Analysing Ecosystem Services – InVEST’s Carbon Module

In a previous post I discussed the growth of the concept of ecosystem services (ES), thanks in part to large scale studies such as the Millennium Ecosystem Assessment. It is undoubtedly an attractive concept to both conservationists and policy makers alike who can see its value in both promoting conservation policies and measuring their potential benefits (Goldstein et al. 2012).

The growing availability of software that is able to quantify and visualise the provision or value of ES has been crucial to its development as a cornerstone of how socio-ecological interactions are defined and analysed (Bagstad et al. 2013, Daily et al. 2009, Harmackova and Vackar, 2015).

An example of such software is InVEST (Integrated Valuation of Ecosystem Services and Trade-offs). InVEST will play a crucial part in the analysis for my thesis and is a widely used tool in the field of ES analysis. InVEST’s freely available models rely primarily on the input of geo-referenced land use / land cover (LULC) information combined with biophysical functions defined by the user. Some of its models are more complex and involve spatial analytical elements such as distance to potential threats (habitat quality) or flow direction (water quality) but InVEST’s carbon module is relatively simple. Each LULC class is assigned a carbon value for four pools (above and below ground biomass, soil and dead organic matter) and the total stores of carbon are aggregated based on the area of each class in the LULC raster (Sharp et al. 2016). While this might seem simple enough, the output of this model has been used in a wide variety of analyses, carbon storage and sequestration being one of the most studied and analysed ES (Ayanu et al. 2012). The table below outlines the diversity of applications of InVEST’s carbon module in the literature.

Author(s) How was InVEST used?
Sharps et al. (2017) Analysed ES provision from afforestation scenarios. Also compared the accuracy of InVEST with LUCI and ARIES, other examples of ES modelling software.
Bottalico et al. (2016) Quantified the potential impact of various forestry policies on timber production and carbon storage.
Cabral et al. (2016) Quantified the change in ES provision for a mixed urban / rural region based on past land cover change.
He et al. (2016) Combined InVEST with an econometric model of urban growth to analyse how urbanisation scenarios might affect carbon storage and sequestration.
Garrastazu et al. (2015) Modelled the potential impact on ES provision resulting from changes to legislation for vegetative riparian buffers.
Chaplin-Kramer et al. (2015) Modelled different spatial patterns of deforestation and used InVEST to assess ES provision of resulting land covers.
Harmackova and Vackar (2015) Modelled various conservation scenarios for a wetland landscape and assessed ES provision of the resulting landscapes.
Keller et al. (2015) The output of InVEST’s carbon module was used in a multi criteria analysis, selecting optimal sites for new shale gas wells.
Tao et al. (2015) Used InVEST to estimate carbon stocks along an urbanisation gradient.
Lawler et al. (2014) Analysed ES provision for national landscape change scenarios; modelled econometrically based on socio-economic drivers of change.
Bhagabati et al. (2014) Assessed ES provision for different landscapes resulting from various conservation scenarios for rare Sumatran tiger habitat.
Bagstad, Semmens and Winthrop (2013) Compared with output from ARIES in an assessment of the accuracy of ES modelling software.
Delphin et al. (2013) Assessed the potential damage hurricanes might cause to the timber industry and the ES of carbon storage.
Kovacs et al. (2013) Output from InVEST models used in return on investment calculations for society, based on potential landscape scale conservation initiatives.
Liu et al. (2013) Output included in a multi criteria analysis, defining priority areas for conservation based on their provision of ES.
Goldstein et al. (2012) Assessing the ES provision of future landscape scenarios in order to inform decision making for a private landowner.
Izquierdo and Clark (2012) Provided input to decision support software to aid in the prioritisation of conservation planning.
Bai et al. (2011) Used InVEST output in an assessment of the spatial relationship between ES and biodiversity.
Polasky et al. (2011) InVEST was used to quantify changes in ES, habitat quality and returns to landowners for LULC change in Minnesota between 1992-2001.
Nelson et al. (2010) InVEST output used to assess the impact of various 2000-2015 change scenarios on global ES provision.

There are of course limitations to InVEST’s carbon module. For one it is highly dependent on the scale and quality of the LULC data in the model as well as the accuracy of carbon pools used to calibrate it (Sharps et al. 2017). The field is aware of this and identifies the development of spatially explicit archives as a key goal in developing ES modelling (Bagstad et al. 2013, Chaplin-Kramer et al. 2015). Keller et al. (2015) directly counter this limitation, explaining that if InVEST’s output is used more as an indicator of the potential magnitude and direction of change in ES provision, then issues around the accuracy of model output can be somewhat overlooked. Unless the data that has parameterised the model is of exceptional quality using InVEST to quantify absolute values of ES may bring validity issues (Keller et al. 2015).

Other problems lie in the simplicity of InVEST’s approach to modelling the flux of carbon sequestration. Unless there has been no change in LULC class between years then the model assumes a stable state of carbon storage. This of course completely ignores important biogeochemical and ecological process that can affect the value and flow of carbon between pools (Cabral et al. 2016).

Despite these limitations InVEST remains a widely used toolkit for ES analysis. It is freely available and has relatively low data demands; lots of default biophysical values are even included in the models should the user wish to make use of them. It has been shown to improve stakeholder engagement and understanding in the concept of ES and positively effect decision making (Bhagabati et al. 2014). ES analysis is becoming a bigger part of policy and decision making. The use of easy to understand modelling tool kits that are simple to operate will be a major boon to conservation and sustainability especially as the users of these models refine and improve them (Bhagabati et al. 2014, Cabral et al. 2016).

 

References

Ayanu, Y.Z., Conrad, C., Nauss, T., Wegmann, M. and Koellner, T. (2012) ‘Quantifying and mapping ecosystem services supplies and demands: a review of remote sensing applications’, Environmental science & technology, 46(16), pp. 8529

Bagstad, K.J., Semmens, D.J. and Winthrop, R. (2013) ‘Comparing approaches to spatially explicit ecosystem service modeling: A case study from the San Pedro River, Arizona’, Ecosystem Services, 5, pp. 40-50.

Bai, Y., Zhuang, C., Ouyang, Z., Zheng, H. and Jiang, B. (2011) ‘Spatial characteristics between biodiversity and ecosystem services in a human-dominated watershed’, Ecological Complexity, 8(2), pp. 177-183.

Bhagabati, N.K., Ricketts, T., Sulistyawan, T.B.S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A. and Tallis, H. (2014) ‘Ecosystem services reinforce Sumatran tiger conservation in land use plans’, Biological Conservation, 169, pp. 147-156.

Bottalico, F., Pesola, L., Vizzarri, M., Antonello, L., Barbati, A., Chirici, G., Corona, P., Cullotta, S., Garfì, V. and Giannico, V. (2016) ‘Modeling the influence of alternative forest management scenarios on wood production and carbon storage: A case study in the Mediterranean region’, Environmental research, 144, pp. 72-87.

Cabral, P., Feger, C., Levrel, H., Chambolle, M. and Basque, D. (2016) ‘Assessing the impact of land-cover changes on ecosystem services: a first step toward integrative planning in Bordeaux, France’, Ecosystem Services, 22, pp. 318-327.

Daily, G.C., Polasky, S., Goldstein, J., Kareiva, P.M., Mooney, H.A., Pejchar, L., Ricketts, T.H., Salzman, J. and Shallenberger, R. (2009) ‘Ecosystem Services in Decision Making: Time to Deliver’, Frontiers in Ecology and the Environment, 7(1), pp. 21-28.

Delphin, S., Escobedo, F., Abd-Elrahman, A. and Cropper, W. (2013) ‘Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model’, Journal of environmental management, 129, pp. 599-607.

Garrastazú, M.C., Mendonça, S.D., Horokoski, T.T., Cardoso, D.J., Rosot, M.A., Nimmo, E.R. and Lacerda, A.E. (2015) ‘Carbon sequestration and riparian zones: Assessing the impacts of changing regulatory practices in Southern Brazil’, Land Use Policy, 42, pp. 329-339.

Goldstein, J.H., Caldarone, G., Thomas, K.D., Ennaanay, D., Hannahs, N., Mendoza, G., Polasky, S., Wolny, S. and Daily, G.C. (2012) ‘Integrating ecosystem- service tradeoffs into land- use decisions’, Proceedings of the National Academy of Sciences, 109(19), pp. 7565.

Harmáčková, Z.V. and Vačkář, D. (2015) ‘Modelling regulating ecosystem services trade-offs across landscape scenarios in Třeboňsko Wetlands Biosphere Reserve, Czech Republic’, Ecological Modelling, 295, pp. 207-215.

He, C., Zhang, D., Huang, Q. and Zhao, Y. (2016) ‘Assessing the potential impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models’, Environmental Modelling & Software, 75, pp. 44-58.

Izquierdo, A.E. and Clark, M.L. (2012) ‘Spatial analysis of conservation priorities based on ecosystem services in the Atlantic forest region of Misiones, Argentina’, Forests, 3(3), pp. 764-786.

Keller, A.A., Fournier, E. and Fox, J. (2015) ‘Minimizing impacts of land use change on ecosystem services using multi-criteria heuristic analysis’, Journal of environmental management, 156, pp. 23-30.

Kovacs, K., Polasky, S., Nelson, E., Keeler, B.L., Pennington, D., Plantinga, A.J. and Taff, S.J. (2013) ‘Evaluating the return in ecosystem services from investment in public land acquisitions’, PloS one, 8(6), pp. e62202.

Lawler, J.J., Lewis, D.J., Nelson, E., Plantinga, A.J., Polasky, S., Withey, J.C., Helmers, D.P., Martinuzzi, S., Pennington, D. and Radeloff, V.C. (2014) ‘Projected land-use change impacts on ecosystem services in the United States’, Proceedings of the National Academy of Sciences of the United States of America, 111(20), pp. 7492-7497.

Liu, Y., Zhang, H., Yang, X., Wang, Y., Wang, X. and Cai, Y. (2013) ‘Identifying priority areas for the conservation of ecosystem services using GIS-based multicriteria evaluation’, Pol.J.Ecol, 61(3), pp. 415-430.

Nelson, E., Sander, H., Hawthorne, P., Conte, M., Ennaanay, D., Wolny, S., Manson, S. and Polasky, S. (2010) ‘Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models’, PloS one, 5(12), pp. e14327.

Polasky, S., Nelson, E., Pennington, D. and Johnson, K.A. (2011) ‘The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the State of Minnesota’, Environmental and Resource Economics, 48(2), pp. 219-242.

Sharp, R., Tallis, H.T., Ricketts, T., Guerry, A.D., Wood, S.A., Chaplin-Kramer, R., Nelson, E., Ennaanay, D., Wolny, S., Olwero, N., Vigerstol, K., Pennington, D., Mendoza, G., Aukema, J., Foster, J., Forrest, J., Cameron, D., Arkema, K., Lonsdorf, E., Kennedy, C., Verutes, G., Kim, C.K., Guannel, G., Papenfus, M., Toft, J., Marsik, M., Bernhardt, J., Griffin, R., Glowinski, K., Chaumont, N., Perelman, A., Lacayo, M. Mandle, L., Hamel, P., Vogl, A.L., Rogers, L., Bierbower, W., Denu, D., and Douglass, J. 2016. InVEST +VERSION+ User’s Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund.

Sharps, K., Masante, D., Thomas, A., Jackson, B., Redhead, J., May, L., Prosser, H., Cosby, B., Emmett, B. and Jones, L. (2017) ‘Comparing strengths and weaknesses of three ecosystem services modelling tools in a diverse UK river catchment’, Science of The Total Environment, 584–585, pp. 118-130.

Tao, Y., Li, F., Liu, X., Zhao, D., Sun, X. and Xu, L. (2015) ‘Variation in ecosystem services across an urbanization gradient: A study of terrestrial carbon stocks from Changzhou, China’, Ecological Modelling, 318, pp. 210-216.