population dynamic models to 16 annual censuses of a population of the perennial bunchgrass Bouteloua rigidiseta. Variable-K models provided substantially better fits to the data than did models that varied the potential rate of population increase. The distribution of estimated values of K was skewed, with a long right tail (i.e., a few "jackpot" years). The population did not track K closely. Relatively slow responses to changes in K combined with large, rapid changes in K sometimes caused N to be far from K. In 13%-20% of annual intervals, K was so much larger than N that the population's dynamics were best described by geometric growth and the population was, in effect, unregulated. Explicitly incorporating temporal variation in K substantially improved the realism of models with little increase in model complexity and provided novel information about this population's dynamics. Similar methods would be applicable to many other data sets.

Census error and the detection of density dependence

1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the density-dependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.

Dynamics of coastal cod populations: intra- and intercohort density dependence and stochastic processes

Skagerrak populations of Atlantic cod (Gadus morhua L.) have been surveyed at several fixed stations since 1919. These coastal populations consist of local stocks with a low age of maturity and a short life span. We investigated 60 time-series of 0-group juveniles (i.e. young of the year) sampled annually from 1945 to 1994. An age-structured model was developed which incorporates asymmetrical interactions between the juvenile cohorts (0-group and 1-group; i.e. one-year-old juveniles) and stochastic reproduction. The model was expressed in delay coordinates in order to estimate model parameters directly from the time-series and thereby test the model predictions. The autocovariance structure of the time-series was consistent with the delay coordinates model superimposed upon a long-term trend. The model illustrates how both regulatory (density-dependent) and disruptive (stochastic) forces are crucial in shaping the dynamics of the coastal cod populations. The age-structured life cycle acts to resonance the stochasticity inherent in the recruitment process.

Cycles and trends in cod populations

Year-to-year fluctuations in fish stocks are usually attributed to variability in recruitment, competition, predation, and changes in catchability. Trends in abundance, in contrast, are usually ascribed to human exploitation and large-scale environmental changes. In this study, we demonstrate, through statistical modeling of survey data (1921-1994) of cod from the Norwegian Skagerrak coast, that both short- and long-term variability may arise from the same set of age-structured interactions. Asymmetric competition and cannibalism between cohorts generate alternating years of high and low abundance. Intercohort interactions also resonate the recruitment variability so that long-term trends are induced. The coupling of age-structure and variable recruitment should, therefore, be considered when explaining both the short- and long-term fluctuations displayed by the coastal cod populations. Resonant effects may occur in many marine populations that exhibit this combination of traits.