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Predicting macrovegetation distribution - What can be achieved?

PREHAB conclusion/result:
Models predicting the distribution of various macrovegetation species and groups of species performed well across the Baltic Sea. The performance was similar in all study-areas where they were modelled. Rooted plants, including seagrass and flowering plants of freshwater origin, showed the best performance in distribution models, whereas species and groups of algae showed more variability in their predictability.

What can be achieved - conclusion to manager?
Manager perspective: "Check this!"

Generally, the success of predictive modelling depends on several interconnected factors. For example, the "modelability" of a species depends on the quality of data available, both the numbers of observations and the reliability of those observations and the representativeness of the observations over the range of conditions the species inhabits.

Representativeness is often a factor of scale and varies depending on the environmental attribute in question (e.g. depth variation vs. salinity variation in the Baltic Sea). With very common, generalist species it is often not feasible to cover the whole gradient of distribution. The representativeness accomplished by a given number of sampling sites is also dependent on the size and variability of the area.

Biological samples reliable?
With observations of occurrence (a dataset consisting of records or either presence or absence at a given site) issues of detectability come into play. Larger species are easier to see and some species are easier to identify whilst others require closer inspection which cannot be done from observations from video records or by experts in the field and necessitates collection of samples. Larger plants can also hide smaller plants.

Seasonal species are more unreliable
Species that stay in one place from year to year produce more consistant information over time, which reduces the stochastic variability in the dataset used to build models. Seasonal and irregular patterns in the species presence or detactability at a site often result in unreliable data. Annual filamentous algae, for example have seasonal life cycles with changes in community throughout the year (Kraufvelin et al).

Modelling datasets are usually a compilation from many sources collected in a number of years and different times of year, which introduces measurement based error into the data. The method of data collection: remote sampling, scuba diving or video observation, also contributes to detectability.

Choosy species models better
The balance of observations of presence and absence along environmental gradients define how well a model can distinguish places that are well suited for a species or group from those that are not. The success of models hence depends on the ability of the environmental factors we have available to represent the distribution of our species of interest. Some species have very distinct requirements from their environment, others are more lax. Therefore, models predicting the occurrence of, for example, eelgrass (Zostera marina), usually have a high accuracy in the Northern Baltic Sea, due to the very specific environmental requirements of this species.

A source of error is introduced when random unpredictable factors have a strong effect on the species of interest, which we are not able to map and include in models. Perennial slow growing brown algae such as Furcellaria and Fucus, are frequently missing from environmentally suitable sites due to their susceptible to random disturbances and overgrowth by faster growing algae, and they do not recolonise habitats rapidly. In general, species with distributions strongly affected by factors that cannot be included in models, often due to lack of data on those variables, will produce poorer performing models.

Groups are harder to predict
Often species are grouped into larger groups (such as filamentous algae, red algae or aquatic plants), due to either the difficulty of identifying individual species or the lack of observations on individual species. The grouping increases the number of presences thus providing more information on the environmental preferences of the new group variable. The tradeoff is the specificity of needs the individual species had. By combining e.g. all red algae together, the group becomes very heterogenous with regards to its ecological attributes and therefore, environmental requirements. Consequently it will be more difficult for models to pinpoint suitable environments.

As an example, in the Nothern Baltic the filamentous green algae in the shallow sublittoral consists mostly of the proliferate and easy to identify Cladophora glomerata, which is present throughout the open water season (Kiirikki and Lehvo, 1997). In contrast, filamentous red algae as a group consist of many different species, with different seasonal cycles and also show more interannual variability within sites (Kiirikki and Lehvo, 1997). As a result filamentous red algae as a group yielded poorer models than filamentous green algae. The pros and cons of modelling groups or species have to be weighed up against each other, and one should always bear in mind the ecological similarities and differences of species included in such groups.

Kiirikki, M & Lehvo, A.(1997). Life strategies of filamentous algae in the northern Baltic Proper. Sarsia 82: 259-267Kiirikki, M & Lehvo, A.(1997). Life strategies of filamentous algae in the northern Baltic Proper. Sarsia 82: 259-267



Discuss the following issues:
• General predictive power (performance etc)
• Differences among organisms
• Differences among types of habitats
• Regional differences
• Grain and extent/Scale issue?

Author: Anna-Leena
and co-authors Martynas & Mats/Martin G


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