Ecological Models to Infer the Quantitative Relationship between Land Use and the Aquatic Macroinvertebrate Community

Land-use changes influence the ecological water quality. In spite of this knowledge, land-use information is often missing in ecological water quality studies. Therefore, in the present research, we selected 39 peer-reviewed model-based scientific papers that study the relationship between land use and aquatic macroinvertebrates. From the selected papers, we found that certain water bodies responded more to local land use, while other water bodies were more likely to be affected by catchment land use. Hence, combined land-use information from both the local scale and the catchment scale will provide a better understanding of the impact of land-use changes on the ecological water quality. To gain this knowledge, efforts need to be taken to acquire land-use information from field observations and remote sensing or a geographic information system (GIS) data source. Furthermore, we concluded on the benefits of using models to better understand the relationship between the ecological water quality and environmental variables. Depending on the aim of the study and the nature of the data, researchers can select the most suitable model to ensure fast analysis.


Introduction
Anthropogenic activities that are taking place upstream and in the surrounding surface waters can influence the water quality by altering its physico-chemical and hydromorphological characteristics [1,2].There is a clear link between land use and water quality, either positive or negative.For example, urbanization, industries, and intensive agriculture activities may increase erosion and sediment accumulation [3][4][5][6], increase the input of chemicals, such as nitrogen and phosphorus [4,5,7,8], and create a more homogeneous flow and bed substrate of streams [3,9].The impact of land-use changes due to agriculture can be minimized by reducing the use of agrochemicals, e.g., in small-scale farms [10], or by applying crop rotation and conservation tillage systems [11,12].The impact that land use poses on surface waters is not limited to river ecosystems [13,14], but also affects ponds [15] and lakes [16,17].The change in the physico-chemical and hydromorphological characteristics of the impacted river or catchment will consequently affect the richness and abundance of aquatic organisms, such as fish [18,19], macroinvertebrates [20,21], and plants [22,23].
Despite the clear linkage between land use and water quality, land use was not always included in water quality studies.Many studies relating water quality and aquatic organisms only focused on water quality variables, such as physico-chemical characteristics and hydromorphological conditions [24,25].
During the search, we found that several papers had studied ecological water quality based on macroinvertebrates and diatoms.As our primary focus and expertise was on aquatic macroinvertebrates, we excluded papers that solely dealt with diatoms.Using 'land use' as key word(s) in the title resulted in 15 papers, while using 'model*' as a key word in the title resulted in 28 papers (Figure 1).Note that four papers among these 28 papers were also listed in the 15 papers.Hence, in total 39 (= 15 + 28 − 4) papers were retained that covered a wide range of internationally available studies related to our objectives.The papers were assessed in terms of input variables included in the models, spatial scale of land-use information, ecological community that is assessed, biological index used, type of ecological model, and country of study.A strengths, weaknesses, opportunities, and threats (SWOT) analysis was utilized to evaluate the use of models in ecological water quality studies and the inclusion of land-use information in the analysis.Finally, the methods were compiled to provide a recommendation for worldwide studies, especially in developing countries.

Introduction
Most of the 39 papers used macroinvertebrate data identified up to family level (19 papers, Table 1).Seventeen papers used macroinvertebrate data up to species or genus level for most taxa and up to family level for the remaining taxa, while two papers only used order level and one paper did not mention the level of identification.Macroinvertebrate data were collected either from national/regional databases (20 papers) or during tailor-designed sampling campaigns (19 papers).Macroinvertebrate sampling was done mainly using the kick-net method (13 papers) or the surber method (4 papers), while 2 papers did not mention the type of sampling they performed.Several papers studied macroinvertebrate data based on taxa richness (16 papers), using various biotic indices (17 papers), or various diversity indices, such as Simpson's diversity and the Shannon-Wiener index (2 papers), or a combination of biotic and diversity indices (4 papers).In one paper, the authors performed their assessment based on biological, physiological, and ecological macroinvertebrate traits [13], while in two other papers, the assessment was based on the functional feeding group [55,56].The papers were assessed in terms of input variables included in the models, spatial scale of land-use information, ecological community that is assessed, biological index used, type of ecological model, and country of study.A strengths, weaknesses, opportunities, and threats (SWOT) analysis was utilized to evaluate the use of models in ecological water quality studies and the inclusion of land-use information in the analysis.Finally, the methods were compiled to provide a recommendation for worldwide studies, especially in developing countries.

Introduction
Most of the 39 papers used macroinvertebrate data identified up to family level (19 papers, Table 1).Seventeen papers used macroinvertebrate data up to species or genus level for most taxa and up to family level for the remaining taxa, while two papers only used order level and one paper did not mention the level of identification.Macroinvertebrate data were collected either from national/regional databases (20 papers) or during tailor-designed sampling campaigns (19 papers).Macroinvertebrate sampling was done mainly using the kick-net method (13 papers) or the surber method (4 papers), while 2 papers did not mention the type of sampling they performed.Several papers studied macroinvertebrate data based on taxa richness (16 papers), using various biotic indices (17 papers), or various diversity indices, such as Simpson's diversity and the Shannon-Wiener index (2 papers), or a combination of biotic and diversity indices (4 papers).In one paper, the authors performed their assessment based on biological, physiological, and ecological macroinvertebrate traits [13], while in two other papers, the assessment was based on the functional feeding group [55,56].The compiled papers suggested that more studies addressed urban and industrial land uses.Moreover, urban and industrial areas pose more negative consequences toward aquatic ecosystems (seven papers), compared to agricultural areas (five papers).A combination of agricultural and urban was also considered to negatively influence the aquatic ecosystems (three papers, Table 2).This result corroborated with the report published by the United Nations Environment Program (UNEP, Table A1).The UNEP has published a list of economic activities with their effects on aquatic ecosystems where industries were identified to pose the most threats toward aquatic ecosystems [89].However, many papers only included land-use information to support the analysis but did not specifically study land-use effect on the aquatic ecosystems.Moreover, several papers did not mention land-use classification following the typical classification system (e.g., urban, agricultural, and forest).Depending on the purpose of the study, land use was sometimes classified into more detailed classes (e.g., heavy and light pastoral [77]).
Studies on the effect of land use on ecological water quality in developing countries are still limited.From the 39 selected papers, only eight studies were performed in developing countries (Table A2).Four of these studies were performed in South America, three studies were done in Asia, and one study was done in Africa.However, it is possible that most studies in developing countries have been published in local journals that are not accessible via the Web of Science portal.
Most of the 39 studies mainly focused on the local or riparian scale, and only 25% of the papers studied land-use effects at both local or riparian and catchment scales.Among the 39 papers, only two papers included land-use change (temporal aspect, Table A2) and five papers studied effects of land-use change by creating a scenario of future conditions (Table A2).
The land-use information is collected in different ways.In addition to the conventional way of field observation, other observation methods and data sources for acquiring land-use data exist (Table A3).For example, land-use data have been collected via remote sensing [62,85]; GIS sources [67,80], an available national database; or a combination of the methods and sources (Table A3).A national database and a GIS can be available in various forms, e.g., shape file and digital map; however, this was not always specified in the selected papers.Hence, both were considered as separate sources in Table A3.By combining different methods and data sources, the area coverage of land-use information can be enlarged beyond the dimensions of field observation.
As explained by Kuemmerle et al. [90], the limited availability of comparable land-use data is due to varying land-use categories between disciplines.Another reason is that adequate approaches to quantify land use and integrate various data sources are often missing.The problem is observed more in developing countries, where sometimes countries lack consistent data collection and data-sharing frameworks among institutions [90].Scenario crop rotations Guse et al. [81] Mixed use (combination of agricultural, residential, forest, etc.) Carlisle and Hawkins [75] Water 2018, 10, 184 7 of 25

Local or Riparian Land-Use Scale
Most of the selected papers included land-use information at local or riparian scale as this information can be relatively easily collected through field observations during a dedicated sampling campaign [58,60,73] (Table 3).Here, we considered a riparian zone (as described by Crétaz and Barten [91]) to be comprised of a stream valley and terrace slope, including stream channel, floodplain, and parts of adjacent uplands where aquatic and terrestrial ecosystems interact.A riparian zone acts as storage for flood waters, organic material, and nutrients that are transported from uplands to streams.However, the function of a riparian zone varies according to residence time of pollutants in the buffer, the thickness of the unsaturated zone, and the upland land use [91].Having defined our view on the riparian zone, note that in scientific literature the term local was sometimes used for riparian land use.Therefore, in this review we have combined local and riparian into one scale.Here, we provide examples of scales from selected papers and other land-use-related studies (Table 3).Several studies have confirmed the importance of local land use on the water quality [61].For example, Sanchez et al. [69] studied the importance of urban and Hawkins and Yuan [83] studied the influence of agricultural areas where human interventions are generally expanded until the edge of the streams.However, many studies included the information of local land use but did not specifically assess its potential effects on the water quality [78,82] or did not find its importance on the ecological water quality after analyses [79].

Catchment or Regional Land-Use Scale
The effect of land use at catchment scale has not been studied as much as the impact of land use at local or riparian scale (only seven out of 39 papers studied it), despite the potential impact that land use at catchment scale poses on the ecological water quality.Since the area coverage of a catchment can be relatively large (i.e., of a large river), it requires relatively more time and human resources to assess the land use through field observation.Remote sensing via satellite images and aerial surveys [77] and available GIS data [31] are common methods and sources in assessing the catchment land use.The scale of catchment land use varies and is not always mentioned (examples in Table 3).Some studies did not classify the catchment land use or did not study specifically its effects on the ecological water quality [59].However, Carlisle and Hawkins [75] and Carlisle and Meador [76] successfully defined land-use effects at catchment scale on the macroinvertebrate.They found the degree of land-use effects following a sequence of land-use classes: mixed land use and urban were reported to have the most adverse effects, whereas forests posed a positive effect.Lastly, Woznicki et al. [71] assessed and classified the catchment land use.However, their study did not assign a key importance to land use and therefore they focused on water quality variables instead.

Recommendation for Integrated Local or Riparian and Catchment or Regional Land-Use Scales
Since the effectiveness of local or riparian areas to store flood waters, organic material, and nutrients depends on the catchment's characteristics and regional climate [91], studies on the impact of land-use changes on aquatic communities should integrate both local or riparian and catchment land-use information.For example, Lowrance et al. [105] studied the effectiveness of a riparian forest buffer at the Chesapeake Bay watershed based on nutrient transport from an agricultural watershed into the coastal plain and the Chesapeake Bay.The diverse and complex relation between local or riparian and catchment land-use scales was the reason why 11 out of 39 papers studied the impacts of land use at both riparian and catchment scales.The complementary benefit of combining both land-use scales can be seen from the studies done by Weigel [56] and Cortes et al. [13].Weigel [56] found out that the influence of each scale to determine macroinvertebrate distribution was dominant at certain parts of his study area, but not exclusive of each other.However, Van Sickle et al. [88] found out that riparian land use explained the land-use impacts better than catchment land use, while Sheldon et al. [70] concluded the opposite.
When field observation and either remote-sensing observation or GIS data are combined, land-use data become more informative and area coverage can be enlarged more than what is possible through field observation alone.In the future, more land-use data will become available for developing countries through open source data, especially with the improvement of satellite images, aerial surveys, and digital data globally [106].For example, Baltazar et al. [60] could access the land-use data of the Niyugan River Sub-watershed, The Philippines, through Google Earth; while Moreno et al. [68] accessed the land-use data of the das Velhas River, Brazil, through digital cartography data.Similarly, remote sensing was done using Google Earth and the GIS data were accessed from the Ministerio de Agricultura, Ganadería, Acuacultura y Pesca (MAGAP) of Ecuador to collect land-use data of the Guayas river basin, Ecuador (unpublished paper).This way, developing countries nowadays have some modest initial access to land-use data and thus have the possibility to improve their ecological water quality studies in relation to land use.For future studies, we recommend combining field observations, remote sensing, and whenever possible GIS data sources for local or riparian land use.For catchment land use, remote sensing can be utilized and GIS data sources can be accessed.By combining methods and sources, land use can be quantified for both the local and catchment land-use scales.

Land-Use Change
Only two out of the 39 papers included temporal aspects of land use, and both papers had similar conclusions.Maloney and Weller [84] found that past land use occurring 50 years ago still influences the present day conditions of streams.Similarly, Schmalz et al. [41] also found negative effects of deforestation on the streams and aquatic ecosystems within a 30-year period.
Besides land-use change due to anthropogenic activities, water-quality variables may also change due to natural processes [91,107] and land-use change due to extreme events or natural disasters, such as climate change, floods, fires, and earthquakes [108][109][110][111].For example, an increase in ammonium-N and nitrate-N concentrations of the Swedish streams and a decrease in aquatic macroinvertebrate richness and abundance were observed after a flashflood event [112].Another example is wildfire together with post-wildfire rainfall on riparian vegetation.Besides altering microclimatic conditions, increasing runoff, and enhancing erosion, wildfire and post-wildfire rainfall may consequently decrease the richness and abundance of aquatic biota [113].
However, data on past land-use changes are often not available or not stored compared to the current day situation and in these cases the effect of land-use change is difficult to quantify.The poor availability of land-use change information is probably the reason why several studies used land-use Water 2018, 10, 184 9 of 25 scenarios to study land-use impacts using the current situation but without information of past land use [40,62].Indeed, the need for land-use change information depends on the purposes of the studies and is not necessarily required when the study purpose is to assess the effect of current land use.We recommend local and regional government in the developing countries to store their land-use information.Data from past or current surveys and projects should be added to local or regional databases, and the databases need to be updated and completed for other parts of the country.To update their land-use data, developing countries can also access global databases that are continuously developing and are freely available (e.g., GRASS GIS [114]).To be able to track and study changes (e.g., in the perspective of climate change or agro-economic developments), it is important to have both historical and recent data available in these databases.

Input Variables
When studying the impact of land use on macroinvertebrates, different types of input variables were used in the models of the selected papers (Table 4).Geomorphological variables (e.g., elevation, river banks, and sediment type) are the most common type of variables being used in ecological water quality studies (37 papers), followed by physico-chemical (e.g., nutrients and pH; 35 papers) and hydrological variables (e.g., annual discharge and flow; 23 papers).Geomorphological and hydrological variables can be gathered via field observation and in situ sampling.Both geomorphological and hydrological variables can provide information on anthropogenic alteration on the water body.Physico-chemical variables are easily changed within a short period of time; therefore, the change in water quality can be relatively easily detected based on long-term data originating from regular monitoring campaigns.Such long-term data-series are also required to unravel the variability due to land-use changes from the natural variability of the aquatic ecosystem.Some authors were interested in studying certain types of variables only; however, most papers combined different types of variables (Table 4).

Ecological Models
The selected papers used different mathematical and statistical techniques to identify, assess, and quantify the effect of land-use changes on the aquatic community (Table 5).Both multivariate techniques and decision trees have been often used to predict the presence of macroinvertebrate taxa based on environmental variables.Several papers used more than one model from the same technique or a combination of different types of models in their analyses (Table 5).Multivariate analyses were most often used to study the relationship between water quality and environmental variables.Multivariate analyses are useful in analyzing the structure or pattern in the data together with the contributions of the variables.These techniques are useful for a dataset that contains a large number of variables [115][116][117].Ordination, a common multivariate technique, integrates regression and permutation methods and provides easy-to-read graphical outputs [116,117].Due to their relative simplicity, they have been often used in ecological water quality studies.For example, Carlisle and Meador [76] used multiple discriminant analysis, Feio et al. [79] used multi-dimensional scaling and stepwise multiple discriminant function analysis, and Mantyka-Pringle et al. [67] used principal components analysis.Some disadvantages of these techniques are that the outputs can be difficult to interpret and that associations among variables and distribution patterns do not inherently imply causality [118].
The second-most frequently applied methods in the selected papers are regression-based techniques, comprising linear, polynomial, multiple, and non-linear regression.Regression analysis estimates parameter values and standard errors of a given dataset by analyzing the relationship between the response and the explanatory variables [116,117,119].From the selected papers, partial least square regression was used to analyze the ecological water quality of the Flint River watershed in Michigan, USA, by Abouali et al. [57], while a generalized linear model was used to study the water quality of Alto Minho region, Portugal, by Hughes et al. [40].Linear and logistic regression techniques are useful to develop a precise and concise model from a large dataset.However, linear regression cannot handle missing values, while logistic regression will divide variables with missing values into classes [120].
Other types of ecological models that are commonly applied in ecological water quality studies are decision tree models based on classification and regression trees (CART).Decision tree models are simple techniques that can provide a clear structure of the data having many explanatory variables and the type of interactions between variables.The basic principle of decision trees lies in its binary recursive partitioning, which is splitting the data along coordinate axes of the explanatory variables.Classification trees are applicable when the response variable is nominal, while regression trees are applicable when the response variable is continuous [116,117,121].Decision trees are also able to deal with relatively small datasets [122].For example, Dahm and Hering [54] utilized boosted regression tree to identify the recolonization of source sites for fish and macroinvertebrates in Germany, while Lock and Goethals [87] used classification trees and random forest to predict the occurrence of Plecoptera in Belgium.Despite their simplicity and ability to deal with datasets containing many variables, decision trees are not robust and should be avoided when there are only few observations in the data [120].
A combination of different model types, the so-called ensemble methods, was also proven to be beneficial in ecological water quality studies.Alemneh et al. [58] combined multiple regression analysis and canonical correspondence analysis to identify environmental disturbance affecting macroinvertebrate communities in the Upper Blue Nile, Ethiopia.Analysis of covariance, random forest, and boosted regression tree were utilized by Clapcott et al. [77] to predict the expected reference condition for macroinvertebrate communities in New Zealand.Stepwise linear regression in combination with adaptive neuro-fuzzy inference systems were used to define the relationship between macroinvertebrates and environmental variables in Saginaw River watershed, USA [62].Depending on the purpose, the application of ensemble methods can improve the quality of the results.

Recommendation for Statistical Analysis and Model Selection
Researchers studying the effect of land-use changes on the ecological water quality can rely on a myriad of ecological models or statistical analyses.The selection of the type of analysis to be used depends on the nature of the data (the type of response and explanatory variables) and the aim of the study.Model selection can also depend on the experience of the modeler because no model can be considered as the best option in every situation [122].In a regression-based model, the selected model should fit best to the data and produce the least unexplained variation, while bearing in mind the parsimony principle and that all model parameters are statistically significant.Several models may explain a given dataset equally well, while in other cases no single best model can explain a dataset [116,117].The provided guidelines here on data exploration and model selection serve as a recommendation on how analysis can be done in ecological water quality studies.
Zuur et al. [123] have formulated a scheme for various data exploration techniques, which is a very important step before applying a model (Table 6).Not every dataset requires each step, because different models require different assumptions.Without having the ambition to give a full overview on how to perform a data analysis (for that we refer to specific books, e.g., Witten and Frank [124] and Zuur et al. [117]), a process, for example a histogram analysis, is not required prior to principal component analysis (PCA).Similarly, normality and homogeneity do not need to be checked before developing regression models, since normality and homogeneity can be verified using the residuals produced by the regression models [123].When the aim of the study is only to understand the data, standard inferential statistics can be applied to get the statistics of the data [124].In many cases, we also need to understand the structure and the underlying causal relationship of the data (descriptive methods) or to find an association and make predictions for future observations (predictive methods).Prior to modelling, the aim of the study must be specified to optimize the criterion of interest.Since both descriptive and predictive methods have a statistical background, a model will possess some level of explanatory and predictive accuracy [124,125].Therefore, both explanatory and predictive qualities of the models need to be retained and reported [125].Here, we provide the classification (Table 7) and comparison (Table 8) of various descriptive and predictive models based on Tuffery [120] to help in selecting an appropriate model for analysis.Table 8 summarizes the advantages and disadvantages of descriptive and predictive modelling in terms of the required assumptions regarding the problem to be solved, the capacity of the model in treating the data exhaustively within a reasonable period for all cases, and the possibility of the model to handle heterogeneous or incomplete data [120].For a more detailed explanation on a specific method, we refer the readers to Tuffery [120], Van Echelpoel et al. [122], Berk [121], and Zuur [126].
Table 7. Classification of descriptive and predictive modelling and purposes/examples of using them; grey background shows methods that integrate statistics and data analysis [120]; PLS: partial least squares, (M)ANOVA: (multivariate) analysis of variance, (M)ANCOVA: (multivariate) analysis of covariance.Finding predictors for macroinvertebrate composition [13] Correspondence analysis (CA), multiple correspondence analysis (MCA) CA to understand the distribution of macroinvertebrate taxa among sites [127] Cluster analysis Partitioning methods (moving centres, k-means, dynamic clouds, k-medoids, etc.) Classifying reference sites [82] Hierarchical methods (agglomerative, divisive) Macroinvertebrate classification into biologically similar groups [76] Cluster analysis + dimension reduction Neural clustering (Kohonen maps) Determining macroinvertebrate distribution [128] Combinatorial models Clustering by aggregation of similarities

Models based on mathematical functions
Neural networks Supervised learning networks (perceptron, radial basis function network, etc.) Predicting macroinvertebrate occurrence based on environmental variables [129] Parametric or semi-parametric models Continuous dependent variable: linear regression, ANOVA, MANOVA, ANCOVA, MANCOVA, general linear model (GLM), PLS regression ANOVA to determine differing average values among steams [75], PLS to refine selection of predictors after PCA [13] Qualitative dependent variable: Fisher's discriminant analysis, logistic regression, PLS logistic regression Discriminant analysis to select environmental variables estimating probability of a site belongs to a group [76] Count dependent variable: log-linear model Continuous, discrete, count or qualitative dependent variable: generalized linear model (GLM), generalized additive model (GAM) GLM to identify and quantify interactions between drivers and response variables [40] Prediction without model Probabilistic analysis k nearest neighbours Predicting macroinvertebrate presence in a river [130] Table 8.Comparison of methods based on Tuffery [120]; CHAID: Chi-squared automatic interaction detector.Another modelling type is mechanistic modelling, that derives the relationships between significant variables based on theories and principles that govern the studied system.The resulting model is given in mathematical equations.Examples for surface water are modelling of discharges from a wastewater treatment plant, industries, and storm water; agricultural/urban runoff; and a food chain [131].Paillex et al. [132] and Schuwirth et al. [49] showed the use of such mechanistic models in ecological water quality studies.Mechanistic models allowed them to understand the mechanism behind the presence of taxa based on a combination of traits and environmental conditions [49,132].Nevertheless, these mechanistic models have disadvantages.Besides the required long process in building such a mathematical model, there is no guarantee that the mechanistic explanation of the model is correct [131].Especially in ecological studies, the available trait information that is necessary in a mathematical model might not be complete, and there is a possibility that an important variable required to understand the system is missing [49,132].With the complexities and uncertainties of aquatic ecosystems, it is not surprising that this technique is not as popular as descriptive and predictive models.However, it is not our intention to provide a lengthy discussion on mechanistic models.For those interested, we refer to Nirmalakhandan [131].
For practicality, we also list some guidance on typical ecological models based on the nature of the response variable (Table 9), adapted from Guisan and Zimmermann [133].The most important strength of using ecological models is time saving for analysis, despite the possible large number and various types of input variables in the studies (Table 10).Second, researchers can use models to test hypotheses, to understand a studied system, and to define further research [93].Ecological models can be used to conceptualize the relationships in ecosystems and, despite their limitations, they allow researchers to integrate expert knowledge in the modelling process, which in its turn is beneficial for management purposes.The third strength of using ecological models is that they can be used for any land-use scale or for a specific land-use type.Fourth, when land-use information is included in the models, a certain stressor can be related or traced to its source and the degree of its effect on the water bodies can be estimated.Moreover, as a categorical variable, land-use information can be easily quantified during a dedicated sampling campaign without specific equipment.Lastly, the ecological models are also widely applicable in terms of the methodology and results, and could facilitate communication between researchers and the public (e.g., studies by Van Sickle et al. [88], Alvarez-Mieles et al. [134]).In the present research, we have identified several weaknesses of the use of models related to land use.First, due to the complexity of environmental processes, there is no model that can perfectly explain all environmental processes as a whole [133] and pre-analysis may be required to select an appropriate model.Second, models can simplify the selection process of model variables, which might result in final model containing variables that are less suitable based on general ecological knowledge.Third, since current ecological models can accommodate more input variables, sampling campaigns might require higher budgets to collect more data.Yet, financial means were not to be discussed in the current research.Fourth, available land use and land-use change information that can be collected via remote sensing and other sources is still lacking, especially in developing countries.Fifth, land-use data is not regularly updated, thus any possible land-use change and its effects are unknown.Besides, not all countries have all their land use registered, and in some cases the land use is recorded only when a specific sampling campaign is taking place.Lastly, there is no consensus regarding land-use assessment methods and their scale effects.Hence, studies on land-use effect are still lacking.
The first identified threat regarding the use of ecological models in ecological water quality studies is possible over-or under-fitting of the models compared to reality.This goes hand-in-hand with the nature of ecological models that over-simplify the reality [133].The second threat is the use of less-appropriate models that may provide misleading results.Moreover, due to the ongoing development of models, researchers without sufficient knowledge in modelling might use more recent models instead of older ones which might threaten the proper use and proper selection of the models.Third, there is over-(where researchers accept the results of the models even though not all variables contained in the models are ecologically suitable) or under-reliance of models' results (where the results of the models are not accepted to support decision-making).Fourth, due to the lack of availability of land-use data to be accessed via remote sensing and other sources, sometimes researchers had to use outdated data that might not be useful in the analysis or may give a misleading result.After some time, a model also might not be applicable anymore on the area where land-use data were collected to develop the model, because land use tends to change quickly.Lastly, over-simplification of the land-use classifications to be included in the model may shield the real land-use effects in the model's results.
Despite the abovementioned weaknesses and threats, we have two main opportunities for using ecological models in studying land-use impacts (more detail in Table 10).The first obvious opportunity is related to model development.Model development to improve a model's applicability is ongoing, for instance via involving potential users from an early stage of the development process.Moreover, there are various models available for different ecological study purposes.Thus, the qualities and quantities of collected variables are also improved.Continuous model development is also supported by ongoing capacity building in both developed and developing countries.Free software, such as R [135], has also been developed to support modelling activities and is accessible worldwide.The second opportunity is related to land-use information.Nowadays, researchers are aware that land-use change has a potential anthropogenic impact on the aquatic system, and should be included when assessing multiple stressors conditions.Moreover, land-use data can be gathered in various ways, such as during a sampling campaign and by accessing the global databases (e.g., GRASS GIS [114]), thus increasing the availability of land-use information.New technologies, such as the use of drones to record land-use data, are promising and cost saving compared to a common manned-aircraft survey [136].

Conclusions
Land use can highly influence ecological water quality, but its information is often not included in ecological water quality studies.Since land use can influence the ecological water quality and it can change quickly, it is recommended to include land-use information in ecological water quality studies on both local and catchment scales.Various methods and sources to collect land-use information are available and are continuously developing; therefore, efforts need to be taken to collect land-use data through field observation, remote sensing, and other sources.Moreover, prior to selecting the most appropriate type of ecological models, one should exactly know what the aim of the study is, how the related research hypothesis is formulated, and what type of data are available.Despite models' limitation in explaining environmental processes as a whole, models can support a fast and quantitative analysis, especially when the influence of many variables needs to be evaluated.Developing countries can benefit from huge opportunities of using various ecological models to integrate land-use information in ecological water quality studies to support their decision-making.

Figure 1 .
Figure 1.Scheme of search category and key word(s) for paper selection in the Web of Science and the number of resulting papers.

Figure 1 .
Figure 1.Scheme of search category and key word(s) for paper selection in the Web of Science and the number of resulting papers.
trees to define trait and tolerance values that distinguished taxa presence[75]

Table 2 .
Effects of land use based on the selected published papers.

Table 3 .
Various scales in quantifying land use at local or riparian and catchment scales from selected papers and other land-use-related studies: unless otherwise mentioned, the local scale is not described as length, width, or radius; scale is given as length × width.

Table 4 .
Type of input variables.

Table 10 .
Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for the use of models in studying land-use impacts on ecological water quality focusing on macroinvertebrates.