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).



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.

Birds as ecological surrogates

In a broad sense, ecological surrogacy involves the assessment of one or more components of an environment or its biota, with the assumption that variation in the surrogate reflects change in another important, but difficult-to-measure attribute” – Westgate et al. (2017)

I am obsessed with birds, they brighten every single day of my life and give me an enormous amount of pleasure. For me, the intrinsic value of this taxonomic group is enough to justify continued efforts to monitor and conserve their populations. However are they of value to wider conservation and ecological studies?

One of the analyses I aim to perform as part of my PhD is an assessment of the habitat quality of the Welland Catchment using the InVEST module for habitat quality and risk. The model parameters need to be calibrated with a specific taxonomic group in mind to achieve the best results (Sharp et al. 2016) and naturally I am hoping to model habitat quality for passerine songbirds. While it is not an explicit aim of my research to scale these results to the wider ecological health of the catchment, it would be useful if the habitat quality for birds could be used to infer quality and threats for other taxa. Fortunately there is evidence in the literature to justify my choice.

 “Excellent knowledge exists of bird ecology and behaviour and they have great public resonance and so are good at raising awareness of biodiversity issues” – Eglington, Noble and Fuller, (2012)

Birds are the most extensively monitored taxonomic group within Europe, whilst worldwide they comprise 75% of biodiversity atlases (Eglington, Noble and Fuller, 2012). Up until 2013, bird population indexes formed part of DEFRA’s annual sustainability indicators (DEFRA, 2013).

Yesterday, the British Trust for Ornithology published its annual ‘State of the UK’s Birds report’, an example of the kind of long term monitoring that birds receive . The report has tracked population changes in the UK’s bird species since 1999 and identifies those of conservation concern (Red, Amber or Green). A brief explanation of how the levels of concern are determined can be found here.

The report is based, in part, on data from breeding bird surveys, carried out by volunteers twice a year in the spring and early summer. My Dad and I conduct the surveys for two sites, it is a great way for anyone with an interest in ecology and conservation to help contribute to long term data (more information on getting involved is here). This year’s report was the product of surveys from 2,600 volunteers, of whom a large proportion have monitored their sites for many years.

There were mixed headlines from the report, with an increase in the number of species now included on the red list. Worryingly, a quarter of all species assessed are now found on the list of species of highest concern (BTO, 2017). However 13 species moved from the amber to green list based on improvements in their status (BTO, 2017). The Tree Sparrow, Passer montanus, was the focal species of my MSc dissertation and I was pleased to see that their population is continuing to recover following near devastation at the end of the 20th century.

The theory behind their power as bio-indicators is that as birds utilise a wide variety of habitats and can be found at or near the top of their food chains they make useful general indicators of the state of wildlife (Eglington, Noble and Fuller, 2012). But doubts remain whether the accuracy of birds in this role is a benefit or hindrance to conservation. Two recently published studies address this and provide some support for my choice of taxa for the intended habitat quality assessment.

In a large meta-analysis, investigating 145 measures of effect size from the literature, Eglington, Noble and Fuller (2012) demonstrated that, on average, bird species richness explained 19% of the species richness of other taxa. An ecological explanation for this lack of strength is that birds have repeatedly been shown to respond to ecological resources on a landscape scale (e.g. Hardman et al. 2016) whereas plants and invertebrates respond to resources at much finer scales (Eglington, Noble and Fuller. 2012). For mammals, who respond to ecological resources at comparable spatial scales to birds, the strength of the relationship was greater.

While the average strength of the general relationship indicates that birds do not make good indicators of total species richness, the study highlights other aspects of birds as bio-indicators that do suggest utility. Effect sizes were shown to be much larger in studies that were based on heterogeneous, patchy landscapes, such as agricultural areas, when compared to studies in more homogenous landscapes such as grasslands (Eglington, Noble and Fuller. 2012). The Welland Catchment fits the bill for patchy and heterogeneous habitats, so inferences about other taxa from birds may potentially be more relevant for my landscape of study.

Westgate et al. (2017) also performed a meta-analysis examining the optimal taxa in terms of species richness and composition for use as ecological surrogates for other taxa. This study takes a different approach to Eglington, Noble and Fuller (2012), focusing on an analysis of patterns of pairwise, cross taxon congruence. Their key finding was that

birds and vascular plants outperform a range of alternative taxa as surrogates for the richness and composition of unmeasured taxonomic groups” – Westgate et al. (2017)

Results were shown to be highly dependent on the target of surrogacy and also the metric used for analysis but birds featured in many of the most powerful surrogate pairs for other taxa. Of all studies analysed, birds and mammals were shown to be optimal taxa for representing the composition of unobserved vertebrate taxa, possibly due to the ecological similarities mentioned earlier.

The most complex statistical analysis carried out by Westgate et al. (2017), involving the inclusion of study sample size and spatial variables, showed that birds were the optimal taxa for representing both the richness and composition of other vertebrates. For an assessment of richness and composition for all taxa, a combination of birds and plants was shown to be the most powerful surrogate.


Validation of these findings

Both studies included sensitivity testing of the results of their analyses to validate their utility. For Eglington, Noble and Fuller (2012) this took the form of a vote counting method which, although it does not include information on the strength of correlation, indicates that “significantly more studies have reported positive relationships between species richness in birds and that of other taxa” (Eglington, Noble and Fuller 2012) than have not (keeping in mind the ‘file drawer-effect of meta analyses (Eglington, Noble and Fuller 2012)).

Westgate et al. (2017) showed that the optimal surrogate identified by their analysis always had higher monitoring power than randomly selected surrogates, especially when specific taxonomic targets are identified over assessing ‘total’ biodiversity.



Eglington, Noble and Fuller (2012) state that birds will continue to be used as bio-indicators or ecological surrogates due to the wealth of knowledge on their ecology, their public resonance and the relative ease with which they can be assessed. While both studies discussed in this post highlight the potential of birds as indicators it is vital to be critical and explicit in terms of what taxa or component of a region’s ecology they are acting as surrogates for. This is because although birds often outperform other taxa as surrogates for diversity and composition, especially when specific taxa are targeted, they still account for relatively small variances in total diversity.




BTO (2017), ‘State of the UK’s Birds 2016’, British Trust for Ornithology, available at: (Accessed: 12/04/2017)

DEFRA (2013) ‘Sustainable development indicators’ Department for Environment Food & Rural Affairs, 2013

Eglington, S.M., Noble, D.G. and Fuller, R.J. (2012) ‘A meta-analysis of spatial relationships in species richness across taxa: birds as indicators of wider biodiversity in temperate regions’, Journal for Nature Conservation, 20(5), pp. 301-309

Hardman, C.J., Harrison, D.P.G., Shaw, P.J., Nevard, T.D., Hughes, B., Potts, S.G., Norris, K. and Marini, L. (2016) ‘Supporting local diversity of habitats and species on farmland: a comparison of three wildlife‐friendly schemes’, Journal of Applied Ecology, 53(1), pp. 171-180

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

Westgate, M. J., Tulloch, A. I. T., Barton, P. S., Pierson, J. C. and Lindenmayer, D. B. (2017), Optimal taxonomic groups for biodiversity assessment: a meta-analytic approach. Ecography, 40: 539–548