Introduction

Human activities like fishing and shipping, agricultural and industrial runoff from land, and climate change profoundly change marine ecosystems around the world [1, 2, 3, 4, 5]. In turn, marine ecosystems heavily impacted by humans often stop to provide important services for people [6]. Maintaining or restoring good marine ecosystem condition has thus been the objective of recent environmental laws like the European Union’s Marine Strategy Framework Directive [7]. Reaching this goal, however, requires understanding how several anthropogenic stressors in combination affect marine ecosystems [8] and where problematic combinations of stressors occur.

Responding to this challenge, Halpern et al. [3] developed a simple additive model for mapping human impacts on marine ecosystems. They presented a global map of human impacts on marine ecosystems that was recently updated [9]. The model evaluates where human stressors (e.g. fishing with different gear types, shipping lanes, water pollution with different substances) overlap spatially with ecosystem types (e.g. seagrass meadows, soft material sea bottom) or important species that are sensitive to these stressors. It calculates a human impact index, as opposed to a measurable biological, physical or chemical quantity.

By now, the model has also been used for regional human impact assessments for e.g. the northern Hawaiian islands [10], the California Current region [11], the waters of British Columbia [12, 13], the Baltic Sea [14, 15], the waters off Masachusetts [16], the eastern North Sea [17] and the Mediterranean and Black Seas [18]. Variations of the model have been used to assess human impacts on individual benthic habitat types in the Baltic Sea [19] and on marine top predators in the California Current region [20]. While the model has been developed and mostly used by the marine science and conservation community, similar additive human pressure models have been used for freshwater and terrestrial systems [21, 22].

Thus, Halpern et al.’s model is well established, but potential model users face technical challenges:

1. The model is often implemented using off-the-shelf Geographical Information System (GIS) software or a spreadsheet calculator like Microsoft Excel. This process is labour intensive, prone to making mistakes, requires a high level of technical skill, and in some cases relies on expensive commercial software.
2. The model requires spatial data for all important stressors and ecosystems in the study area. While more and more relevant data become available, most studies using the model report that key data were missing or of inadequate quality. Once better data become available, the complicated implementation with general-purpose software makes it difficult and time-consuming to update the model outputs.
3. Using the model requires many small data processing decisions that may influence results, but are often undocumented. For example, resampling methods used to represent input data with different coordinate reference systems and spatial resolutions on the same regular grid are rarely described. While many authors using the model publish most of the pre-processed input data they use, often some input data are only available “raw” (if at all) from original sources. This makes it impossible to exactly reproduce the results of model applications. Indeed, as part of the testing process, the software described here was used to reproduce three published human impact maps [11, 15, 18]. While the resulting maps were very similar to the originally published ones (R²>0.9 in all three cases), exact reproduction was not possible.
4. Research teams often have a dedicated contributor to take care of the technical aspects of using the model. Other members of the research team, as well as external users of model results (e.g. managers making decisions based on an existing human impact map) may lack the access to commercial software and the technical skills to visualize the input data and test alternative modelling decisions, thus missing important opportunities to understand and double-check the model results

The software presented in this paper, in the following called the EcoImpactMapper, implements Halpern et al.’s model with a simple user interface and helps address these four challenges. It has been used to map the potential for human impacts in Arctic marine waters west and south of Greenland [23].

Implementation and architecture

The additive model as suggested by Halpern et al. [3] uses three kinds of input data:

• D_i: Spatial distribution of stressors, such as fishing effort or shipping intensity, as regular grids. Stressor data are log(x+1)-transformed and rescaled so that the maximum is 1. This transformation and rescaling can optionally take place in the EcoImpactMapper.
• e_j: Spatial distribution of ecosystem components as regular grids, e.g. continental slope soft bottom habitat either as presence (1) and absence (0) or, in some cases [17, 20], continuous data like probabilities of presence.
• μ_i,j: Sensitivity weights” numerically representing the sensitivity of ecosystem component j to stressor i. These weights are typically derived by expert judgment.

For n stressors and m ecosystem components, the model estimates a human impact index (I_Sum) at location (x, y) as:

The EcoImpactMapper (Figure 1) was implemented in Java with NetBeans IDE 7.3. The GUI is based on Swing and was designed with Matisse. Table 1 shows an overview of the software’s main classes.

It should be noted that the EcoImpactMapper was designed to simplify a time-consuming and difficult part of producing human impact maps with Halpern et al.’s model. It was not designed to replace general-purpose Geographical Information System (GIS) software, or to support the whole mapping process from raw data acquisition to publication-quality visualization. For example, the EcoImpactMapper can handle only one input data format. All spatial input data must have the same spatial resolution, extent and coordinate reference system. Furthermore, there are only basic visualization functions: For example, there are no functions to zoom, read values from the data at a given location, or produce publication-quality maps. The EcoImpactMapper is thus not a stand-alone tool, but must be used in combination with other software for data preparation and for creating high quality maps. Figure 2 shows the general workflow for creating human impact maps with the EcoImpactMapper.

All spatial model inputs and outputs are comma-separated value (CSV) files representing regular grids. Each input file must contain at least three columns: X coordinates, Y coordinates, and values for the respective locations (e.g. stressor intensities or presence-absence data for ecosystem components). Also sensitivity weights are loaded from CSV files where rows represent stressors and columns ecosystem components.

The EcoImpactMapper allows users to choose individual pre-processing steps for all stressor and ecosystem component data. Currently, it only supports two optional pre-processing steps (suggested by [3]): Log(X+1)-transformation and rescaling so that the largest value in any data layer is 1. Users can visualize the effects of the selected pre-processing steps by switching between a “raw data” and a “processed data” view in the main window. Additional pre-processing functions will be added to the EcoImpactMapper in the future.

Once all input data are loaded, the EcoImpactMapper can calculate the following spatial outputs:

• Impact index ISum: see Eq. 1
• Ecological diversity index:
  
  (Eq. 2)

  $E_{Div}(x,y)={\sum }_{j=1}^m{e}_j(x,y)$

  M2 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {E_{Div}}(x,y) = \sum _{j = 1}^m{e_j}(x,y) \] \end{document}
• Unweighted stressor index:
  
  (Eq. 3)

  $S_{unweighted}(x,y)={\sum }_{i=1}^n{D}_i(x,y)$

  M3 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {S_{unweighted}}(x,y) = \sum _{i = 1}^n{D_i}(x,y) \] \end{document}
• Stressor index weighted with the mean of the sensitivity weights of all ecosystem components to the stressor:
  
  (Eq. 4)

  $S_{weighted}(x,y)={\sum }_{i=1}^n(D_i(x,y)\frac{1}{m}{\sum }_{j=1}^m{\mathbf{\mu }}_{i,j})$

  M4 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {S_{weighted}}(x,y) = \sum _{i = 1}^n({D_i}(x,y)\frac{1}{m}\sum _{j = 1}^m{{\bf{\mu}} _{i,j}}) \] \end{document}
• Mean impact (as opposed to the sum) over all ecosystem components present in location (x,y):
  
  (Eq. 5)

  $I_{Mean}(x,y)={\sum }_{i=1}^n{\sum }_{j=1}^m\frac{1}{E_{Div}(x,y)}{D}_i(x,y)e_j(x,y){\mathbf{\mu }}_{i,j}$

  M5 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {I_{Mean}}(x,y) = \sum _{i = 1}^n\sum _{j = 1}^m\frac{1}{{{E_{Div}}(x,y)}}{D_i}(x,y){e_j}(x,y){{\bf{\mu}}_{i,j}} \] \end{document}

Furthermore, the EcoImpactMapper can calculate the following non-spatial outputs:

• Each Di’s contribution to ISum or IMean in all grid cells in the study area. This allows ranking stressors based on their contribution to modeled impacts, a commonly reported result in studies using Halpern et al.’s model.
• % of each stressor occurring in the x% of the study area with the highest EDiv. For example, this function has been used to calculate that in western Greenland, about 54% of trawling (an important stressor) occurred in the 20% of the study area with the highest ecological diversity index [23].

Quality control

Calculations were tested using artificial data for three ecosystem components and two stressors in a 4x4 cell regular grid (Table 2). For this small grid, all outputs were calculated with the EcoImpactMapper and manually with Microsoft Excel. The results were identical.

To test the software’s functioning with large realistic data sets, publicly available stressor and ecosystem data from three published human impact assessments [11, 15, 18] were downloaded and converted to the software’s input data format. These assessments contain dozens of stressors and ecosystem components covering large international regions at high spatial resolution. While it was not possible to exactly reproduce these assessments’ results (in part because of missing data, in part because the papers did not provide all details of data pre-processing), the EcoImpactMapper, running on an off-the-shelf 2012 MacBook Pro with 16GB RAM, worked with these large data sets without problems.

Lastly, correct functioning on different operating systems was tested by running the EcoImpactMapper on Mac OS X 10.3.3, Windows 7 and Ubuntu 14.04.

Operating system

Platform-independent; requires Java Runtime Environment.

Programming language

Java 1.7.0

Additional system requirements

None, but working with large regions and high spatial resolution (e.g. the data from [11] and [18]) requires at least 16GB RAM.

Dependencies

None.

Software location

Language

English