Distribution And Niche Modeling

For centuries, biogeographical patterns have been studied in a descriptive way by delineating provinces and regions based on the presence of observed species and degrees of endemism, rather than quantifying and explaining these patterns based on environmental variables (Adey and Steneck, 2001). The question as to which environmental variables best explain seaweed species' niches and distributions is, however, one of the most important in global change research. Biogeographical models based on these variables could allow for predicting range shifts and directing field work to discover unknown seaweed species and communities.

It is widely recognized that temperature is a major forcing environmental variable for coastal macrobenthic communities, in general, and seaweeds, in particular. Temperature plays a significant role in biochemical processes, and generally species have evolved to tolerate only a (small) portion of the entire range of temperatures in coastal waters. Thus, it is evident that sea surface temperature (SST, often used as a proxy for water column temperature in shallow coastal waters) plays a prominent role in seaweed niche distribution models. Furthermore,while temperature is often measured in a time-averaged manner (daily, monthly, yearly), it is important to note that the timing of seasons differs globally (even within hemispheres due to seasonal upwelling phenomena). As some seaweed species or specific life cycles are limited by maximum and others by minimum temperatures, it is obviously essential to base models on biologically more relevant maximum, minimum, and related derived variables rather than on raw time-averaged measurements.

van den Hoek et al. (1990) gave an overview of how generalized or annual temperature isotherm maps could be used to explain the geographic distribution of seaweed species in the context of global change.

Adey and Steneck (2001) later described a quantitative model based on the maximum and minimum temperatures as the main variables, combined with area and isolation, to explain coastal benthic macroalgal species distributions. Additionally, their thermogeographic model was integrated over time as they incorporated temperatures from glacial maxima, allowing biogeographical regions to dynamically shift in response to two historical stable states of temperature regimes (glacial maxima and interglacials). In this respect, their study is of significant value in global change research, although their graphic model outputs were not based on GIS and not straightforward to interpret. Moreover, using analogous or vector isothermal SST maps, both studies suffered from a lack of resolution in SST input data, consequently compromising the resolution and accuracy of the model outputs.

Recently, two major studies demonstrated how seaweed distribution models can benefit greatly from the extensive and free availability of environmental variables on a global scale through the use of satellite data. These data are not only geographically explicit and readily usable in GIS, but also provide much more accuracy than isotherm maps due to their continuity. Schils and Wilson (2006) used Aqua/MODIS 3-monthly averaged SST data in an effort to explain an abrupt macroalgal turnover around the Arabian Peninsula. Their results pointed to a threshold of 28°C, defined by the average of the three warmest seasons, explaining diversity patterns of the seaweed floras across the entire Indian Ocean. They stressed that a single environmental factor can thus dominate the effect of other potentially interacting and complex variables. On the other hand,

Table 3. Current and future environmental variables retrievable from satellite data on a global scale.





Sea Surface


4 km (2 arcmin)


Temperature (SST)

Chlorophyll-a (Chl)


9 km (5 arcmin)



4 km (2 arcmin)




9 km (5 arcmin)


active Radiation (PAR)

Euphotic Depth


4 km (2 arcmin)


Surface winds

QuikSCAT/SeaWinds Scatterometer

110 km (1 arcdegree)


Various sources, assembled in ETOPO2

4 km (2 arcmin)



40 km



100 km


Graham et al. (2007) took several other variables in consideration to build a global model predicting the distribution of deepwater kelps. Their study was essentially a 3D mapping effort to translate the fundamental niche of kelp species, as determined by ecophysiological experiments, from environmental space into geographical space, based on global bathymetry, photosynthetically active radiation (PAR), optical depth, and thermocline depth stored in GIS. The latter was based on the interpolation of vertical profiles, whereas the former three variables were derived from satellite data sets (Table 3).

The latest development in distribution modeling approaches concerns several Species' Distribution Modeling (SDM) algorithms, also termed Ecological Niche Modeling (ENM), Bioclimatic Envelope Modeling (BEM), or Habitat Suitability Mapping (HSM). While the names are often mixed in the same context, a slight difference in meaning exists: the latter three are mostly based on presence-only data and predict the distribution of niches rather than actual species distributions, whereas the former involves presence/absence of input data and allows accurately predicting and verifying actual species distributions. Many different algorithms and software implementations exist (Maxent, GARP, ENFA, BioClim, GLM, GAD, BRT, but see Elith et al. (2006) for a review), but two fundamental properties are combined in these techniques, clearly separating them from the studies described earlier, which showed at most one of these properties. First, input data are a combination of a vector point file, representing georeferenced field observations of a species (as opposed to ecophysiological experimental data), on the one hand, and climatic variables stored in a raster GIS, on the other hand. The modeling algorithms then read the data out of GIS and use statistical functions to calculate the realized niche (as opposed to the fundamental niche; Hutchinson, 1957) in environmental space, subsequently projecting the niche back into geographical space in GIS. Second, instead of a binary identification of suitable and unsuitable areas, ENM output is a continuous probability distribution, which makes more sense from a biological point of view. Continuous probability maps may then be converted to binary maps using arbitrary thresholds. Additionally, ENM algorithms typically use several statistics to pinpoint the most important environmental variable in terms of model explanation, giving its percent contribution to the model output. Also, response curves can be calculated for the different variables, defining the niche optima.

However, care must be taken to restrict model input to uncorrelated environmental variables to obtain valid results. With a growing availability of (global, gridded) environmental data sets, which are often correlated or redundant, a data reduction strategy should be considered. One may perform a species-environment correlation analysis or ordination to make a first selection of relevant variables and perform a subsequent Pearson correlation test between environmental variables to get rid of redundant information. Alternatively, spatial principal component analysis (PCA) may be performed to obtain uncorrelated variables, using PCA components as input variables (Verbruggen et al., 2009), although the resulting variable contributions and response curves might be hard to calculate back to original variables.

Pauly et al. (2009) applied ENM using Maxent (Phillips et al., 2006) to gain insight into worldwide blooms of the siphonous green alga Trichosolen growing on physically damaged coral (Fig. 3). A correlation analysis was applied to

Figure 3. (a) A Pseudobryopsis/Trichosolen (PT) bloom on physically damaged coral. (b) Worldwide occurrence points of PT on coral. (c) Environmental grids used for model training in Maxent. (d) Relative importance of each variable in the model as identified by the algorithm. (e) Response curve of PT to the average of the three warmest months. (f) Binary habitat suitability map for PT. The gray (blue) shade represents suitable environment, whereas the dark (red) shade along the coast delineates bloom risk areas.

Figure 3. (a) A Pseudobryopsis/Trichosolen (PT) bloom on physically damaged coral. (b) Worldwide occurrence points of PT on coral. (c) Environmental grids used for model training in Maxent. (d) Relative importance of each variable in the model as identified by the algorithm. (e) Response curve of PT to the average of the three warmest months. (f) Binary habitat suitability map for PT. The gray (blue) shade represents suitable environment, whereas the dark (red) shade along the coast delineates bloom risk areas.

identify the two least correlated biologically meaningful variables derrived from SST and Chl (based on monthly data sets), adequately describing the position and extent of the distribution in environmental space. The model delineated the potential global distribution of Trichosolen occurring on coral based on a 95% training confidence threshold, including areas where the bloom had previously occurred. This allowed identifying areas with a high potential risk for future blooms based on environmental response curves. For instance, the response curve for the average of the three warmest months (included as a variable based on the conclusions of Schils and Wilson (2006)) shows that Trichosolen populations are only viable above 22°C, but only environments above 28°C are likely to sustain blooms.

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