Valuing Ecosystem Services Provided by Lakes:

Insights from a Meta-Analysis∗

By

The values of ecosystem services provided by lakes and reser-

voirs are examined through a meta-analysis on an expanded world-

wide database. The study assesses the socio-economic values at-

tributable to the hydrological, biogeochemical and ecological func-

tions provided by lakes and reservoirs (either natural or artificial).

Based on the most extensive global database of non-market and

market valuations of ecosystem services provided by artificial and

natural lakes, we provide an estimation of the average value of a

lake per household (84 USD$2010 per respondent and per year)

and we offer some insights on how people value eleven different

lake ecosystem services. A particular a high valuation is found for

lake amenities whereas a low value is documented for the spiritual

or symbolic appreciation of the lake. An interesting result is the

fact that some interactions between ecosystem services appear to

be significant for explaining lake values. This reflects some trade-

offs, synergies and antagonisms between ecosystem services. As a

result, the value for a specific lake ecosystem service is shown to

depend upon other ecosystem services provided by this lake. Key-

words: Meta-Analysis, Lake, Ecosystem services, Environmental

Valuation

I. Introduction

Lakes are one of the most important source of water available for human and

economic use. It is considered that at the world level 90% of liquid water is con-

∗ This work is a part of the FP7 European Mars project.

1

2

tained in natural and artificial lakes and, according to Shiklomanov and Rodda

(2003), the estimated area of all lakes in the world is about 2 million km2 repre-

senting about 1.5% of the total land area.1

Lakes (and more generally freshwater resources) provide many services. Some

of them are directly valued by humans (increased water quantity, reduced damage

due to flooding) whereas others benefit mainly to environment (reduced erosion,

improved habitat for species). Since most of these services are not traded on

markets, their economic valuation is not straightforward. As a result a wide

non-market valuation literature has developed in the last decades and numerous

lake valuation studies have been performed.2 Due to the wide range of valuation

methods, characteristics of lakes and value estimates, it is very difficult to assess

whether any systematic trends can be distilled from this literature and to shed

light on what factors determine a lake’s value.3 Trying to identify if there exists

an unobserved valuation function that determines a lake’s value given its physical,

economic and geographic characteristics is the main objective of our paper.

We propose here to conduct a meta-analysis on the value of ecosystem services

provided by lakes. The term meta-analysis was coined by Glass (1976) to refer

to “the statistical analysis of a large collection of analysis results from individ-

ual studies for the purpose of integrating the findings” (p. 3).4 This approach

allows us to synthesize information regarding to the value of ecosystem services

from selected studies in a systematic way, and to test some hypotheses on the

determinants of these estimates.

In the field of economic valuation of environmental resources, several meta-

analyses published are related to water resources. These meta-analyses include

for wetlands (Brouwer, Langford, Bateman, and Turner 1999, Brander, Florax,

1This percentage varies highly according to the country considered, up to 8.6 and 9.4% for Swedenand for Finland, respectively.

2See Artell (2014), Abbott and Klaiber (2013) or Abidoye, Herriges, and Tobias (2012) for somerecent examples of this literature.

3A similar point has been made by Woodward and Wui (2001) for wetlands.4Originally used in experimental medical treatment and psychotherapy, meta-analyzes have been

playing an increasingly important role in environmental economics research since the beginning of the1990s, Brouwer, Langford, Bateman, and Turner (1999).

3

and Vermaat 2006, Ghermandi, van den Bergh, Brander, de Groot, and Nunes

2010, Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes,

Schaafsma, Vos, and Wagtendonk 2012, Eppink, Brander, and Wagtendonk 2014),

coastal recreation (Ghermandi and Nunes 2013), coral reef recreation (Brander,

Beukering, and Cesar 2007, Londono and Johnston 2012), lake amenities (Braden,

Feng, Freitas, and Won 2010), aquatic resources (Johnston, Besedin, Iovanna,

Miller, Wardwell, and Ranson 2005, Johnston, Ranson, Besedin, and Helm 2006,

Moeltner, Boyle, and Paterson 2007, Johnston and Thomassin 2010), water qual-

ity (Van Houtven, Powers, and Pattanayak 2007) and flood risk (Daniel, Florax,

and Rietveld 2009). To our best knowledge, our meta-analysis is the first one

focusing on ecosystem services provided specifically by lakes.

We argue that the results of a meta-analysis on ecosystem services provided by

lakes might be useful for several reasons. First, as explained above there remain

substantial debates on the economic value of lakes. Understanding of the physi-

cal, economic and geographic characteristics of lakes impact upon their economic

value may inform decisions related to their use, conservation or restoration. Sec-

ond, it is not clear if the relationships obtained with the existing meta-analyses for

other water bodies (rivers, wetlands, coastal water) may by used for lakes espe-

cially because, since some services provided by lakes are quite specific, ecosystem

economic values may differ according to the water body considered.5

The analysis in this paper relies on the most extensive global database of non-

market and market valuations of ecosystem services provided by artificial and nat-

ural lakes.6 In total, we identified and reviewed over 300 publications related to

5Magat, Huber, Viscusi, and Bell (2000) develop a framework for valuing river water quality im-provement. They find that the mean valuation of people for a lake water quality improvement is roughlytwice as valuable as a similar improvement in river water quality, implying that far more people werewilling to pay large amounts to improve lakes over rivers. Working on water quality of water bodies inthe United States, Viscusi, Huber, and Bell (2008) find that people have a significant preference for lakeimprovements over river improvements, a result compatible with a higher valuation attributed to lakes.Using an hedonic price approach, Sander and Polasky (2009) reports significant higher amenity valuesfor houses located at proximity of a lake, compared to a proximity of a river.

6Including artificial lakes (dams and reservoirs) is important due to their environmental impacts.In their mapping of the world’s reservoirs and dams, Lehner, Liermann, Revenga, Vorosmarty, Fekete,Crouzet, Doll, Endejan, Frenken, Magome, Nilsson, Robertson, Rodel, Sindorf, and Wisser (2011) indi-cate that 7.6% of the world’s rivers with average flows above 1 cubic meter per second are affected by a

4

valuations of ecosystem services provided by lakes. Among them, we selected val-

ues from a subset a little bit more than 100 of these studies that were sufficiently

comparable for inclusion in a meta-analysis. We then identify and quantitatively

evaluate the role of income effects, substitution effects, return to scale, population

density, biodiversity and geo-climatic conditions in the formation of lake ecosys-

tem values. Our result show promise for benefit transfer because they suggest that

it may be possible to reliably predict the value of ecosystem services provided by

lakes based on their physical, economic and geographic characteristics. This opens

the door to some value upscaling approaches as the ones proposed by Brander,

Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos,

and Wagtendonk (2012) or Ghermandi and Nunes (2013).

II. Ecosystem services provided by lakes and reservoirs

This section outlines the definition and typology of lakes and reservoirs used in

this article, the functions that are utilized by humans, and the valuation methods

that are applied to value various lake and wetland services. This section also

discusses the heterogeneity of lake value estimates.

A. Defining lakes

One may think that defining lakes is an easy task but it is not in practice. Quite

simply, lakes are bodies of water that occupy depressions on land surface. There

is however no universally accepted definition of a lake. The International Glossary

of Hydrology briefly defines a lake as an “inland body of water of considerable

size”.7 In the European Water Framework, a lake is defined as a “body of standing

inland surface water” and this will be the definition we will refer to in the rest of

cumulative upstream reservoir capacity that exceeds 2% of their annual flow.7The International Glossary of Hydrology is a joint publication of the United Nations Educational,

Scientific and Cultural Organization and the World Meteorological Organization. It is available athttp://webworld.unesco.org/water/ihp/db/glossary/glu/aglu.htm.

5

this paper.8

We will include both natural and artificial lakes. We will also consider bodies of

water in dams and reservoirs. Indeed, since dams and reservoirs provide several

services including regulation of river flows, water storage, flood control, irrigation

of agricultural lands, navigation and electricity, they may have specific economic

values.9 On the other hand, dams and reservoirs can induce substantial costs to

human societies (population displacement, loss of land) but also to environment,

see Lehner, Liermann, Revenga, Vorosmarty, Fekete, Crouzet, Doll, Endejan,

Frenken, Magome, Nilsson, Robertson, Rodel, Sindorf, and Wisser (2011). Flow

regulation from dams and reservoirs has been shown to lead to numerous physical

and ecological impacts on freshwater ecosystems and on their dependent species.

The fragmentation of aquatic habitats, which limits the movement of species but

also the delivery of nutrients and sediments downstream, is another important

adverse ecological consequence of dams and reservoirs. It has also been debated

recently debated whether dams and reservoirs used for hydroelectric generation

are merely in-stream water users or whether they also consume water in the sense

of taking away water from water bodies.10 Those detrimental impact of dams

and reservoir might result is some negative value premia put by people on these

water bodies. This is an issue we will investigate in the meta-analysis.

Due to their specificities, we will exclude from the scope of our analysis wet-

lands.11 The interested reader mays refer to (Brouwer, Langford, Bateman, and

8One of the most elaborated definition of lakes has been provided by Kuusisto (1985) as “a depressionor a group of depressions partly or fully filled by water, all parts of the water body have the same surface,excluding temporary variability, caused by wind or ice, the ratio between in-flow and volume is smallenough to let most of the suspended, inflowing material to form bottom sediments, and the surface areaexceeds a given minimum value.”

9Mekonnen and Hoekstra (2012) indicate that hydropower accounts for about 16% of the world’selectricity supply and that about 30–40% of irrigated land worldwide relies on water stored behinddams.

10Working on 35 hydropower plants representing 8% of the global installed hydroelectric capacity,Mekonnen and Hoekstra (2012) suggests that hydropower is in fact a large consumptive user of water.The amount of water lost through evaporation annually from the selected reservoirs is equivalent to 10%of the global blue water footprint related to crop production.

11The International Glossary of Hydrology defines wetlands as area of marsh, fen, peatland or water– whether natural or artificial, permanent or temporary – with water that is static or flowing, fresh,brackish or salt, including areas of marine water the depth of which does not exceed six metres at lowtide. Wetlands play a specific role for instance in abating nitrogen load from agricultural sources or in

6

Turner 1999, Brander, Florax, and Vermaat 2006, Ghermandi, van den Bergh,

Brander, de Groot, and Nunes 2010, Brander, Bruer, Gerdes, Ghermandi, Kuik,

Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk 2012, Eppink,

Brander, and Wagtendonk 2014) for some meta-analyses of values generated by

wetlands, in different part of the world.

B. Identifying, measuring and valuing ecosystem services provided by lakes

In Table 1, we proposed a classification of ecosystem services provided by lakes.

We also provide the methods most commonly used in their valuation.

The classification of ecosystem services has been developed by the Joint Re-

search Center of the European Commission specifically for lakes within the FP7

European MARS project. The conceptual framework is based on the CICES

v4.3 and has been tested in several pilot studies, including one on freshwater

ecosystems.12

flood protection.12Several classification of ecosystem services have been proposed, the most well-known can be found

in Costanza, d’Arge, de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill, Paruelo, Raskin,Suttonkk, and van den Belt (1997), de Groot, Wilson, and Boumans (2002), TEEB (2010), Haines-Youngand Potschin (2011). Some classifications are dedicated to aquatic ecosystem services, see for instanceBrander, Florax, and Vermaat (2006) for wetlands.

7

Table1—

Lakeecosy

stem

services,

typeofvalue,and

commonly

applied

valuationmethods

Eco

syst

emse

rvic

esC

ate

gory

aV

alu

ety

pe

Valu

ati

on

met

hodb

Exam

ple

sof

econ

om

icgood

pro

vid

ed

1-

Fis

her

ies

an

daqu

acu

ltu

reP

rovis

ion

ing

Dir

ect

MP

,R

Cfi

shca

tch

2-

Wate

rfo

rd

rin

kin

gP

rovis

ion

ing

Dir

ect

MP

,C

Vw

ate

rfo

rd

om

esti

cu

ses

3-

Raw

(bio

tic)

mate

rials

Pro

vis

ionin

gD

irec

tM

P,

RC

alg

ae

as

fert

ilis

ers

4-

Wate

rfo

rn

on

-dri

nkin

gp

urp

ose

sP

rovis

ion

ing

Dir

ect

MP

,PF

wate

rfo

rin

du

stri

al

or

agri

cult

ura

lu

ses

5-

Raw

mate

rials

for

ener

gy

Pro

vis

ion

ing

Dir

ect

RC

wood

from

rip

ari

an

zon

es

6-

Wate

rp

uri

fica

tion

Reg

ula

tion

Ind

irec

tR

C,

CV

exce

ssn

itro

gen

rem

oval

by

mic

roorg

an

ism

s

7-

Air

qu

ality

regu

lati

on

Reg

ula

tion

Ind

irec

tR

Cd

eposi

tion

of

NO

xon

veg

etal

leaves

8-

Ero

sion

pre

ven

tion

Reg

ula

tion

Ind

irec

tR

Cveg

etati

on

contr

ollin

gso

iler

osi

on

9-

Flo

od

pro

tect

ion

Reg

ula

tion

Ind

irec

tR

C,

CV

veg

etati

on

act

ing

as

barr

ier

for

the

wate

rfl

ow

10-

Main

tain

ing

pop

ula

tion

san

dh

ab

itats

Reg

ula

tion

Ind

irec

tR

Ch

ab

itats

use

as

anu

rser

y11-

Pes

tan

dd

isea

seco

ntr

ol

Reg

ula

tion

Ind

irec

tR

C,

CV

natu

ral

pre

dati

on

of

dis

ease

san

dp

ara

site

s

12-

Soil

form

ati

on

an

dco

mp

osi

tion

Reg

ula

tion

Ind

irec

tR

Cri

chso

ilfo

rmati

on

infl

ood

pla

ins

13-

Carb

on

sequ

estr

ati

on

Reg

ula

tion

Ind

irec

tR

C,

MP

carb

on

acc

um

ula

tion

inse

dim

ents

14-

Loca

lcl

imate

regu

lati

on

Reg

ula

tion

Ind

irec

tR

C,

MP

main

ten

an

ceof

hu

mid

ity

patt

ern

s

15-

Rec

reati

on

Cu

ltu

ral

Dir

ect

CV

,T

C,

DC

,H

Psw

imm

ing,

recr

eati

on

al

fish

ing,

sights

eein

g

16-

Inte

llec

tual

an

daes

thet

icap

pre

ciati

on

Cu

ltu

ral

Non

-use

CV

,D

Cm

att

erfo

rre

searc

h,

art

isti

cre

pre

senta

tion

s17-

Sp

irit

ual

an

dsy

mb

olic

ap

pre

ciati

on

Cu

ltu

ral

Non

-use

CV

,T

C,

DC

exis

ten

ceof

emb

lem

ati

csp

ecie

s

18-

Raw

ab

ioti

cm

ate

rials

Extr

aab

ioti

cD

irec

tP

F,

MP

extr

act

ion

of

san

d&

gra

vel

19-

Ab

ioti

cen

ergy

sou

rces

Extr

aab

ioti

cD

irec

tP

F,

MP

hyd

rop

ow

ergen

erati

on

Note:

Th

ecl

ass

ifica

tion

of

ecosy

stem

serv

ices

has

bee

nd

evel

op

edsp

ecifi

cally

for

lakes

wit

hin

the

FP

7E

uro

pea

nM

AR

San

dp

rop

ose

dby

the

Join

tR

esea

rch

Cen

ter

of

the

Eu

rop

ean

Com

mis

sion

.a:

Pro

vis

ion

ing,

Reg

ula

tion

an

dm

ain

ten

an

ce,

Cu

ltu

ral,

Extr

aab

ioti

cse

rvic

es.

b:

Conti

ngen

tvalu

e(C

V),

Hed

on

icp

rice

(HP

),M

ark

etp

rice

(MP

),p

rod

uct

ion

fun

ctio

n(P

F),

Rep

lace

men

tco

st(R

C),

travel

cost

s(T

C).

8

As noted above, lake provide a wide range of vital ecosystem services, which

have an equally wide range of value.13 Economists usually decompose the to-

tal economic value of ecosystems into direct use, indirect use and nonuse values.

Direct use values refer to consumptive and non-consumptive uses that entail di-

rect physical interaction with the lakes and their services such as outputs of

fish, fuel wood, recreation, and transport. Indirect use values include regulatory

ecological functions, which lead to indirect benefits such as flood control, storm

protection, nutrient retention, nursery grounds for different species, and erosion

control. Nonuse values include existence and bequest values of lakes.

Methods for valuing ecosystem services vary depending on the nature of the

service, and belong to two main categories namely revealed preference and stated

preference methods. Revealed-preference methods exploit the relationship be-

tween some forms of observed individual behavior (e.g., visiting a lake) and asso-

ciated environmental attributes (e.g., water quality of the lake) to estimate value.

The revealed preference approaches include market price (MP), production func-

tion (PF), hedonic pricing (HP), travel cost (TC), replacement cost (RC), and

damage cost avoidance (DC). On contrary, stated preference methods use survey

questions to have respondents explicitly or implicitly state their preferences and

values for a specific good. Within this category scholars usually make the dis-

tinction between contingent value (CV) and discrete choices (DC). The choice of

valuation method matters and depends upon the context. For instance, revealed

preference cannot be used to estimate nonuse values. While stated preference

techniques can, in principle, be used to value any type of ecosystem service, in

practice there may be cognitive limitations to stating preferences.

13Ecosystem services have been defined as the direct and indirect contributions of ecosystems tohuman well-being in TEEB (2010). The existing literature of ecosystem services provided by lakes hasbeen recently summarized in Schallenberg, de Winton, Verburg, Kelly, Hamill, Hamilton, Dymond, et al.(2013).

9

C. A global assessment of lake ecosystem services

We survey now the existing literature having addressed ecosystem services pro-

vided by lakes and reservoir, following the structure of the classification proposed

in Table 1. When no information is available specifically for lakes and reservoir,

we discuss the ecosystem services for inland waters.

Provisioning services. — Provisioning services are the products provided by

ecosystems, of which freshwater and food are two of the most important.

The Fisheries and aquaculture service corresponds to the ability of an ecosys-

tem to support fish supply. In 2012, it was estimated that 33.8% of world fisheries

and aquaculture production comes from inland water (FAO 2014), and this per-

centage has steadily increased over the last years from 28.4% in 2007. No official

publication allows to make the distinction between fish an aquaculture produced

from lakes and from rivers. Fish and fishery products are of great importance

for since they represent an important source of protein and essential micronu-

trients for human consumption.14 A specific characteristic of this service is its

highly uneven distribution at the global level. Indeed, 90% of the inland fish

catch worldwide is concentrated in Africa and Asia where the direct dependence

on inland fisheries and human well-being is the highest, see Dugan, Delaporte,

Andrew, O’Keefe, and Welcomme (2010).

The Water for drinking service corresponds the ability of an ecosystem to pro-

vide water for domestic use. Inland surface water sources account for a substan-

tial part for providing this service. Using a global-scale assessment model, Doll,

Hoffmann-Dobrev, Portmann, Siebert, Eicker, Rodell, Strassberg, and Scanlon

(2012) indicate for instance that 64% of the water used by domestic users world-

wide comes from surface sources. We may expect that lake will play an even

14According to FAO (2012), fish accounted for 16.6% of the world population’s intake of animal proteinin 2009 and 6.5% of all protein consumed.

10

more important role in the future in securing drinking water supply. Indeed some

global projection models such as Hejazi, Edmonds, Chaturvedi, Davies, and Eom

(2013) predict an increase of municipal water withdrawals from 466 km3 year−1

in 2005 to 1098 km3 year−1 in 2100.

The Raw (biotic) materials service is the capacity for an ecosystem to sustain

the production of biotic resources such as wood and strong fibers (for building),

biochemicals or biodynamic compounds (latex, gums, oils, waxes, tannins, dyes,

hormones, etc.) for all kinds of industrial purposes. Lakes produce a large amount

of biotic resources such as algae (which can be used as fertilisers) or vegetal

compounds (which can be used as cosmetics).

The Water for non-drinking purposes service corresponds to the provision of

water for industrial or agricultural uses.

The Raw materials for energy service include the supply in fuelwood from

riparian zones.

Regulation & Maintenance services. — This category encompasses all benefits

obtained from the regulation of ecosystem processes.

The Water purification service corresponds to the fact that some ecosystems

may allow the sedimentation (retention) of some soil particles. Run-off from city

streets and agricultural fields contain various pollutants such as oil, pesticides,

and fertilizer as well as excess soil. These pollutants are absorbed by the plants

and broken down by plants and bacteria to less harmful substances. Pollutants

attached to suspended soil particles are filtered out by grasses and other plants and

deposited in lakes. This process helps improve water quality. It has been shown

that lakes and reservoirs can contribute substantially to river network nitrogen

retention (Harrison, Maranger, Alexander, Giblin, Jacinthe, Mayorga, Seitzinger,

Sobota, and Wollheim 2009, Powers, Robertson, and Stanley 2013). Powers,

Robertson, and Stanley (2013) present also evidence of long-term retention of

11

phosphorous by lakes and reservoirs.

The Air quality regulation service corresponds to the fact that lakes extract

chemicals from the atmosphere, influencing many aspects of air quality.

The Erosion prevention service corresponds to the fact that the vegetative cover

on lake banks plays an important role in soil retention and the prevention of

landslides. Soil erosion is the most widespread form of soil degradation. 12% of

land area is globally affected by erosion which represents 1094 million ha (Mha).

Flood protection. For both flood formation and the occurrence of droughts,

the storage and retention of water in lakes or reservoirs is of high importance.

Analysing disasters having impacted population over the period 1975–2001, Jonkman

(2005) concludes that floods were the most frequently occurring, followed by wind-

storms. While some other disasters are more significant with respect to numbers

of killed (especially droughts and earthquakes), floods by far affect the most per-

sons, in total almost 2.2 billion over the considered period.

The Maintaining population and habitats service is the fact that an ecosystem

provides living space for all wild plant and animal species.

The Soil formation and composition service corresponds to rock weathering and

organic matter accumulation leading to the formation of productive soils.

Carbon sequestration is the process of capture and long-term storage of at-

mospheric carbon dioxide. It has been estimated that, on a global basis, lakes

and reservoirs sequester around 20% of the carbon transferred from land, re-

ducing carbon losses from inland waters to the atmosphere by around one-third

(Tranvik, Downing, Cotner, Loiselle, Striegl, Ballatore, Dillon, Finlay, Fortino,

Knoll, et al. 2009). Therefore, lakes can perform an important ecosystem service

in reducing the effect of climate warming. It has been however recently shown

that inland waters can be substantial sources of carbon dioxide and methane emis-

sions (Bastviken, Tranvik, Downing, Crill, and Enrich-Prast 2011). Accordingly,

the terrestrial green house gas sink may be smaller than currently believed. Typ-

ically, effects of lakes and reservoirs within river networks have been expressed as

12

changes in flux magnitude, but changes in flux variability may also occur. This

has recently been shown through decreased intra-annual variability of stream dis-

solved organic carbon fluxes downstream of natural lakes (Goodman, Baker, and

Wurtsbaugh 2011).

The Local climate regulation service corresponds to the fact that an ecosystem

may affect climate at the regional scale. Inland waters affect climate at the

regional scale through exchange of heat and water with the atmosphere (Krinner

2003). Inland waters tend to humidify the atmosphere, especially in summer,

and may modify the pattern of precipitations. Inland waters also regulate local

temperatures by absorbing heat in summer time and releasing it in winter (Hardin

and Jensen 2007).

Cultural services. — These are the nonmaterial benefits people obtain from

ecosystems in particular through spiritual enrichment, cognitive development, re-

flection, recreation, and aesthetic experiences. Cultural ecosystem services are

among the most challenging of services to address since they comprise complex

ecological and social properties and interactions.

Extra abiotic environmental services. — Abiotic resources are all products

not from living plants and animals, like minerals, fossil fuels, windand. In build-

ing our classification, we have decided to consider two types of abiotic services

(raw abiotic materials and abiotic energy sources) due to their importance when

considering.15

Raw abiotic materials include mainly extraction of sand & gravel. Globally,

between 47 and 59 billion tonnes of material is mined every year, of which sand

15Some previous ecosystem service classification have excluded abiotic resources based on the groundthat they were usually non-renewable and/or they cannot be attributed to specific ecosystems (de Groot,Wilson, and Boumans 2002).

13

and gravel, hereafter known as aggregates, account for both the largest share

(from 68% to 85%) and the fastest extraction increase (Krausmann, Gingrich,

Eisenmenger, Erb, Haberl, and Fischer-Kowalski 2009).

Abiotic energy sources corresponds to the production of renewable abiotic en-

ergy. Mekonnen and Hoekstra (2012) indicate that hydropower accounts for about

16% of the world’s electricity supply.

D. Valuating ecosystem services provided by lakes at the large scale

Research on the monetary valuation of ecosystem services dates back to the

early 1960s but received wide attention with the publication of Costanza, d’Arge,

de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill, Paruelo, Raskin,

Suttonkk, and van den Belt (1997). Surprisingly, whereas an important number

of valuation case studies have been published for lakes ecosystem services (see

following sections), it is quite difficult find some aggregated values at world-wide

level. There are however a few exceptions.

Costanza, d’Arge, de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill,

Paruelo, Raskin, Suttonkk, and van den Belt (1997) have estimated the world-

wide economic value of 17 ecosystem services for 16 biomes. They report world-

wide average value for lake and rivers equal to $8,498 per hectare and per year,

64% of this value being provided by the water regulation service.

Another exception is TEEB (2010). Appendix C of this book gives the results of

an analysis of 11 main biomes/ecosystem-complexes (i.e. open ocean, coral reefs,

coastal systems, coastal wetlands, inland wetlands, rivers & lakes, tropical forests,

temperate & boreal forests, woodlands, grasslands and polar & high mountain

systems) and collate their monetary values from different socio-economic con-

texts across the world. For the rivers & lakes biomes, the total monetary value

of the potential sustainable use of all services varies between 1.779 and 13.488

Int.$/ha/year-2007 value. One should however point out that this average value

has been computed based on only 12 points taken from 6 distinct studies.

14

III. Meta-database

We propose to use a meta-analysis as a means to estimate benefit functions that

synthesize information from multiple primary studies having valuated ecosystem

services provided by lakes and reservoirs. We will focus our attention on cultural

services, and within this category more specifically on recreational services.

A. Search protocol

The scientific references have been selected through systematic searches of the

keywords Valuation and Lake, Value and Lake, Willingness to pay or WTP and

Lake, Stated preferences and Lake, on various search engines and on the web sites

of major publishers of academic journals (Scopus, Science Direct, Wiley, Web of

knowledge, RepEc, AgEconSearch, etc.). Similar searches were also conducted on

databases specialized in environmental valuation.16 Lastly, the grey literature was

searched using various search engine including Google Scholar and Science.gov.17

In all, the literature search process took about six months (December 2013 – May

2014).

A three-step procedure has been implemented for each search. Based on the

abstract, studies have been first classified into three categories namely irrelevant

(studies without any reference to one or several lakes or those which did not re-

port any economic valuation results), potentially relevant and relevant. Irrelevant

studies where disregarded at this first step. Second, further investigations were

then conducted on potential relevant studies in order to reclassify them either as

irrelevant or relevant. Third, all studies considered as relevant were downloaded

and an additional screening process was conducted to decide if they had to be

included or not in the final database.18

16We have in particular considered the Environmental Valuation Reference Inventory, the databaseof valuation studies in Southeast Asia, the Nordic Environmental Valuation Database and the GreekEnvironmental Valuation Database.

17This is important to reduce the influence of a potential publication bias in the metaregression analysisbut it implies further search efforts.

18As an example we provide some information on the search with SCOPUS, the largest abstract and

15

Figure 1. Type of study in the meta-analysis

5

11

2

1

92

2

0 20 40 60 80 100frequency

Working paper

Report

Phd Thesis

Other

Journal

Book

The selection procedure led us to retain 101 studies. A vast majority of the

database is made of peer-reviewed articles (86 studies), the second category the

most represented being institutional reports (9 studies).

Studies are quite recent on average. Among the 98 studies of the database,

39 have been published after 2010, 41 between 2000 and 2010 and the remaining

before 2000.

All continents are represented in our database, with an over-representation of

North-America. North-America ranks first with 61 studies (60 studies deal with

United States). The second continent the most represented is Europe with 20

studies. As already mentioned, United States are by far the country for which

we have the most of studies. This may result from a selection bias since our

systematic searches for lake valuation study has been done in English. It may

also reflect the fact that hedonic price approaches have been extensively used in

this country for valuing housing amenities. We will come back to this issue of

citation database of research literature. The first stage of the search resulted in selecting 95 studies (thedomain was restricted to documents in economics or in Social Sciences). Based on the abstract, 44 wereclassified as irrelevant, 13 as potentially relevant and 38 as relevant. After having downloaded the 13potentially relevant studies, only 1 was reclassified from potentially to relevant. The 45 relevant articleswere then downloaded and carefully examined. Following this third screening step, only 31 studies fromthe SCOPUS search have been kept and included in the final database. A similar method has been usedfor other search engines.

16

Figure 2. Repartition of studies per country

642

11

33

23

13

122

12

111

101

31

31

0 20 40 60frequency

United StatesTurkey

ScotlandPolandNorway

New ZealandNetherlands

JapanItaly

IndiaGreece

GermanyFranceFinland

EthiopiaEstonia

EnglandCzech Republic

ChinaChile

CanadaCameroon

AustraliaArmenia

Figure 3. Repartition of observations per country

36526

12

5525

1019

21098

1553163

198

181

144

0 100 200 300 400frequency

United StatesTurkey

ScotlandPolandNorway

New ZealandNetherlands

JapanItaly

IndiaGreece

GermanyFranceFinland

EthiopiaEstonia

EnglandCzech Republic

ChinaChile

CanadaCameroon

AustraliaArmenia

17

sample representativeness in the discussion section.

A given study main report multiple lake values, either because several lakes are

considered or because of use of several valuation methods or scenarios. Due to

multiple values per studies, we have then 563 observations (i.e. lake value) in our

final sample. This represents on average a little bit more than 5.5 observations

per study. Again, United States rank first with 338 observations. They are

followed by Norway (54 observations), Finland (23 observations) and China (22

observations).

B. Description of water bodies

One critical issue when conducting a meta-analysis is the high level of hetero-

geneity and the potentially non-comparability of studies pooled in the metadata.

As a good practice, studies included in the meta-analysis should satisfy a criterion

of minimal consistency for the dependent variable across observations, (Smith and

Pattanayak 2002). This commodity consistency criterion requires in particular a

minimal level of uniformity for the definition of the good that is valued. In our

metadata, the way lakes have been defined varies significantly from one primary

study to another. For instance, some studies refer to a particular lake whereas

others consider all water bodies in a given area. Some studies focus on artificial

lakes whereas other deal with natural ones. All these lake characteristics should

be introduced as moderators in the meta-analysis in order to insure a minimal

level of uniformity for the good valued.

In a vast majority of cases, values are reported for a specific lake. We have

in our meta-database 170 distinct lakes for which, on average, a little bit more

than 3 values are reported. This means that we have mainly in our database a

collection of local valuation studies, which is relevant from the point of view of

conducting a meta-analysis.

Lake can be either natural or artificial, and this distinction may matter since

ecosystem services differ according to this two categories of lakes. Most of the

18

Figure 4. Location and number of observations per lake

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lake values have been obtained for natural lakes (405 observations). An artificial

lake is considered for 126 observations whereas for 33 observations both natural

and artificial lakes are included in the valuation exercise (which is conducted in

that case either at a regional or a national level).

When restricting our sample to local lake valuation studies, the average area

of each lake is equal to 5014 km2, varying from 0.02 km2 (Raintree Ranch lake

in Arizona, United States) to 58000 km2 (lake Michigan, United States). The

median lake area is 33 km2 which suggests a very skewed distribution of the lakes

in our sample. If we exclude Lake Michigan for the sample (Lake Michigan is by

19

far the greatest lake in our sample in terms of area), the average area drops to

722 km2, and to 146 km2 if other Great Lakes are taken out. In Figure 5, we

gives the distribution of observations per lake area (restricting our sample to local

lakes).

Figure 5. Repartition of observations per area of the lake

184

200

188

57

0 50 100 150 200frequency

>1000 km2

[20,1000[ km2

[1,20[ km2

< 1 km2

It should be noticed that, when possible, lakes in our database have been geo-

referenced with ArcGIS. This will allow us to add some spatially explicit con-

text variables (such as anthropogenic pressure, climate conditions or GDP in the

considered area) to be accounted for in the meta-regression model. Spatially-

augmented data have been used for a meta-analysis in particular by (Ghermandi,

van den Bergh, Brander, de Groot, and Nunes 2010, Ghermandi and Nunes 2013).

In addition and in order to get additional information on lakes in our database

(type of use, shape, shoreline length, etc.), we have complemented our data with

lake attributes taken from two global lake and reservoir databases, namely the

Global Lakes and Wetlands Database (GLWD)19 and the Global Reservoir and

19The GLWD compiles worldwide data on lakes and reservoirs with an extensive list of attribute foreach. We have used the first level (GLWD–1) which comprises the 3067 largest lakes (area greater than50 km2) and 654 largest reservoirs (storage capacity greater than 0.5 km3) worldwide. The GLWD isjointly developed by WWF and the Center for Environmental Systems Research, University of Kassel inGermany.

20

Dam Database (GRanD)20

C. Valuation methods

An additional consideration for any meta-analysis of nonmarket values is the

degree of consistency in welfare measures (Johnston and Rosenberger 2010).21

In our case, lake value measures have been obtained from the primary studies

through various valuation methods and analytical techniques including travel cost,

choice experiment, contingent valuation and hedonic price methods. This raises

some welfare consistency concern since the measures obtained may not rely on

the same theoretical construct.22

One way to address this issue consists in pooling estimates drawn from the nu-

merous methods (contingent valuation, travel cost, choice experiment, net factor

income, productivity, gross revenue methods, etc.) into a single meta-database

and including dummy variable for the used methods as moderators in the meta-

analysis, see for example (Brander, Beukering, and Cesar 2007). This approaches

still raises some welfare inconsistency concerns, see Nelson and Kennedy (2009).

Another solution adopted by Londono and Johnston (2012) consists in excluding

from the analysis any study which does not comply with a strict application of

the welfare consistency criterion. One drawback of this solution is to exclude de

facto all studies based on methodologies that do not generate well-defined wel-

fare measures (e.g., replacement costs, gross revenues, etc.). There might be also

a selection bias issue if the excluded studies present some systematic character-

istics related to lake values. A third solution consists in estimating separated

meta-regressions for lake values obtained by valuation methods based on differ-

20The GRanD compiles reservoirs with a storage capacity of more than 0.1 km3. it contains 6.862spatially explicit records of reservoirs with their respected dams and gives information on their storagevolume. The development of the GRanD database has been coordinated by the Global Water SystemProject, University of Bonn in Germany.

21Welfare consistency requires that welfare measures represent the same theoretical construct (Smithand Pattanayak 2002).

22It is for instance well-known that contingent valuation and travel cost methods provide Hicksian andMarshallian welfare measures, respectively, so that pooling across these study types violates the strictestform of welfare consistency (Smith and Pattanayak 2002).

21

ent theoretical constructs. This approach however implies to have a sufficient

number of observations for each sub-sample considered. In what follows, we will

implement these three strategies to address this welfare consistency issue.

In Figure 6, we have plotted the distribution of observations per type of valu-

ation method.

Figure 6. Type of valuation method

124

22

224

84

175

0 50 100 150 200 250frequency

TravelCost

Other

HedonicPrice

ChoiceExp

CV

Four main valuation methods have been used namely contingent valuation

(CV), choice experiment (CE), hedonic prices (HP) and travel costs (TC). A

fifth category (Other) has been added, which basically includes a small number

of studies having used some mixed protocols.

With 236 and 153 observations respectively, hedonic price and contingent val-

uation are the two methods which have been the most often used by scholars for

valuing lake’s ecosystem services. Travel costs and choice experiment rank third

and fourth, respectively. We do not expect a particular impact of using a specific

method since the existing literature does not report any systematic bias which

could be associated with using a specific valuation method.

For a given valuation method, lake value may differ according to the format

used. For instance, among contingent valuations, open-ended elicitation formats

are more liable to free-riding behavior, which may lead to understatement of lake

22

values. Such value estimates likely lie below those obtained with other elicitation

formats such as payment card and dichotomous choice. Among the 153 observa-

tions based on contingent valuations, 72 use a dichotomous choice format (either

single or double-bounded), 44 a payment card format, 31 an open-ended format

and 6 an iterative bidding approach. The valuation format will be included as a

moderator in the meta-analysis.

D. Ecosystem services provided by lakes

Different ecosystem services have been valued in the literature we have sur-

veyed, although not all of the services identified in Table 1 have been valued

(e.g., carbon sequestration or erosion prevention). In total, we have gathered

some economic values for 11 different ecosystem services provided by lakes ap-

pearing in our database, see Figure 7.

Figure 7. Lake ecosystem services in the valuation studies

20

179

228

199

145

33

174

158

240

26

13

0 50 100 150 200 250frequency

ESS_Spiritual

ESS_PopHabitat

ESS_Amenity

ESS_UnspecRec

ESS_Sightseeing

ESS_Camping

ESS_Boating

ESS_Swiming

ESS_Fishing

ESS_DrinkWater

ESS_Flood

For each lake valuation study (or for each observation in case of multiple ob-

servations par study) we have identified the ecosystem services provided by the

considered lake. They belong to three categories of ecosystem services (provision-

ing services, regulation and maintenance services, cultural services ).

23

We have only 22 observations of economic values for provisioning services and

all these observations correspond to the “water for drinking service”.

We have 174 observations of economic values for regulation and maintenance

services. The majority (163 observations) refers to the “maintaining populations

and habitats” services (ESS PopHabita), whereas the remaining observations

deal with the “flood protection” service (ESS Flood).

Not surprisingly, the vast majority of ecosystem services for which a lake value

is associated with corresponds to the cultural service category. In order to reflect

the distinctions that are generally made between cultural services of lakes in the

valuation literature, we have categorized these services in our database slightly

differently from the list in Table 1. In particular, the “recreation service” has

been split into several sub-services (e.g., fishing, boating, swimming, camping,

sightseeing and unspecified recreational service). In addition, the “Amenity”

sub-service has been created for valuation studies based on the hedonic price

approach.23 Among the cultural service category, the “amenity service” ranks

first (244 observations for ESS Amenity) followed by the different recreational

services such as “fishing” (192 observations for ESS Fishing) or “boating” (144

observations for ESS Boating).

Some studies value only one particular lake ecosystem, but a significant num-

ber of them provides values for two or more services, Figure 8. The number of

ecosystem services valued in each study varies from 1 to 7, with an average a lit-

tle bit higher than 2. This raises an interesting identification issue since in most

cases a direct mapping between a particular service and its associated economic

value does not exist. This identification issue might be particularly relevant to

address in case of complementarity or substitutability relationships among ser-

23As explained in (Lansford and Jones 1995), an hedonic study of shoreline and “near-the-lake” prop-erties capture an important component of the recreational and “amenity” (aesthetic) values that areprovided by the existence of such a lake. There is however no direct mapping between these amenitiesand the cultural service category as defined in Table 1. In fact to obtain the total recreational andaesthetic value, other components must be added to the value of amenities. These include the value topersons living outside the immediate area who travel to the lake to enjoy its benefits and componentsfor existence, bequest, and option value by those who never visit the lake yet believe it to be beneficial.

24

Figure 8. Number of ecosystem services valued in each study

12

61

47

38

19

69

383

0 100 200 300 400frequency

7

6

5

4

3

2

1

vices. Indeed, in all previous meta-analysis on water ecosystem services, it has

been assumed that the economic value of a water body is a linear function of the

ecosystem services provided by a lake.24 We argue that such a specification could

be questioned in case of trade-offs, synergies and antagonisms between ecosystem

services. Since there are complex relationships among ecosystem services (Fu, Su,

Wei, Willett, L, and Liu 2011, Raudsepp-Hearne, Peterson, and Bennett 2010)25,

the value for a specific ecosystem services might depend upon the other ecosys-

tem services provided by a lake. Not introducing interactions across ecosystem

services may then lead to biased estimates in the meta-analysis. It also raises

some concerns with respect to using a “value catalog approach” for doing some

transfer of values for ecosystem services.

24In existing meta-analyses, ecosystem services are accounted for by a set of binary variables indicatingthe ecosystem services valued (Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes,Schaafsma, Vos, and Wagtendonk 2012). In their meta-analysis of values of natural and human-madewetlands, (Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010) estimate an extended modelthat includes a series of cross-effect variables. These variables capture the relationship between theprovision of a specific wetland service and the type of wetland that provides it, but not the relationshipsamong services.

25In their analysis of provision of multiple ecosystem services across landscapes, Raudsepp-Hearne,Peterson, and Bennett (2010) report that among the 66 possible pairs of ecosystem services they haveconsidered, 34 pairs have appeared to be significantly correlated either positively (synergies and comple-mentarities) or negatively (tradeoffs). At the landscape scale, they typically observe a pattern of tradeoffsbetween provisioning ecosystem services and both regulating and cultural ecosystem services. On con-trary they document synergies across regulating ecosystem services, all regulating ecosystem servicesbeing positively correlated with each other.

25

Figure 9. Lake ecosystem services by valuation methods

1392

2110

8525

10785

11422

2

054

744

352

3641

3640

32

2192102110

11

431

041

235

2830

7000

0 50 100 150 200 0 50 100 150 200

ESS_SpiritualESS_PopHabitat

ESS_AmenityESS_UnspecRecESS_Sightseeing

ESS_CampingESS_Boating

ESS_SwimingESS_Fishing

ESS_DrinkWaterESS_Flood

ESS_SpiritualESS_PopHabitat

ESS_AmenityESS_UnspecRecESS_Sightseeing

ESS_CampingESS_Boating

ESS_SwimingESS_Fishing

ESS_DrinkWaterESS_Flood

ESS_SpiritualESS_PopHabitat

ESS_AmenityESS_UnspecRecESS_Sightseeing

ESS_CampingESS_Boating

ESS_SwimingESS_Fishing

ESS_DrinkWaterESS_Flood

ESS_SpiritualESS_PopHabitat

ESS_AmenityESS_UnspecRecESS_Sightseeing

ESS_CampingESS_Boating

ESS_SwimingESS_Fishing

ESS_DrinkWaterESS_Flood

CV ChoiceExp

HedonicPrice TravelCost

frequencyGraphs by ValuMethod2

Finally, it should be mentioned that, for some lake ecosystem services, the

economic values have been obtained using a single valuation method whereas, for

others, they have been derived from multiple methods. For instance, the amenity

service has been valued mainly using an hedonic price approach whereas the

contingent valuation approaches have been mainly implemented for the “water

for drinking” service. On contrary the “fishing service” has been valued using

contingent valuation, choice experiment and travel cost approaches. This will

allow us to assess if a particular valuation method results in a systematic bias for

valuing an ecosystem services.

E. Reconciliating lake values

Lake/reservoir values have been reported in the literature in many different

metrics (i.e. willingness to pay per unit of area or volume, marginal values,

capitalized value), using different currencies and for different period of time. In

order to enable a comparison across studies all these values must be standardized.

26

As explained by Ghermandi et al. (2010) or by Londono and Johnston (2012),

the standardization of different and heterogenous metrics used to value ecosystem

services is a difficult and controversial task. We explain here how ecosystem

services values from the original studies have been normalized.

Accounting for heterogeneity in space and in time . — In our meta-database,

lake/reservoir values have been obtained for different countries (21 countries) and

for different period of time (from 1957 to 2012). This requires some normal-

ization procedures. First, to account for differences in purchasing power among

countries, a purchasing power parity indexes has to be used. Following Gher-

mandi et al. (2010), differences in purchasing power among countries have been

accounted for by using the purchasing power parity (PPP) index provided by the

PennWorld Table. As a result, all currency have been converted in USD PPP.

Second, the problem of having different years of observation is usually solved by

using appropriate price deflators, see Ghermandi and Nunes (2013) for a recent

example. Values reported for price levels other than 2010 have been converted

using national customer price indexes (CPI) provided by International Monetary

Fund (World Economic Outlook 2014). As a result, all ecosystem services values

are computed on an annual basis and they have been expressed in 2010 US$.

Similar transformations have been done for the other economic variables to be

used in the meta-regression (household income for example).

Applying the commodity and welfare consistency principles. — Based on

the requirement to work on estimates satisfying both the property of commodity

consistency for the dependent variable across observations and the property of

consistency of the welfare measure, we have decided to split our meta-dataset

into two sub-samples. The first sub-sample will include all observations for which

lake values have been obtained by an hedonic price approach. The second sub-

sample include all other remaining observations.

27

Normalized lake value for hedonic price studies. — Restricting the sub-

sample to hedonic price studies is a way to satisfy the requirement of working

on quite homogenous data in terms of good valued and welfare measure. Indeed,

the good which is valued is well identified (a property sold on a market) and,

under some assumptions on the functioning of the property market, the implicit

marginal price obtained from hedonic price studies is directly related to a measure

of consumer welfare. One specific issue with hedonic price studies is that they

give a capitalized value whereas the other valuation methods typically provide a

value estimate per unit of time. Additional data management is then required

for making estimates obtained with these different methods more comparable. In

our case, the capitalized values obtained from hedonic price studies have been

annualized assuming constant value per year, using the 30-year fixed mortgage

rate as a discount factor (for the year of the study) and considering a 30-year time

horizon.26 In our meta-analysis all values obtained from hedonic price studies have

then been normalized and expressed in monetary units per sold property and per

year.

Normalized lake value for other valuation studies. — Lake values reported

in studies which are not based on an hedonic price approach are expressed in very

different metrics including monetary units per unit of lake area per unit of time

or monetary value per household/person/trip per unit of time. Rationalizing the

use of a normalized is quite difficult is this case.

Some previous studies have used a normalized value expressed in monetary

units per unit of area per unit of time Woodward and Wui (2001), Ghermandi

et al. (2010), Brander et al. (2012), Ghermandi and Nunes (2013). When an

aggregated value for the investigated ecosystems is provided in the primary study,

26A similar procedure has been used by Woodward and Wui (2001) or Ghermandi and Nunes (2013)using the discount factors provided directly in the studies (or a 6% rate in the two studies in Woodwardand Wui (2001) that did not state any discount rate). In the empirical analysis we will test the robustnessof our annualization procedure, in particular by considering other time horizons (20 or 25 years).

28

such a normalization procedure is easy to implement.27 When no aggregated value

is provided, the study must be disregarded.

This is why some meta-analysis of ecosystem service values have excluded stud-

ies in which values are estimated per unit of area (Londono and Johnston 2012).

In that case values are usually expressed in monetary units per visit per unit of

time (Brander, Beukering, and Cesar 2007, Johnston and Rosenberger 2010) or

in monetary units per household/respondent per unit of time (Brouwer, Lang-

ford, Bateman, and Turner 1999, Johnston, Besedin, Iovanna, Miller, Wardwell,

and Ranson 2005, Johnston, Ranson, Besedin, and Helm 2006, Londono and

Johnston 2012, Ge, Kling, and Herriges 2013). We have opted for this later

normalization procedure and expressed all the values from primary studies in

monetary units per household/respondent per year. In some cases, the reported

primary study results needed to be adapted to fit the required format. For ex-

ample, values per person/visit were to be transformed into values per person

per year using data on number of visit/duration of visit. Such adjustments are

required to reconcile variable definitions across sites (commodity consistency re-

quirement), and are nearly universal in valuation meta-analyses (Johnston and

Rosenberger 2010, Nelson and Kennedy 2009).

As discussed above, it is clear that not all values reported within this category

rely on the same theoretical construct. This is for instance the case for contingent

valuation and travel cost methods which provide Hicksian and Marshallian wel-

fare measures. The welfare requirement issue will be discussed in the empirical

analysis.

A preliminary view of lake values. — For studies using an hedonic price

approach, we find a mean value of a lake equal to 769 USD$2010 per property per

year. The median value is 215 USD$2010 per property per year, showing that the

27It simply requires to divide the total value of the ecosystem by its area. Notice however thatthe methods for computing the aggregated value of ecosystems might differ from one primary study toanother. This may raise some concerns with respect to the commodity consistency requirement.

29

distribution of values is skewed with a long tail of high values. For other studies

(i.e studies not relying on an hedonic price approach), we find an annual value of

a lake equal to 348 USD$2010 per respondent and per year with a median value

equal to 106 USD$2010 per respondent and per year. A first result is that hedonic

price studies result in significantly higher lake values, compared to studies using

other valuation methods.

It might be interesting to compare the mean lake values obtained from our meta-

database with those obtained form similar/comparable meta-analysis. Brouwer,

Langford, Bateman, and Turner (1999) have conducted a meta-analysis for the

use and non-use values generated by wetlands across North America and Europe.

On average, the values we find for ecosystem services provided by lakes are higher

than the ones reported by Brouwer, Langford, Bateman, and Turner (1999) for

wetlands. Their average willingness to pay for wetland function preservation

found in all studies taken together is 134 USD$2010 per respondent and per

year.28 The median is considerably lower, namely 74 USD$2010 per respondent

and per year.

We discuss now the breakdown of lake values according to a number of possible

explanatory factors. Mean lake values have been calculated (1) by countries, (2)

by lake size classes and (3) by ecosystem services. Results are presented separately

for studies using an hedonic price approach and for studies using another type of

valuation method.

Figure 10 presents the mean annual value of lakes per country (in USD$2010 per

property per year for studies using an hedonic price approach and in USD$2010

per respondent per year for studies using another type of valuation method).

When considering hedonic price studies, United States rank first with a mean

annual value of lakes per property equal to 816 USD$2010. The following countries

in terms of values are Finland, Ireland Canada and Netherlands with 552, 543, 387

28The willingness to pay in Brouwer, Langford, Bateman, and Turner (1999) is expressed in Interna-tional Monetary Fund’s Special Drawing Rights for 1995. It has been converted in USD$2010 using anappropriate discount factor (US CPI).

30

Figure 10. Mean annual value of lakes per country and per valuation method

0 200 400 600 8002010 USD PPP

United StatesTurkey

ScotlandPolandNorway

New ZealandNetherlands

JapanItaly

IndiaGreece

GermanyFranceFinland

EthiopiaEstonia

EnglandCzech Republic

ChinaChile

CanadaCameroon

AustraliaArmenia

LakeValueHedonic LakeValueOther

and 284 USD$2010, respectively. When considering studies using another type of

valuation method, Switzerland ranks first with a mean annual value of lakes per

respondent equal to 765 USD$2010. The following countries are France, United

States and Australia. For countries where lake values are available both with an

hedonic price approach and with another valuation approach (i.e. China, England,

Finland, Netherlands, United States), we observed some significant differences

across lake values by method of valuation. This indicates that the valuation

method used in the primary study is likely to have an impact.

Another lake characteristic that we may expect to determine its value is its

size (area). There is no clear a priori expectation of the sign of this relation-

ship given on the one hand that there may be diminishing marginal returns to

most lake services as lake size increases, but on the other hand some ecological

functions require minimum thresholds of habitat area which suggests that lake

values may increase with size, see (Brander, Florax, and Vermaat 2006). For he-

donic price studies, no monotonic relationship seems to emerge between the lake

31

Figure 11. Mean annual value of lakes per size of lake and per valuation method

0 500 1,000 1,500 2,0002010 USD PPP

>1000 km2

[20,1000[ km2

[1,20[ km2

< 1 km2

LakeValueHedonic LakeValueOther

value per respondent and its size. This is consistent with previous findings having

found constant returns to scale with respect to size for some ecosystem service

values. Indeed, both Brander, Florax, and Vermaat (2006) and Woodward and

Wui (2001) conclude that the economic value of wetland services is not signifi-

cantly influenced by the size (area) of wetlands, i.e. that wetland values exhibit

constant returns to scale. When considering studies using a valuation method

different from the hedonic price one, the picture is quite different. We find in

that case a positive relationship between the size of the lake and its value. This

may indicate the presence of increasing return to scale but the result may also be

related to the fact that the biggest lakes in our meta-database are located in the

United States (lake Michigan, lake Erie and lake Ontario), a country for which

we would expect a priori high values for lake ecosystem services.

In Figure 12 we have split the annual value of lakes according to the pres-

ence or not of a specific ecosystem services (and still according to the valuation

method used in the primary study). Values reported in this Figure should not be

interpreted as the value for the the considered service since each lake in our meta-

database provides on average more than one service. This figure calls for a few

comments. In our meta-database, cultural ecosystem services provided by lakes

32

Figure 12. Mean annual value of lakes per ecosystem services and per valuation method

0 1,000 2,000 3,000 4,0002010 USD PPP

Flood

0 1,000 2,000 3,000 4,0002010 USD PPP

DrinkWater

0 1,000 2,000 3,000 4,0002010 USD PPP

Fishing

0 1,000 2,000 3,000 4,0002010 USD PPP

Swiming

0 1,000 2,000 3,000 4,0002010 USD PPP

Boating

0 1,000 2,000 3,000 4,0002010 USD PPP

Camping

0 1,000 2,000 3,000 4,0002010 USD PPP

Sightseeing

0 1,000 2,000 3,000 4,0002010 USD PPP

UnspecRec

0 1,000 2,000 3,000 4,0002010 USD PPP

Amenity

0 1,000 2,000 3,000 4,0002010 USD PPP

PopHabitat

0 1,000 2,000 3,000 4,0002010 USD PPP

Spiritual

LakeValueHedonic LakeValueOther

33

are highly valued. This is particularly true for the “spiritual and symbolic ap-

preciation” and for the “amenity” services. Interestingly, the two regulation and

maintenance services in our meta-database (i.e. “flood protection” and “maintain-

ing populations and habitats”. this is quite consistent with previous findings on

wetland ecosystem services. Indeed, in their meta-analysis of values for wetland

ecosystem services, Brouwer, Langford, Bateman, and Turner (1999) report that

the wetland function which generates the highest value is flood control, followed

by wildlife habitat provision and landscape structural diversity. More recently,

Brander, Florax, and Vermaat (2006) also report high values for biodiversity,

amenity and flood protection services of wetlands.29

IV. Meta-analysis specification and results

The above analysis of the available data in the lake valuation literature does

not allow for interactions between the various potential explanatory variables. In

order to attain marginal effects – given the interference of potentially relevant

intervening characteristics – we will use a meta-regression analysis to assess the

relative importance of all potentially relevant factors simultaneously.

A. Non-independence of estimates from primary studies

The non-independence of estimates from primary studies has been recognized

has a crucial methodological issue in the meta-analysis literature, Nelson and

Kennedy (2009). There are two main reasons why primary estimates may not be

independent of one another.

The most common one is the use by researchers of multiple estimates from the

same primary study, which implies within-study autocorrelation. Within-study

correlation is usually not the most difficult problem to solve. One simple way

to address this issue is the use of regression weighting observations (generalized

29Brander, Florax, and Vermaat (2006) indicate for instance an average annual value equal to 17000US$ for the biodiversity service of wetlands.

34

least-squares) in which each study in the data set receives equal weight, instead

of each observation as in ordinary least squares (Ghermandi, van den Bergh,

Brander, de Groot, and Nunes 2010). Another common treatment consists in

selecting a single observation per primary study. In their review of meta-analysis

in environmental economics, Nelson and Kennedy (2009) indicate that the most

common treatment for data dependencies or correlation is the use of a single

observation per primary study (30 studies over a total of 140 studies reviewed).

A second reason is that the primary studies (from which primary estimates are

taken from) may not be independent of each other, which implies between-study

autocorrelation. Nelson and Kennedy (2009) mentions several potential sources

for between-study correlation: (1) some primary studies may utilize the same

data sources or may have conducted on the same area, (2) the analyst may apply

a similar adjustment to the primary data, (3) some primary studies may share

an unobservable characteristic such as similar management of an environmental

commodity at different locations, or (4) several primary studies may share an

observable characteristic, such as an identical functional form, omission of a key

explanatory variable, or data drawn from the same study location.

There are various ways to address the issue of between-study correlated observa-

tions. A first solution consists in using specific panel data models, see Rosenberger

and Loomis (2000). A second approaches is to rely on a multilevel modelling ap-

proach (MLM) which allows the regression coefficients to vary randomly across

groups, creating composite errors see Brouwer, Langford, Bateman, and Turner

(1999), Bateman and Jones (2003), Londono and Johnston (2012) among others.

It should be stressed that the random-effect model for panel data matches the

multilevel model with random intercept commonly used in this literature. Its

estimation via panel-data software produces results that are virtually identical to

its estimation using hierarchical/multilevel software, Nelson and Kennedy (2009).

This is the approach we have chosen to follows here.

35

B. Empirical specification and estimation strategy

The dependent variable in our regression equation is is the natural logarithm

lake values in USD per year in 2010 prices, labelled ln y. The explanatory variables

are grouped in different matrices that include the ecosystem services provided by

the lake (with potential interactions across ecosystem services) in ES, the water

body characteristics in Xb (i.e., type of water body, size of water body, etc.),

the study characteristics in Xs (i.e., survey method, payment vehicle, elicitation

format, etc.) and context-specific explanatory variables in Xc.

There are two popular panel-data models which can be used for estimating

the meta-regression model, e.g. the fixed-effect model and the random-effects

model. The crucial difference between these two models lies on the assumptions

used to define the error variance. In the fixed-effect model it is assumed that all

studies included in the meta-analysis share a common true effect size, differences

in observed effects arise only due to sampling error. However because studies

commonly differ in implementation and underlying population, among others,

the assumption of the fixed-effect model is often implausible. The random-effects

model allows the true effect size to differ from study to study and this is the

approach we have chosen to follows here. The base meta-analytical regression

model is specified as follows:

(1) ln yij = α+ γESij + βbXbij + βsXs

ij + βcXcij + µi + εij

where the subscript i takes values from 1 to the number of studies and subscript

j takes values from 1 to the number of observations, α is the constant term,

µj is an error term at the second (study) level, εij is an error term at the first

(observation) level and the vectors βb, βs, βc, γ contain coefficients to be estimated

by the model on explanatory variables inXb, Xs, Xc, ES, respectively. We assume

that µj and εij follow a normal distribution with means equal to zero and that

they are uncorrelated, so that it is sufficient to estimate their variances, σ2µ and

36

σ2ε , respectively.

The first group of moderators, ES, consists in a set of dummy variables rep-

resenting all ecosystem services provided by the lake under consideration (Flood,

DrinkWater, Fishing, Swimming, Boating, Camping, Sightseeing, UnspecRec,

Amenity, PopHabitat, Spiritual). We also include a set of dummy variables

when two ecosystem services are jointly provided by the lake (PopHabitat ×

Fishing, PopHabitat×Swimming, PopHabitat×Boating, PopHabitat×Sightseeing).

The second set of moderators, Xb, include some characteristics of the water

body. Natural is a dummy variable equal to 1 if the water body is natural. In

order to capture a size effect, a set of dummy variables depending on the water

area of the lake has been included. The reference category is lake with a water

area lower than 1 km2.

The third group of moderator variables, Xs, control for specific characteris-

tics of the primary studies. As a proxy for quality of the study, we include

a dummy variable (PeerReviewed) which takes the value of one to distinguish

studies that have been published in refereed journals from those published as

book chapters, dissertations, working papers or conference proceedings. We also

introduce a set of dummy variables depending upon the valuation method used in

the primary study (ChoiceExp,HedonicPrice, T ravelCost,Other), the reference

category being contingent valuation.

The last group of moderator variables Xc, includes some context variable which

were not directly available from the primary studies. GDPcapita gives the GDP

per capita based on annual and country-level figures provided by the World Bank.

Three variables have been introduced to capture water scarcity within the river

basin of each study site (Water Stress, Water Variability and Drought Index).

These variables are based from the GIS datasets developed within the Aqueduc

project. In addition we make use of a GIS to compute a spatially explicit vari-

able (Lakeabundance) representing lake and wetland abundance for each primary

study site. Using the Global Lakes and Wetlands Database we compute the lake

37

and wetland abundance as the share of lake and wetland area within a 100 km

radius. This variable is intended to capture the availability of substitute or com-

plementary sites within the vicinity of each study site.

C. Meta-regression results

As a starting point of the analysis, we provide in Table 2 some estimates of the

meta-analytical models using random-effects approach, for primary studies which

have not used an hedonic price approach (i.e for primary studies relying on choice

experiments, contingent valuation, travel cost and other methods).

38

Table2—

Est

imatesofthemeta-analy

ticalwithrandom-effects–Nonhedonic

pricest

udies

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Eco

syst

emse

rvic

es

Flo

od

0.0

61.4

5-0

.49

1.4

50.1

51.3

21.2

71.2

8

Dri

nkW

ate

r1.2

3**

0.5

21.6

4***

0.5

61.0

2*

0.5

31.0

3*

0.5

6F

ish

ing

0.5

8**

0.2

30.1

90.2

8-0

.10

0.2

6-0

.30

0.2

6

Sw

imm

ing

-0.0

80.3

0-0

.49

0.3

8-0

.56

0.3

4-0

.13

0.3

4

Boati

ng

0.4

40.3

10.2

20.3

8-0

.25

0.3

4-0

.26

0.3

3C

am

pin

g0.3

00.3

90.2

50.4

10.3

00.3

70.3

20.3

6

Sig

hts

eein

g1.5

9***

0.3

60.9

1*

0.5

11.0

9**

0.4

80.7

80.4

9

Un

spec

Rec

-0.1

80.2

2-0

.14

0.2

3-0

.19

0.2

10.1

30.2

1A

men

ity

4.5

9***

0.6

54.1

9***

0.6

54.4

9***

0.6

43.5

2***

0.6

4

Pop

Hab

itat

-0.6

0**

0.2

6-1

.65***

0.3

7-1

.77***

0.3

6-1

.54***

0.3

5

Sp

irit

ual

-0.5

20.4

9-0

.68

0.4

9-0

.11

0.4

70.2

20.4

9

Eco

syst

emse

rvic

ein

tera

ctio

ns

Pop

Hab

itat×

Fis

hin

g0.0

60.6

71.0

30.6

31.5

5**

0.6

2P

op

Hab

itat×

Sw

imm

ing

0.8

00.6

80.9

30.6

31.2

4**

0.6

0

Pop

Hab

itat×

Boati

ng

0.4

70.6

31.2

0**

0.5

80.6

80.5

8

Pop

Hab

itat×

Sig

hts

eein

g1.1

8*

0.6

81.5

6**

0.6

60.7

10.6

5

Ch

ara

cter

isti

csof

the

wate

rb

od

y

Natu

ral

-0.9

9***

0.2

7-1

.24***

0.2

8[1,2

0[

km

21.1

3**

0.5

0-0

.17

0.5

5

[20,1

000[

km

20.6

90.5

2-0

.09

0.5

1>

1000

km

20.9

2*

0.5

00.4

20.4

9

Ch

ara

cter

isti

csof

the

stu

dy

Ch

oic

eExp

-0.7

3***

0.2

6-0

.80***

0.2

5H

edon

icP

rice

0.0

0.

0.0

0.

Oth

er-1

.61***

0.4

3-1

.80***

0.4

7T

ravel

Cost

0.4

4*

0.2

30.3

9*

0.2

3P

eerR

evie

w1.6

8***

0.2

71.0

6***

0.2

9

Conte

xt

vari

ab

les

GD

Pca

pit

a0.3

0***

0.1

0

Wate

rst

ress

0.0

5**

0.0

3W

ate

rvari

ab

ilit

y-0

.14

0.4

4D

rou

ght

ind

ex0.0

00.0

2

Lake

ab

un

dan

ce7.0

9***

1.4

4

Con

stant

3.9

7***

0.1

13.0

8***

0.2

03.5

6***

0.2

62.0

7***

0.6

40.1

01.2

5

R-s

qu

are

d0.0

00.2

60.2

90.4

60.5

2N

385.0

0385.0

0385.0

0384.0

0384.0

0

***,

**,

*fo

rsi

gn

ifica

nt

at

the

1,

5,

10

per

cent

level

,re

spec

tivel

y.

39

The results obtained for different specifications of the basic metaregression

model described in Equation (1) are presented in Table 2. For all estimates

in Table 2, a series of diagnostic tests were performed.

Model 1 does not include any explanatory variable. In Model 2, only dummy

variables for the ecosystem services provided are included. The set of explanatory

variable is augmented in Model 3 by added some variables describing interaction

across ecosystem services. In Model 4 we add some variables describing the water

body and the study from which values are obtained. Lastly, context variables

are included in Model 5. After having conducted some specification tests, we

estimate the basic metaregression model described in Equation (1) using a log-

linear specification.

The first model provides an estimation of the average value of a lake per respon-

dent (84 USD$2010 per respondent and per year). The second model provides

some indications on how people value lake ecosystem services. Interestingly, six

ecosystem services (among the eleven specified) appear to be significant namely

drinking water, fishing, sightseeing, amenity, maintenance of populations and

habitats and spiritual or symbolic appreciation. A particular high value is found

for amenity (+51.4 USD$2010 per respondent and per year) whereas a low value

is documented for spiritual or symbolic appreciation.

An interesting result from Model 3 and 4 is the fact that some interactions

between ecosystem services appears to be significant. In Model 3, the inter-

action between the “maintenance of populations and habitats” service and the

“recreational sightseeing” service appears to be significant at 10% with a positive

sign, which suggests some complementarities among services. This means that

there is a particularly high value for a lake jointly providing these two services.

More specifically, the added value of a lake jointly providing these two service is

estimated to be 15.5 USD$2010 per respondent and per year. This is not sur-

prising given the potentially high level of complementarity between a providing

a high level of maintenance of populations and habitats, and the provision of a

40

recreational service like sightseeing. In Model 4, we also document some com-

plementarity between the “maintenance of populations and habitats” service and

the “recreational swimming” service.

In model 4, we have added some characteristics describing the type of water

body (natural versus artificial, size of the water body) and the study from which

a value is obtained (valuation methods). We do not find any specific premium

for natural lakes compared to artificial ones. Although some artificial lakes may

have been constructed without fully replacing natural lakes ecological functions

or without fully supporting recreational activities or aesthetic services, it appears

that they are valued by people in the same way as natural lake are. We also

find a significant impact of the valuation method used to obtain lake values. In

particular, the value obtained with a travel cost approach results in a higher lake

value (2.8 USD$2010 per respondent and per year). The impact of the valuation

method has already been document in some previous meta-analysis of ecosystem

services value, but with contrasting results. For instance for value of wetlands,

(Woodward and Wui 2001) have found positive and significant coefficients for

replacement cost and hedonic pricing, while (Brander, Florax, and Vermaat 2006)

have reported a positive coefficient for contingent valuation.

Next, in Table 3, we provide some estimates of the meta-analytical models

using random-effects approach, for primary studies having used an hedonic price

approach.

41

Table3—

Est

imatesofthemeta-analy

ticalwithrandom-effects–Hedonic

pricest

udies

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Coeff

.S

td.

err.

Eco

syst

emse

rvic

es

Flo

od

-3.2

8**

1.3

5-3

.28**

1.3

5-3

.81***

1.4

8-3

.30***

1.1

6

Fis

hin

g-9

.69*

5.7

2-9

.69*

5.7

20.0

0.

0.0

0.

Boati

ng

5.3

93.6

25.3

93.6

2-4

.71

3.2

6-4

.69

3.1

3

Am

enit

y-3

.60

2.2

7-3

.60

2.2

7-4

.46*

2.3

8-3

.77

2.4

3

Pop

Hab

itat

-4.2

33.0

4-4

.23

3.0

4-5

.90*

3.1

7-4

.89

3.0

9S

pir

itu

al

1.8

81.5

51.8

81.5

51.6

51.7

01.6

51.3

8

Eco

syst

emse

rvic

ein

tera

ctio

ns

Pop

Hab

itat×

Boati

ng

0.0

0.

12.4

3**

5.9

911.5

8**

5.8

1

Ch

ara

cter

isti

csof

the

wate

rb

od

y

Natu

ral

-0.2

20.4

8-0

.75

0.5

0[1,2

0[

km

20.6

30.6

8-0

.74

0.7

3

[20,1

000[

km

2-0

.15

0.5

2-0

.54

0.5

6

>1000

km

20.8

40.6

30.5

80.6

7

Ch

ara

cter

isti

csof

the

stu

dy

Ch

oic

eExp

0.0

0.

0.0

0.

Hed

on

icP

rice

10.5

3***

2.5

02.6

97.0

7

Oth

er0.0

0.

0.0

0.

Tra

vel

Cost

0.0

0.

0.0

0.

Pee

rRev

iew

-0.6

90.7

2-0

.44

0.5

5

Conte

xt

vari

ab

les

GD

Pca

pit

a0.8

40.6

2W

ate

rst

ress

0.2

10.5

7W

ate

rvari

ab

ilit

y-2

.85*

1.6

0

Dro

ught

ind

ex0.0

00.0

5L

ake

ab

un

dan

ce7.5

0***

2.5

4

Con

stant

5.6

2***

0.2

69.2

9***

2.3

09.2

9***

2.3

00.0

0.

0.0

0.

R-s

qu

are

dN

214.0

0214.0

0214.0

0205.0

0205.0

0

***,

**,

*fo

rsi

gn

ifica

nt

at

the

1,

5,

10

per

cent

level

,re

spec

tivel

y.

42

D. Valuing some hypothetical lakes

Table 4—Valuation of hypothetical lakes

Lake A Lake B Lake C

Ecosystem services providedFloodDrinkWater × × ×FishingSwimming × ×BoatingCamping ×Sightseeing × ×UnspecRecAmenityPopHabitat × ×Spiritual

Lake characteristicsNatural × × ×[20, 1000[ km2 × × ×

Value of the lake295.9* 223.6* 340.4*

*(in USD$2010 per respondent and per year.

In this paragraph, we demonstrate how the results of the meta-analysis may be

used to provide some predictions for the value of some hypothetical lakes. We will

consider three different lakes for which we would like to predict the value (with

a travel costs approach). Lake A is a natural lake with a total area belonging

to the second size class ([20, 1000[ km2). This lake is supposed to provide two

ecosystem services namely drinking water and maintenance of populations and

habitats. Applying the valuation function from Model 4 in Table 2, the predicted

value of this lake is estimated to be 295.6 USD$2010 per household and per year.

Next, we consider another lake (lake B) similar to lake A except that instead of

providing the “maintenance of populations and habitats” service, it provide two

recreational services namely “sightseeing” and “swimming”. Applying again the

valuation function from Model 4 in Table 2, we obtain a predicted value for this

lake equal to 223.6 USD$2010 per household and per year. Comparing lake A and

B value, we can conclude that the loss of the “maintenance of populations and

43

habitats” service is not fully compensated by the gain of the “sightseeing” and

“swimming” recreational services, for the lake characteristics we have consider

here. This very simple simulation exercise allows to quantity the relative benefit

of each ecosystem service. If we add the “camping” service to lake B (i.e we

pass from lake B to lake C), then the predicted value is estimated to be 340.4

USD$2010 per household and per year. Allowing people to benefit from the

three recreational services “sightseeing”, “swimming” and “camping” cancels out

the loss of welfare resulting from loosing the “maintenance of populations and

habitats”.

V. Conclusion

Today, while there is now widespread recognition that lakes provide valuable

ecological services, there remain substantial debates on their economic value. As

a result, lake ecosystems are often undervalued in decisions related to their use,

conservation or restoration.

In this paper, estimates for values attached to different ecosystem services pro-

vided by lakes and reservoirs have been compared and synthesised in a meta-

analysis. The meta-analysis provides insights into the factors that have to be

considered when attempting to transfer lake values on the basis of the valuation

studies. We have provided some preliminary estimates first using OLS.

We provide an estimation of the average value of a lake per household (84

USD$2010 per respondent and per year) and we offer some insights on how peo-

ple value different lake ecosystem services. A particular high value is found for

lake amenity whereas a low value is documented for spiritual or symbolic appreci-

ation of the lake. An interesting result is the fact that some interactions between

ecosystem services appear to be significant for enplaning lake values. This tra-

duces some trade-offs, synergies and antagonisms between ecosystem services as

already documented by (Fu, Su, Wei, Willett, L, and Liu 2011, Raudsepp-Hearne,

Peterson, and Bennett 2010) for landscape. We then show that the value of a

44

specific lake ecosystem service might depend upon other ecosystem services pro-

vided by this lake. Not introducing the potential interactions across ecosystem

services may then lead to biased estimates of ecosystem service values.

The work presented in this paper is in progress. More variables describing

lake’s characteristics and methodologies used in the primary studies should be

introduced. A more substantial comparison of our meta-regression results and

the existing meta-analyses in this field is planned to be carried out to extend the

paper (in particular by including the different lake values we have mentioned in

the descriptive part of the paper). Another step in the analysis presented here

will be to test the performance of in and out of sample transfers based on the

estimated meta-regression model. This implies that additional control factors

should be included for the countries and regions in which the valuation studies

were conducted in the spirit of what has been done by (Brander, Bruer, Gerdes,

Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk

2012) for wetlands (Ghermandi and Nunes 2013) for costal areas.

45

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