Model application

A positive temporal trend in both habitat suitability and macrophyte occurrence was expected due to improved management and dispersal. Optimised models were applied to the original (imputed) data set to infer macrophyte-specific habitat suitability scores for all sampled sites. Discretisation of the Habitat Suitability Index (HSI) scores followed threshold identification via minimising the absolute sensitivity-specificity difference and subsequent temporal grouping to derive annual prevalence (predicted number of suitable sites divided by the total number of sites). Observed and predicted annual prevalence were compared to infer (i) the temporal trend of macrophyte prevalence and (ii) the potential macrophyte presence.

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157 To mimic the potential effects of changing abiotic conditions on habitat suitability and illustrate the value of the constructed species-specific models towards management, six scenarios were developed. These scenarios represent three starting conditions (average, extreme and nutrient enrichment) and two management options (business-as-usual and focus on key variables), as mentioned in Table 7.1 and summarised in Table 7.2. The starting conditions were based on the observed environmental conditions in 2010 due to a lack of sufficient data from subsequent years. Moreover, observations were limited to the months April until September to limit seasonal bias within the temporal trends.

For each variable, the mean (𝒙̅) and standard deviation (s) were estimated (see Appendix, Table D.2) and used as a statistical basis for determining the three different starting conditions. First, the variable means were adopted when the starting conditions were defined to represent the average situation (𝒙̅; ‘AVG’ scenarios). Secondly, nutrient-related variable means were increased with two times the standard deviation to reflect eutrophic sites, representing the nutrient-enriched situation (𝒙̅ for non-nutrient variables and 𝒙̅ + 2 ∙ 𝒔 for nutrient variables; ‘NUT’ scenarios). Thirdly, variable means were increased with two times the standard deviation to reflect highly polluted sites, representing the extreme situation (𝒙̅ + 2 ∙ 𝒔; ‘EXT’ scenarios). Several exceptions were considered in the latter, as pollution is reflected differently within the included environmental variables. More specifically, temperature and pH were not changed (i.e.

𝒙̅) and oxygen (saturation) was decreased instead of increased (i.e. 𝒙̅ − 2 ∙ 𝒔). Actual values can be found in Appendix, Table D.3.

For each variable, specific end points were defined depending on the performed management activities. First, variable-specific temporal trends were used for deriving the average change rates for each individual variable, reflecting the business-as-usual situation (‘BAU’ scenarios). Secondly, partial dependence plots were used for identifying the key habitat descriptors and their associated optimal conditions, reflecting management with a focus on the main habitat descriptors (‘KEY’ scenarios). For these key variables, an exponential temporal pattern was assumed, while all remaining variables were assumed to follow the temporal pattern as defined in the BAU scenario.

The actual values can be found in Appendix, Table D.3.

Table 7.1: Assignment of scenario-specific codes. Business-as-usual management relies on the continuation of variable-specific historical trends, while management focusing on key variables considers the optimal values of partial dependence plot as management endpoints. Starting point conditions are derived from observation data gathered in 2010. A more detailed description of each scenario can be found in Table 7.2.

Average conditions Extreme conditions Nutrient enrichment

Business-as-usual AVG-BAU EXT-BAU NUT-BAU

Key variables AVG-KEY EXT-KEY NUT-KEY

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Table 7.2: Characterisation of management scenarios under different starting conditions. Information extends the codes mentioned in Table 7.1.

Code Description

AVG-BAU Baseline starting point with business-as-usual management.

Starting point of each variable represents the average value observed in 2010. Management entails no alterations towards the previous period, hence the same temporal trend is assumed. Trends were derived by fitting variable-specific linear models to the temporal data (see Figure D.3).

AVG-KEY Baseline starting point with management focusing on key variables.

Starting point of each variable represents the average value observed in 2010. Management entails variable-specific procedures being solely applied to the five key variables, with endpoints derived from the partial

dependence plots (see further).

EXT-BAU Extreme starting point with business-as-usual management.

Starting point of each variable represents the mean observed in 2010, supplemented with two times the standard deviation (𝒙̅ + 2 ∙ 𝒔). Variable-specific exceptions were considered, depending on the included variables.

Management entails no alterations towards the previous period, hence the same temporal trend is assumed. Trends were derived by fitting variable-specific linear models to the temporal data (see Figure D.3).

EXT-KEY Extreme starting point with management focusing on key variables.

Starting point of each variable represents the mean observed in 2010, supplemented with two times the standard deviation (𝒙̅ + 2 ∙ 𝒔).

Management entails variable-specific procedures being applied to the five key variables, with endpoints derived from the partial dependence plots (see further).

NUT-BAU Nutrient enrichment with business-as-usual management

Starting point of each nutrient variable represents the mean observed in 2010, supplemented with two times the standard deviation (𝒙̅ + 2 ∙ 𝒔).

Variable-specific exceptions were considered, depending on the included variables. Management entails no alterations towards the previous period, hence the same temporal trend is assumed. Trends were derived by fitting variable-specific linear models to the temporal data (see Figure D.3).

NUT-KEY Nutrient enrichment with management focusing on key variables.

Starting point of each nutrient variable represents the mean observed in 2010, supplemented with two times the standard deviation (𝒙̅ + 2 ∙ 𝒔). For all other variables, the starting point was represented by the average value observed in 2010. Management entails variable-specific procedures being solely applied to the five key variables, with endpoints being defined by the partial dependence plots (see further).

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159 The effects of the different scenarios on habitat suitability were subsequently assessed by applying the optimised macrophyte-specific models and deriving the suitability index. It should be noted that these scenarios were not developed to closely represent actual natural conditions and trends, but rather to illustrate the potential usage of the constructed models to assess scenario outcomes in function of the considered starting conditions. The obtained outcomes are meant to illustrate how management decisions can be steered by prevailing abiotic conditions.

Finally, the developed models were considered to contrast habitat preferences between two congeneric species. More specifically, occurrence observations of the native Lemna minor and the alien L. minuta (see Box 7.1) were confronted with predictions to determine (i) the ability of conditional random forest to identify suitable habitats for both Lemna spp. and (ii) whether the majority of the sites were more likely to support L. minor than L. minuta. It should be noted that the results have to be interpreted with care, as (i) data covered almost 30 years of sampling, (ii) pseudo-absences were used and (iii) L. minuta was relatively recently introduced (thus expected to violate the equilibrium assumption (Gallien et al., 2012)).

Box 7.1: Selection of Lemna minor and Lemna minuta

The freshwater system that is considered as baseline throughout this work is characterised by slow-flowing water and elevated nutrient conditions (see Section 1.2.1). These conditions strongly support the presence of floating macrophytes, including the free-floating duckweed species (Bakker et al., 2013; Zhang et al., 2017).

Among these duckweeds, Lemna minor frequently occurs in European surface waters, while Lemna minuta originates from North and South America and has reached a widespread status throughout Europe (Hussner, 2012). L. minor and L. minuta are morphologically similar and are often reported in the same locations, though their habitat preferences are not necessarily identical.

The development of species-specific models allows for distinguishing habitat preferences between these congeneric species and identifying the consequences of management on species-specific habitat suitability. Moreover, it can be used as an early-warning tool to locate sites with significantly higher HSI scores for the alien species compared to the native species. However, such applications merely illustrate preferences and suitability scores, while actual management decisions on avoiding species presence are to be made by the user.

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7.3 Results