Contents lists available at ScienceDirectJournal of Enviro

Contents lists available at ScienceDirect
Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
Research article
Climate change and the provision of biodiversity in public
temperate forests
– A mechanism design approach for the implementation of
biodiversity
conservation policies
Andrey Lessa Derci Augustynczik∗ , Rasoul Yousefpour, Marc
Hanewinkel
Chair of Forestry Economics and Forest Planning, University of
Freiburg, Tennenbacherstr. 4, 79106, Freiburg, Germany
A R T I C L E I N F O
Keywords:
Forest biodiversity
Mechanism design
Forest optimization
Conservation planning
Forest birds
A B S T R A C T
The provision of forest biodiversity remains a major challenge
in the management of forest resources.

Biodiversity is mostly considered a public good and the fact
that societal benefits from biodiversity are private
information, hinders its supply at adequate levels. Here we
investigate how the government, as a forest owner,
may increase the biodiversity supply in publicly-owned forests.
We employ a mechanism design approach to find
the biodiversity provision choices, which take into account
agents’ strategic behavior and values towards bio-
diversity. We applied our framework to a forest landscape in
Southwestern Germany, using forest birds as
biodiversity indicators and evaluating the impacts of climate
change on forest dynamics and on the costs of
biodiversity provision. Our results show that climate change has
important implications to the opportunity cost
of biodiversity and the provision levels (ranging from 10 to
12.5% increase of the bird indicator abundance). In
general, biodiversity valuations needed to surpass the
opportunity cost by more than 18% to cope with the
private information held by the agents. Moreover, higher costs
under more intense climate change (e.g.
Representative Concentration Pathway 8.5) reduced the
attainable bird abundance increase from 12.5 to 10%.
We conclude that mechanism design may provide key
information for planning conservation policies and
identify conditions for a successful implementation of
biodiversity-oriented forest management.
1. Introduction
The provision of biodiversity remains a major challenge in the
management of forest resources. Biodiversity has been
continuously
declining worldwide during the past decades, despite its
recognized
importance to human well-being, ecosystem functioning and
ecosystem

resistance and resilience under climate change (Díaz et al.,
2006; Isbell
et al., 2015; Tilman et al., 2014). A main constraint to the im-
plementation of biodiversity conservation strategies is the fact
that
biodiversity is mostly considered a public good, and in the
absence of
markets or policy mechanisms to promote its provision, there
are in-
centives for free riding and undersupply. One option to tackle
this issue,
is to enhance biodiversity goals in public forests. The
government, as a
forest owner and aiming to promote an efficient use of forest
resources,
may raise funds and apply biodiversity-oriented management
solutions
in these areas. Thereby, it is possible to mitigate the
discrepancy be-
tween current and efficient biodiversity supply, promoting
sustain-
ability and increasing social welfare (Kemkes et al., 2010).
The promotion of biodiversity in forest landscapes demands a
cost-
benefit analysis of biodiversity-oriented management strategies,
com-
patible with societal preferences for multiple forest goods and
services.
This requires that both social costs and benefits related to
biodiversity
are known. The quantification of costs is a straightforward task,
e.g.
through the computation of the opportunity costs of
biodiversity-or-

iented forest management or the value of contracts for
biodiversity
amelioration (Rosenkranz et al., 2014). Conversely, the
evaluation of
biodiversity benefits involves indirect assessments,
predominantly ap-
plying choice experiments, where participants are asked how
much
they would be willing to contribute towards an increased
biodiversity
supply or by eliciting their preferences for bundles of ecosystem
ser-
vices (e.g. Meyerhoff et al., 2012; Getzner et al., 2018; Iranah
et al.,
2018). A key issue when considering biodiversity benefits, is
the fact
that the preferences for biodiversity are private information, and
policy
makers may have at hand only prior beliefs (e.g. the probability
dis-
tribution of these preferences), in terms of the willingness-to-
pay (WTP)
for biodiversity. This means that agents may have the incentive
to
misrepresent their true preferences when asked to contri bute
towards
the cost of biodiversity provision, hindering the implementation
of
https://doi.org/10.1016/j.jenvman.2019.05.089
Received 4 February 2019; Received in revised form 13 May
2019; Accepted 21 May 2019
∗ Corresponding author.
E-mail address: [email protected] (A.L.D. Augustynczik).

Journal of Environmental Management 246 (2019) 706–716
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biodiversity conservation programs.
Mechanism design is arguably the best tool to address problems
of
this nature. Mechanism design is a sub-field of game theory,
also known
as inverse game theory. This framework searches for the design
of
games (e.g. auctions and voting schemes) that will lead to a
desired
outcome, such as welfare maximization or other economic goals
(Nisan
and Ronen, 2001). In this sense, mechanism design can be
applied to a
variety of natural resource management problems that involve
asym-
metric information. Formally, a mechanism is composed by a
social
choice function and a payment rule ensuring agents have

incentive to
participate in the mechanism and are not better-off by
misrepresenting
their true valuations. The mechanism designer can then make
use of
these functions to decide upon the implementation of
management
solutions.
Here we use this framework to tackle the increase in
biodiversity
supply in public forests, taking into account the societal
preferences for
biodiversity and the strategic behavior of the agents. In our
setting, the
government proposes a mechanism to raise capital to cover the
costs of
an increased biodiversity supply. According to Rands et al.
(2010), to
increase the success of conservation policies it will be
necessary to
prioritize the management of biodiversity as a public good, to
integrate
biodiversity in both public and private decision-making and to
facilitate
policy implementation. These priorities may be combined under
the
mechanism design framework. Thereby, we are able to
characterize the
supply levels that can be actually realized under the private
information
held by the agents and what the minimum conditions are, in
terms of
the social benefit, that enable the implementation of
conservation po-
licies without the need of external funding. This is crucial to

create
more resilient forest landscapes in the future.
A variety of mechanisms have been studied for the private
supply of
public goods. For example, Güth and Hellwig (1986) and Csapó
and
Müller (2013) provide a framework for defining social choice
functions
and payment rules for the private supply of public goods.
Bierbrauer
and Hellwig (2016) and Grüner and Koriyama (2012) analyze
the
provision of public goods in voting mechanisms. Güth and
Hellwig
(1986) highlight that a further difficulty in the supply of public
goods
arise when a large number of participants are involved, which is
typi-
cally the case for the implementation of biodiversity
conservation po-
licies. The same authors show that in this case, the probability
that a
single participant affects the supply of the public good is small,
leading
them to reduce their willingness-to-pay and contributions
toward the
cost of the public good. Hellwig (2003) addressed this issue and
re-
ported that in the case that the supply level of the public good is
bounded, the costs are independent of the number of agents and
the
number of participants is sufficiently large, then the public
good is
eventually provided.

Traditionally, voluntary mechanisms to increase biodiversity
pro-
vision in forest landscapes have been addressed by game
theoretical
models. These include, for example, auctions to assign forest
reserves
(e.g. Hartig and Drechsler, 2010) or auctions for bundles of
ecosystem
services (Roesch-McNally et al., 2016). Despite the large body
of lit-
erature dealing with the characterization of mechanisms for the
pro-
vision of public goods, and their suitability to address a range
of natural
resource management problems involving the private supply of
public
goods, mechanism design applications are still largely missing.
Apart from the private information regarding the valuations for
biodiversity, a further challenge for the implementation of
biodiversity-
oriented management refers to the uncertainty induced by
climate
change on the dynamics of both forest and forest biota. Climate
change
is expected to modify a variety of forest processes and
interactions, e.g.
forest growth rates, species composition and disturbance
activity
(Lindner et al., 2014). These processes are closely related to
forest
profitability and are therefore predicted to cascade to the
opportunity
costs of biodiversity-oriented management. These novel
environmental
conditions will demand new management solutions to anticipate

cli-
matic impacts. Therefore, a sensible analysis of mechanisms
targeting
biodiversity provision needs to consider future forest
development and
its impacts on the implementation of conservation policies.
Still there is a major gap in the literature regarding the
definition of
adequate provision levels of forest biodiversity taking into
account
social costs and benefits. Moreover, the strategic behavior of
agents
towards contributions to implement biodiversity-oriented
management
is usually neglected. Here we tackle these issues by integrating
ecolo-
gical and economic aspects of forest biodiversity, using the
machinery
of mechanism design. We build upon the frameworks developed
by
Hellwig (2003) and Csapó and Müller (2013), and consider the
provi-
sion of biodiversity in publicly owned forests in a temperate
forest
landscape under climate change, using a coupled ecological -
economic
framework. We addressed in this study the following research
ques-
tions:
• What are the opportunity costs of biodiversity-oriented
management
in a temperate forest landscape under climate change?

• What are the minimum conditions for a feasible
implementation of
biodiversity-oriented forest management in terms of societal
bene-
fits?
• What are the impacts of climate change on the social choice
function
and what are the optimal management solutions to realize an in-
creased biodiversity supply?
To answer the research questions, we applied our mechanism
design
framework to a temperate forest landscape in southwestern
Germany.
To account for climate impacts on forest development and
opportunity
cost, we used the process-based forest growth model 4C under
three
different climate change scenarios and applied five management
stra-
tegies: 1) biodiversity provision; 2) biomass production; 3)
business-as-
usual (BAU); 4) climate adaptation and 5) no management. We
built an
optimization model to maximize forest Net Present Value (NPV)
in an
80-years planning horizon. To define the mechanism, we
considered
biodiversity valuation data from an extensive choice experiment
con-
ducted in Germany, defining the thresholds for the supply of
biodi-
versity under climate change, in terms of the social value of
biodi-
versity. We consider the case where the designer represents the

federal
state (responsible for the management of forest resources) and
agents
represent the six administrative regions the study area,
negotiating on
behalf of their population and deciding upon the contribution
towards
the biodiversity supply cost. Although we conduct our analysis
in a
temperate forest landscape in Germany, the framework proposed
is
flexible and can be easily adapted to different biomes and
conservation
practices, as long as cost and benefits related to biodiversity
supply are
available.
2. Material and methods
We conducted our analysis following three steps. Initially, we
ap-
plied a climate-sensitive growth model to evaluate forest growth
dy-
namics under climate change, assessing the opportunity cost of
biodi-
versity-oriented forest management with the help of an
optimization
model. Subsequently, we computed the social benefits of
biodiversity,
applying the results of a choice experiment conducted in our
research
area and derived a social choice function for the implementation
of
biodiversity-oriented forest management. Finally, we evaluated
climate
impacts on the social choice function and how the required

increase in
biodiversity supply can be realized through forest management.
2.1. Data
To evaluate forest development under climate change, we used
forest inventory data from 98 1-ha plots located in publicly-
owned
forests in Southwestern Germany, following two design
gradients of
forest cover (< 50%, 50–75% and > 75 in 25 km2 radius) and
forest
structure (< 5 habitat trees/ha, 5–15 habitat trees/ha and > 15
habitat
A.L.D. Augustynczik, et al. Journal of Environmental
Management 246 (2019) 706–716
707
trees/ha). The forest inventory recorded tree species identity,
the DBH
of all trees (with DBH > 7 cm), height of 7% of the trees.
Moreover,
lying and standing deadwood amounts were assessed. These
plots were
used to estimate the average forest responses for each forest age
class.
Based on these responses, we performed the optimized forest
planning
of 17503 publicly owned forest stands in the southern Black
Forest,
covering an area of 54227 ha. The forest in the region is
dominated by

Norway spruce (Picea abies (L.) H. Karst.), European beech
(Fagus syl-
vatica L.) and silver fir (Abies alba Mill.).
To assess biodiversity, we used the German Biodiversity
Strategy
indicator, given by the abundance of 10 forest bird species
(from which
seven were present in our research area), in relation to the
abundance
in the 1970's (BMUB, 2015). We computed the responses of the
bird
assemblage to different forest management alternatives in each
of our
plots applying the hierarchical Bayesian model developed in
Augustynczik et al. (2019) for the same study area.
The preferences for biodiversity were based on the results from
the
choice experiment conducted by Weller and Elsasser (2017),
assuming
homogeneous preferences. The authors computed the WTP from
an
increase in the forest biodiversity indicator used in the German
Biodi-
versity Strategy, i.e. an increase in the abundance of 10
indicator bird
species. For assessing the affected population, we retrieved the
popu-
lation statistics from the national ministry of statistics
(Statistiches
Bundesamt, 2016).
We computed forest profitability in terms of the Net Present
Value
in an 80-years planning horizon. We discounted harvesting

revenues
with a 1.5% market interest rate (Müller and Hanewinkel,
2018). This
rate represent the typical return on capital in the region and is
com-
patible with the average long term interest rates in Germany
during the
past 10 years (ECB 2019). Harvesting and planting costs were
retrieved
from Härtl et al. (2013) and for timber revenues we used the
average
market wood prices in Baden-Württemberg in 2016. Therefore,
prices
and costs were assumed to be deterministic in our analysis.
2.2. Forest simulation and biodiversity supply
To evaluate forest development under climate change, we used
the
process-based forest growth model 4C (Lasch et al., 2002). 4C
describes
forest processes at tree and stand scale under changing
environmental
conditions and is capable of simulating a variety of management
in-
terventions, including thinning, planting and final harvesting
(Lasch
et al., 2005). A detailed description of the model is available in
the
model webpage (www.pik-potsdam.de/4c/). Since our plots
spanned
over more than one forest stand, we used the growth model
Sibyla
(Fabrika, 2007) to estimate the average age of each plot and
subse-
quently computed the average responses of each forest age class

based
on the plot average age. Sibyla is an individual-tree model that
uses the
stand generator STRUGEN (Pretzsch, 1997) to generate a forest
stand
according to individual tree input data, enabling to derive the
average
age of each species in the stand.
We considered in our simulations five manage ment strategies
and
three climate change scenarios. The management strategies were
de-
fined as: 1) Biodiversity conservation: increase current rotation
age in
10 years, apply a thinning intensity of 10% of the standing
volume and
replace Norway spruce stands by European beech stands; 2)
Biomass
production: decrease current rotation age by 20 years, apply a
thinning
intensity of 25% of the standing volume and convert spruce
stands to
Douglas-fir (Pseudotsuga menziesii) stands; 3) BAU: maintain
current
rotation age, species composition and a thinning intensity of
17% of the
standing volume; 4) Climate adaptation: decrease current
rotation age
by 10 years, apply a thinning intensity of 20% of the standing
volume
and convert spruce stands to Scots pine (Pinus sylvestris) and 5)
No
management: no thinning, harvesting or conversion. Each plot
and
management alternative was simulated under three climate

scenarios,
given by the combination of the Global Climate Model (GCM)
HadGEM2-ES and the Representative Concentra tion Pathways
(RCP)
2.6, 6.0 and 8.5, bias-corrected by ISIMIP
(https://www.isimip.org/).
We refer to these scenarios in the remainder of this manuscript
as
Had2.6, Had6.0 and Had8.5.
To assess the increase in biodiversity provision through forest
management, we used the biodiversity index of the German
Biodiversity Strategy, which is computed based on the
abundance of 10
forest birds, used as indicator species. We used the outcomes of
the
forest growth model to predict the abundance of these indicator
forest
birds under different management options. Biodiversity supply
was
then evaluated as a flow of benefits along the simulation period.
The
bird abundance data was collected using a point count protocol
with
three repetitions in 2017, which was used to fit an N-mixture
Bayesian
hierarchical model (Eq. (1)). In the model, the community
process was
modelled using a Bernoulli distribution, the abundance of the
species
was described by a zero-inflation Poisson distribution and the
detect-
ability was evaluated through a Binomial observation model (for
details
see Augustynczik et al., 2019). Moreover, we set the abundance

of
microhabitats to its average value, due to limitations on the
detail of
input data to estimate this parameter. To provide more robust
projec-
tions, we also reduced one standard deviation from the mean
estimate
of the parameter related to the conifer share, due to the high
sensitivity
and uncertainty related to this parameter.
= + + + +
+ + +
λ φ b b Slope b Altitude b BA b ConiferShare
b Dvol b NDead b TMHA
exp (
)
i i i i i i i
i i i
0 1 2 3 4
5 6 7 (1)
Where: λi: abundance of species (N/ha); ϕi: zero inflation
coeffi-
cient; Slope: plot slope (°); Altitude: plot altitude (m a.s.l.);
BA: plot
basal area (m2/ha); ConiferShare: share of conifers (%); Dvol:
dead-

wood volume (m³/ha); NDead: number of snags (N/ha); TMHA:
tree
microhabitat abundance (N/ha).
2.3. Costs of biodiversity provision and optimal management
under climate
change
Based on the forest responses obtained in each climate change
scenario, in terms of wood production and biodiversity, we
quantified
the costs of biodiversity provision using an optimization
approach. We
constructed a linear programming model to maximize forest
profit-
ability while increasing biodiversity provision thresholds (for
details
see Appendix A). Thereby, it was possible to establish a Pareto
frontier
between NPV and bird abundance and to derive the total cost
related to
this increase in biodiversity supply. The total cost was defined
by the
difference in NPV between the baseline scenario (maximum
NPV and
no biodiversity requirement) and scenarios including
biodiversity re-
quirements (increase in bird abundance).
2.4. Biodiversity benefits
To identify efficient biodiversity supply levels, besides the
compu-
tation of costs, it is necessary to quantify its social benefits.
Here we
considered solely the non-use value of forest biodiversity, i.e.

existence
and bequest values. The biodiversity benefits were established
using
data from an extensive choice experiment conducted by Weller
and
Elsasser (2017). The authors computed the WTP for an increase
in the
forest biodiversity index used in the German Biodiversity
Strategy. In
this experiment, the authors asked the participants to choose
preferred
landscape structures and corresponding contributions towards a
land-
scape fund, according to the landscape selected. The authors
subse-
quently used conditional logit models assuming homogeneous
pre-
ferences to estimate the WTP for an increase in forest
biodiversity,
measured through the biodiversity index. Specifically, the WTP
for an
increase in the biodiversity index in 5 and 25 points compared
to the
current levels was estimated. This corresponds to a 5% and 25%
in-
crease in the abundance of the 10 indicator species compared to
baseline (1970's abundance), respectively. We used these data
to
Management 246 (2019) 706–716
708
http://www.pik-potsdam.de/4c/
https://www.isimip.org/

calibrate a constant elasticity of substitution (CES) utility
function (Eq.
(2)) for wood and biodiversity. This function was subsequently
used to
derive the benefits of intermediary biodiversity supply levels.
Based on the CES utility function (Eq. (2)), we calculated the
WTP
per unit (% increase in bird indicator abundance) (Eq. (3)) and
the sum
of benefits for our research area, correcting the WTP of the
affected
population by the income in the region. To establish the level of
bio-
diversity benefits, they were discounted and aggregated along
the 80-
year planning horizon, using a 1.5% pure time preference rate,
fol-
lowing the HM treasury (Treasury, 2003). This rate expresses
the pre-
ference of agents for consuming now rather than in the future.
We
weighted the biodiversity supply in public forests according to
the total
forest area in the study region and finally we defined the
benefits based
on Eq. (3) and the level of biodiversity provision.
= ⎡
⎣
+ − ⎤
⎦

− − −U αw α B(1 )
θ
θ
θ
θ
θ
θ1 1 1
(2)
=
− −− −
− −( ) ( )
MB
B w α P
α
( 1)1 1
θ
θ
θ
θ
1 1
(3)
where: U: utility; MB: WTP for biodiversity (per unit); B:
biodiversity
supply level; w: wood consumption; θ : elasticity of substitution

be-
tween wood and biodiversity; α: preference parameter for wood
over
biodiversity.
2.5. A second-best mechanism for biodiversity provision
To handle the asymmetric information on the biodiversity valua-
tions, we applied a mechanism design approach. We considered
that
biodiversity is supplied as a single indivisible unit. The
government
then defines a discrete level of biodiversity to be supplied in
public
forests and designs a mechanism to levy funds to cover the costs
of the
biodiversity-oriented forest management. We assumed that the
agents
display quasilinear utilities and are risk neutral.
A mechanism can be defined as tripleμ A q p( , , ), consisting of
a set
of agents' strategiesA, a social choice function q and a payment
rulep.
Let =i N1, ..., be agents that hold private information on their
pre-
ferences for biodiversity, which is defined by their type θi(here
the type
expresses the WTP of an agent). The types are independently
drawn
from the same probability distribution F x( ) with density
function f x( ),
which is assumed to have a monotone hazard rate (the ratio
−
f x

F x
( )
1 ( )
is
monotone increasing in x). The prior information on the
distribution of
types is common knowledge. Moreover, the agents know the
realization
of their own typeθi, but do not know the types of other agents.
The
government, as a designer, selects a social choice function and a
pay-
ment rule, so that the social choice function maps the vector of
types in
the decision of supplying biodiversity in publicly-owned
forestland
→q θ: [0,1] and the payment rule maps the vector of types i nto
the
N . Since biodi-
versity is a public good, the government can only enforce agents
to
contribute towards the cost of biodiversity supply by
threatening not to
implement biodiversity-oriented forest management, in case the
levied
agents’ contributions are not sufficient. It can be shown that
given some
conditions, the mechanism design problem can be simplified to
the
selection of the social choice function q (see details in

supplementary I).
In addition to the above mentioned assumptions, further
constraints
need to be included in the mechanism proposed. Specifically,
agents
need to derive non-negative benefits when participating in the
me-
chanism (in the interim phase), referred to as interim indi vidual
ra-
tionality constraint. Additionally, the mechanism needs to be
Bayes-
Nash incentive compatible, i.e. in expectation, reporting their
true types
is a dominant strategy for the agents (agents cannot be better -
off by
misrepresenting their type). Finally, the mechanism needs to be
ex-ante
budget balanced, meaning that in expectation the funds levied
by the
mechanism need to cover the cost of implementation of
biodiversity-
oriented management.
In a first-best mechanism, biodiversity is provided whenever the
sum of benefits is greater than the cost. This mechanism,
however,
imposes a loss on the supplier, due to the asymmetric
information on
the biodiversity valuations (Güth and Hellwig, 1986; Börgers,
2015).
This requires the application of a second-best mechanism,
which un-
dersupplies biodiversity but is implementable without external
funding.

Güth and Hellwig (1986) propose such mechanism, where the
designer
selects a choice function that maximizes social welfare, under
the
condition that no external funding is needed to cover the im-
plementation costs. This mechanism can be described by the
max-
imization of Eq. (4) (for a description of the parameters and
functions
see Table 1). In this setting, the sum of biodiversity valuations
need to
cover the cost of implementation, plus a fraction of the sum of
virtual
valuations. The virtual valuations appear in the second term of
Eq. (4)
and are the maximum surplus that the designer can extract from
the
agents in the mechanism.
∫
∫
∑
∑ ⎜ ⎟
= ⎛
⎝
⎜ −
⎞
⎠
⎟

+ ⎡
⎣
⎢
⎛
⎝
−
− ⎞
⎠
− ⎤
⎦
⎥
∈
∈
MaxZ q θ θ c dF θ
λ q θ θ
F θ
f θ
c dF θ
( ) ( )
( )
1 ( )
( )

( )
i N
i
S
i N
i
i
i (4)
When many agents are involved, valuations are reduced, due to
the
fact that each agent has a decreasing influence on the final
decision and
expects that the public good will be provided anyways. Hellwig
(2003)
shows that the valuation of an agent is corrected by the
probability that
the agent is focal to the decision of supplying the public good.
This
probability approximates N1 and the expected revenue of the
me-
chanism is proportional to Eq. (5):
∑ ⎜ ⎟= ⎛
⎝
−
− ⎞
⎠∈
R θ

N
θ
F θ
f θ
( )
1 1 ( )
( )i N
i
i
i (5)
(Hellwig, 2003).
Here we adopted a numeric optimization approach to solve the
mechanism design problem. If we replace the continuous type
space of
of 0 with
probably 0, the optimization problem of the mechanism designer
ad-
mits a Linear Programming (LP) representation (Vohra, 2012).
Using
this framework, we constructed an optimization model to find a
second-
best mechanism, as proposed by Güth and Hellwig (1986). We
used an
Integer Linear Program, based on the optimization approach
introduced
by Csapó and Müller (2013). Here we aimed to find a second-
best

mechanism that maximizes social surplus, while respecting ex-
ante
budget balance, interim individual rationality and Bayes-Nash
incentive
compatibility:
∑ ∑= ⎛
⎝
⎜ −
⎞
⎠
⎟
∈ ∈
MaxZ π q
N
θ c
1
θ Θ
θ θ
i N
i
(6)
Table 1
Description of parameters and functions used in the second-best
mechanism.
Parameter/Function Description

N Number of agents
θi Type of agent i
θ Profile of agents' types
q θ( ) Social choice function
c Cost of provision
F θ( ) Cumulative probability density of agents' types
λS Lagrange multiplier of the ex-ante budget balance
constraint
f θ( ) Probability density function of agents' types
R θ( ) Sum of virtual valuations of profile θ
Management 246 (2019) 706–716
709
∑ ∑− ≥ ∀ ∈ ∀ ′ ∈ > ′
∈ ′∈
′ ′π q π q i N θ θ Θ θ θ0 , , |
θ Θ
θ θ
θ Θ
θ θ i ii i
(7)
∑ − ≥
∈

π q R θ c( ( ) ) 0
θ Θ
θ θ
(8)
∈ ∀ ∈ q θ Θ{0,1}θ (9)
The objective function (Eq. (6)) maximizes the expected social
surplus, given by the sum of valuations minus the cost of im-
plementation, over the type space. Constraint (Eq. (7)) ensures
Bayes-
Nash incentive compatibility and interim individual rationality,
enfor-
cing monotonicity of the choice function in respect to the
agent's type.
Interim individual rationality constraints enforce that agents
receive a
nonnegative utility for participating. Bayes-Nash incentive
compat-
ibility ensures that agents have no incentives to misrepresent
their
types. Constraint (Eq. (8)) enforces ex-ante budget balance,
which re-
quires that, in expectation, the costs of biodiversity provision
are cov-
ered by the contributions of the agents and (Eq. (9)) ensures
that the
choice function takes binary values. For a description of sets,
variables
and data used in the optimization models see Table 2.
To build our optimization problem, we assumed that agents’ va-
luations were composed by 5 types =Θ {1,2,3,4,5} that were
uniformly
distributed inside the confidence interval of biodiversity

valuations
established in section 2.4. Moreover, we considered 6
agents =N {1,2,3,4,5,6}, each representing an administrative
region with
an equal share of the population (negotiating on its behalf) that
need to
agree on the implementation of biodiversity conservation
policies in
public forests. Each agent also needs to contribute towards the
cost of
the increased biodiversity supply, representing fund transfers in
a fiscal
federalism framework (Bönke et al., 2013). Finally, we analyzed
the
implementation of 6 levels of bird indicator abundance in-
crease =B {2.5%, 5%, 7.5%, 10%, 15%, 17.5%}. Besides the
uncertainty
regarding the realization of the vector of valuations of the
agents, the
government must also consider that the costs of biodiversity-
oriented
management are uncertain and contained in =C c c c{ , , }Had
Had Had2.6 6.0 8.5 .
In our analysis, we investigated the social choice on the
expected cost
= + +c c c c( )/3Had Had Had2.6 6.0 8.5 and on each climate
scenario as a sen-
sitivity analysis (Barbieri and Malueg, 2014). We disconsidered
the
trivial cases, where biodiversity should never be provided (the
costs are
lower than the sum of valuations if all agents have the lowest
type) and
never be provided (the costs are higher than the sum of
valuations if all

agents have the highest type). We solved the optimization model
using
the software Gurobi8.1
(http://www.gurobi.com/products/gurobi-
optimizer).
3. Results
3.1. Costs of biodiversity provision under climate change
We perceived that up to a 10% increase in the current bird
indicator
abundance at the end of the century, the opportunity costs
increased
almost linearly with the biodiversity requirements, whereas for
abundance increases above this threshold, it was necessary to
strongly
compromise forest profitability. This behavior is depicted in the
cost
curves (Fig. 1), where we noticed a sharp opportunity cost
increase for
high levels of bird abundance. This was a result of the limits on
the
conversion of highly profitable spruce by beech stands (with
lower
growth rates and wood value) for increasing the share of
broadleaved
forests in the region and the need to reduce the area of more
profitable
management strategies.
Climate change had important implications for the total cost of
biodiversity provision. The increase in forest growth rates under
higher
atmospheric CO2 concentration, in combination with sufficient

pre-
cipitation, led to an increase in forest growth rates and
consequently
higher forest profitability and opportunity cost. This was
determinant
for the attainable level of biodiversity supply under the
mechanism
design approach, since the total supply cost was required to be
met by
the contributions of the agents considered in our analysis.
In addition to climate impacts, the cost behavior was also
related to
the biodiversity responses to forest management in our model.
We used
the N-mixture model described in section 2.2 to estimate the
bird
abundance under novel forest structures generated by the
alternative
management regimes applied in our analysis. Three main
parameters
used to estimate bird abundance were affected by management:
the
basal area of the stand, the share of conifers and the number of
snags.
Among these parameters, the bird assemblage was most
responsive to
the share of conifers and responded marginally to the number of
snags
and the basal area of the stand. Given that the increase in the
snag
number has important economic implications due to the
reduction in
thinning revenues, the increase in the share of broadleaves was
the
most cost-effective management practice to increase

biodiversity supply
and reach the levels required by the mechanism. This
management
action, however, also reduced forest profitability due to
conversion
costs.
3.2. Second-best mechanism for biodiversity provision
The first step taken in the analysis of the mechanism was the
identification of the trivial cases, in which biodiversity is never
pro-
vided or always provided (the social choice function always
equal to 1
or 0 regardless of the profile realization), based on the lowest
and
highest possible sum of valuations. For the average cost
scenario, the
non-trivial case yielded a bird abundance increase of 12.5% at
the end
of the century (Fig. 2) and bird abundance increases below this
value
were nearly always provided.
The optimal choice function, i.e. the threshold related to the
sum of
Table 2
Sets, variables and input data used in the mechanism design
model.
Sets Description
Θ Set of profile realizations
N Set of agents
Variables

qθ Binary variable that takes value 1 if profile θ is included in
the solution
and value 0 otherwise
Data
θ Profile of agents' types
πθ Probability of observing profile θ
c Opportunity cost of biodiversity provision
R θ( ) Sum of virtual valuations of profile θ
Fig. 1. The figure shows the total opportunity cost for
increasing bird abun-
dance levels at the end of the century for each climate change
scenario.
Management 246 (2019) 706–716
710
http://www.gurobi.com/products/gurobi-optimizer
http://www.gurobi.com/products/gurobi-optimizer
valuations that would lead to the implementation of
biodiversity-or-
iented management is depicted in Fig. 2. As expected, the
second-best
mechanism undersupplied biodiversity. We perceive that the
social
choice function was only activated if the sum of valuations
surpassed 89
Million EUR, whereas the first-best mechanism would
implement bio-
diversity-oriented management for valuations above 78 Million
EUR.

Thus, the sum of valuations was required to exceed the cost of
im-
plementation by more than 14%, taking into account the agents
de-
scribed in our model. This was a result of the information rent
held by
the agents on their valuations. Under these conditions, the
probability
of implementation of biodiversity-oriented management was
24%, i.e.
in 24% of profile realizations the social choice function would
be ac-
tivated. Additionally, to maintain Bayes-Nash incentive
compatibility
and interim individual rationality, the payment rule would
require
agents with types 1, 2, 3, 4 and 5 to contribute with an
equivalent of
100, 90, 86, 83 and 81 % of their WTP, respectively. We
perceived that
biodiversity-oriented management was mainly implemented for
profiles
with a combination of high biodiversity valuations (e.g. types 4
and 5,
with the two highest biodiversity valuations according to the
distribu-
tion used in our analysis). This requirement yielded a reduced
prob-
ability of implementation, since all agents displayed
simultaneously
high valuations in a limited number of profile realizations. On
the other
hand, if the first-best solution was considered and external
funding was
feasible, the probability of implementation would increase to
84%, due

to the lower threshold of implementation.
In our model, we required the mechanism to be budget balanced
in
expectation. This condition, however, does not guarantee that
the funds
raised will cover the costs of implementation in all profile
realizations.
This also caused an undersupply compared to the first-best
solution,
which affected the expected surplus of the mechanism. The
first-best
case, disregarding the budget balance condition, would yield an
ex-
pected surplus of 7.5 Million EUR for the agents, whereas for
the
second-best case this figure amounted to 3.8 Million EUR. We
highlight
that, despite the lower expected surplus, this mechanism still
produces
a larger social benefit than the profit maximization mechanism
(ex-
pected surplus of 1.9 Million EUR).
Although the optimization models generated through the me-
chanism design model had high dimensionality, with 15.625
binary
variables and 165.625 non-zeros, the optimal solution could be
effi-
ciently computed, with processing time inferior to 1 s. We
emphasize
that the problem size may become computatio nally prohibitive
when
the number of types and agents is large, since the number of
profile

realizations is proportional to T N . In such cases, heuristic
solutions
may be required to compute the optimal mechanism.
3.3. Sensitivity analysis and management solutions
Climate change had a substantial effect on the choice function
due
to the varying implementation cost (Fig. 3). For the Had2.6 and
the
Had6.0 climate scenarios, the same level of bird abundance
increase
was observed (12.5%). Nevertheless, for the Had2.6 scenario,
the lower
opportunity cost led to a probability of implementation equal to
66%,
whereas for the Had6.0 it reduced to 16%. Similar to the
average cost
scenario, a higher probability of implementation was observed
for the
first-best case (> 99% for the Had2.6 and 76% for the Had6.0
sce-
nario). Considering the Had8.5 scenario, the increase in bird
abundance
amounted to 10% at the end of the century, with a probability of
im-
plementation of 76% in the second-best case. Under such
conditions,
biodiversity-oriented management would be implemented if the
sum of
the valuations surpassed 76 Million EUR, whereas in the fist-
best so-
lution the social choice function would be activated for profiles
with
valuation above 63 Million EUR.

The optimal portfolio for increasing levels of bird abundance in
each
climate scenario is shown in Fig. 4. We observed that the
increase in
bird abundance requirements caused a reduction in the area
under BAU
and biomass-oriented management, whereas the biodiversity
manage-
ment strategy largely increased. Hence, depending on the costs
of
biodiversity supply, the allocation related to the second-best
me-
chanism differed. For example, the Had2.6 scenario would
require the
biodiversity strategy in approximately 28% of the total area,
whereas
for the Had8.5 climate scenario, the biodiversity strategy would
be
reduced to 21% of the total area. Additionally, for a same level
of bird
abundance increase, climate change required tailored
management re-
gimes. The optimal portfolio under the Had2.6 scenario applied
the no
management strategy in 6% of the total area, whereas the same
figure
was reduced to 2% in the Had6.0 scenario. In general, more
intense
climate change led to a reduction in the area under no
management and
an increase in the area of the biomass production strategy.
4. Discussion
Here we analyzed how the government may implement biodi-
versity-oriented forest management in public forests, in order to

reduce
the gap between efficient and current levels of forest
biodiversity in
temperate ecosystems. We computed the costs of biodiversity
provision
and applied a mechanism design approach to account for the
strategic
behavior of agents related to the contribution towards this cost.
We
defined social choice functions for biodiversity supply in public
forests
under climate change and computed optimal management
solutions to
realize the required biodiversity indicator increase.
4.1. Costs of biodiversity provision under climate change
The total costs of biodiversity provision were moderate with up
to a
10% increase in bird abundance at the end of the century,
ranging
approximately between 892 and 1180 EUR/ha, whereas for the
max-
imum biodiversity provision within our modelling framework
increased
by up to 4346 EUR/ha. Since the conversion to broadleaved
forests is
bounded by the current area of Norway spruce, it was necessary
to
increase the abundance through less efficient management
interven-
tions and increasing the opportunity cost, e.g. applying
management
with low thinning intensity to increase mortality and snags
availability.
Rosenkranz et al. (2014) evaluated the implementation costs of

the
Habitats Directive in Germany and reported average loss of
income
Fig. 2. Social choice function value for the average cost across
the climate
scenarios. BAI stands for the level of bird abundance increase
in the non-trivial
provision level. The dotted vertical line shows the opportunity
cost for the
corresponding increase in bird abundance.
Management 246 (2019) 706–716
711
ranging from 1958 to 2496 EUR/ha, depending on the
management
applied and discounted with a 1.5% interest rate. Hily et al.
(2015)
analyzed the cost effectiveness of Natura 2000 contracts in
France, with
an average cost of contracts approaching 1900 EUR/ha.
Climate change and its implications to forest dynamics were
also
important drivers of the costs of biodiversity provision and,
thus, cas-
caded to the mechanism implementation. Climate scenarios with
in-
creased forest productivity showed higher opportunity cost,
since the
profitability of conifer stands increased. An important aspect of

forest
development under climate change not investigated here refers
to the
occurrence of forest disturbances. Disturbances may modify
forest
profitability and interact with forest biodiversity, altering
conservation
costs (Hanewinkel et al., 2013; Seidl et al., 2017), and affecting
the
probability of biodiversity supply. In this sense, a closer
investigation of
disturbances under climate change and its effects on forest
biodiversity
and profitability is encouraged.
A dynamic updating on possible climate realizations and on the
social value for forest biodiversity will help to reduce the range
of costs
and benefits, improving the efficiency of biodiversity-oriented
forest
management when new information becomes available.
Adaptive forest
management in combination with Bayesian updating provide a
natural
framework to dynamically update forest strategic plans in the
face of
new information and may be employed to tackle this issue (e.g.
Yousefpour et al., 2013). Such information may help to identify
not
only optimal conservation actions, but also identify the optimal
timing
for its implementation and avoid that thresholds related to
ecosystem
functioning are surpassed.

4.2. Second-best mechanism for biodiversity provision
The expected agents' surplus in the second-best mechanism
design
approach, as expected, was inferior to the first-best, where
biodiversity
is provided whenever the sum of benefits surpasses the costs of
im-
plementation. This occurs due to the ex-ante budget balance
require-
ment, ensuring that no external funding is needed for an
increase in
biodiversity supply. The social choice function required, in
general, that
valuations surpassed the costs by more than 18%. Yet, under
voluntary
participation, the second-best mechanism provides a powerful
frame-
work for the provision of forest biodiversity, when external
funding is
undesirable. Through this approach it is possible to derive not
only the
thresholds for biodiversity provision, but to attach probabilities
of
success, once the distribution of valuations is known.
Fig. 3. Sensitivity of the social choice function according to the
climate trajectory. BAI stands for the level of bird abundance
increase in the non-trivial provision
levels. The dotted vertical line shows the opportunity cost for
the corresponding increase in bird abundance.
Management 246 (2019) 706–716
712

The mechanism considered here refers to the case where the
gov-
ernment acts to maximize social welfare and does not have any
addi-
tional constraints apart from the budget balance. The
mechanism de-
signer, however, may have different goals. A large body of
literature is
dedicated to the supply of public goods when the designer has
the
objective of maximizing profits (e.g. Csapó and Müller, 2013),
where
the public good is only provided if the sum of virtual valuations
cover
the costs of implementation. Moreover, the government may
have ad-
ditional budget targets and minimum amounts of funds to be
raised that
would modify the mechanism. The formulation of the
mechanism de-
sign problem as an integer linear model allows to seamless
integrate
such additional requirements in the decision-making process,
e.g. by
adding extra constraints (Vohra, 2012). This may provide
valuable in-
formation when closed-form solutions for the models are not
readily
available.
The weight placed on the sum of virtual valuations decreased
when
the costs of biodiversity-oriented management approached the

upper
limit of the sum of valuations, approximating the first-best
mechanism.
This was accompanied, however, by a substantial decrease in
the
probability of implementation, since biodiversity-oriented
management
was only applied if the profile of valuations was composed by
the
highest types. Börgers (2015) shows this behavior of the
thresholds for
the second-best mechanism considering the supply of a public
good, in
which the threshold approaches the first-best criteria for costs
near
upper bound of valuations. In our analysis, when the cost was
close to
the maximum sum of valuations in the average cost and Had2.6
sce-
narios, there was an approximation to the first-best threshold.
In our study, we computed the optimal choice function for the
non-
trivial cases, considering the implementation cost in the average
case
and in each climate change scenario. One may consider the case
where
the designer wishes to guarantee the performance of the
mechanism in
the worst-case scenario. This would require that regardless of
the
climate realization, the feasibility of the mechanism is
preserved.
Hence, one may consider robustness criteria, e.g. by designing
the

mechanism based on the highest possible cost, so that in any
climate
realization the expected revenue is higher than the cost of im-
plementation (Had8.5 in our analysis). The topic of robust
mechanism
design is currently an area of active research. For example,
Bandi and
Bertsimas (2014) formulate an auction problem using robust
optimi-
zation, in which uncertainty sets are used instead of probability
dis-
tributions to characterize the agents’ valuations. Koçyiğit et al.
(2018)
developed an integer linear programming model for auction
design in
the case where the seller is ambiguity-averse. Such analyses
may im-
prove the success of mechanism under deep uncertain settings.
We investigated the application of a direct second-best
mechanism,
where agents announce their valuations and contribute towards
the
costs of biodiversity provision. There are a number of
alternative me-
chanisms dealing with the supply of public goods described in
the lit-
erature. For example, Bierbrauer and Sahm (2008) investigate
demo-
cratic mechanisms, where taxes are introduced to finance the
public
good provision and participants vote to express their
preferences for the
public good supply. Van Essen and Walker (2017) note that
theoretical
optimal mechanisms, did not always produce the desired

outcomes in
experimental studies and propose a simple market-like
mechanism that
always yield a feasible allocation. In their mechanism, the
contribution
of the participants is given by the per capita cost of provision
corrected
by the individual valuation compared to the average valuation.
Such
experimental evaluation of mechanisms for biodiversity
provision are
still scarce in the literature and deserve further investigation.
4.3. Optimal forest management
In order to provide biodiversity at a minimum cost, it was
necessary
to apply tailored management strategies to the set of forest
stands in our
Fig. 4. Optimal management portfolio for each climate scenario
under increasing levels of bird abundance at the end of the
century.
Management 246 (2019) 706–716
713
study area. A combination of management practices will be
required in
the future to balance the provision of products and ecosystem
services
in temperate forests. Gutsch et al. (2018) also show that forests

in
different regions show potential to fulfill optimally different
ecosystem
services in Germany under climate change, according to its
structure
and species composition. Naumov et al. (2018) report similar
patterns
studying forest landscapes in Northern Europe. In this context,
den
Herder et al. (2017) propose a framework for balancing the
provision of
forest goods and services, including economic, environmental
and so-
cial indicators using multi-criteria analysis. Hence, a sound
landscape
management will ask for the consideration of local forest
conditions on
the strategic planning of forest use, allowing to achieve the
desired
goals more efficiently, in terms the provision of multiple
ecosystem
goods and services.
Our solutions indicate that the conversion of spruce stands to
broadleaved forests was the most efficient practice to increase
biodi-
versity provision, measured through the abundance of bird
indicator
species. We highlight here that the indicator species had a
similar re-
sponse to the management actions considered. It is important to
con-
sider, however, that other taxa may have different requirements
re-
garding forest habitats. Particularly, saproxylic organisms
require old-

growth forest attributes, such as deadwood and habitat trees and
con-
nectivity among habitats at a finer scale (Müller et al., 2016;
Thomaes
et al., 2018). These aspects deserve to be further investigated
and both
spatial planning models and benefit assessments regarding these
taxa
are needed.
4.4. Limitations
We conducted our analysis based on the age class of each stand.
If
forest inventory data is available for each stand in the forest
area, we
may increase the accuracy of forest production forecasts and
tailor
management prescriptions to the specific stand structure.
Moreover, an
important aspect of forest dynamics under climate change refers
to the
occurrence of disturbances and how these interact with forest
pro-
ductivity and forest taxa (Hanewinkel et al., 2013; Greenville et
al.,
2018). A coupling of forest growth, disturbance and population
dy-
namics models are recommended for future studies.
We restricted our analysis to a limited set of management
options,
agents and types. Our framework, however, can be easily
extended to
encompass a larger set of management options and valuations to
pro-

vide more accurate estimates. These need to be balanced with
the
problem size generated, especially regarding the number of
types and
agents, as the possible combinations increase exponentially and
the
resulting matrix of the optimization problem has a large number
of non-
zeros.
We have included here relevant uncertainty aspects at the
strategic
planning level, with a focus on climate change and biodiversity
va-
luations. In this sense, we did not consider here all the relevant
sources
of uncertainty to the supply of biodiversity in public forests.
The un-
certainty in the biodiversity responses to management practices
may
significantly affect the operationalization of conservation
actions. This
uncertainty will have stronger influence with an increase in the
sensi-
tivity of the model and larger standard deviation of the
management-
related parameters. For example, in our analysis, the share of
conifers
showed the largest influence on the summed abundance,
compared to
other management actions. Thus, this uncertainty may affect the
total
cost of conservation (increasing the cost if the observed
response had
lower magnitude or decreasing the cost otherwise). Similarly,

un-
certainty in economic parameters (e.g. wood price and interest
rate)
may affect the opportunity costs of an increased biodiversity
supply and
deserve further investigation.
Here we considered independent valuations for forest
biodiversity.
The framework proposed by Csapó and Müller (2013) allows for
the
relaxation of this condition and is easily adaptable to our study.
The
authors accommodate dependent valuations by modifying the
virtual
valuation of agents, according to the joint probability
distribution of the
dependent random variables.
5. Conclusions
Biodiversity conservation remains a complex and important
forest
management problem. The coupling of ecological and economic
models
is key to find efficient conservation solutions and correct the
provision
of forest biodiversity, aiming to create resilient forest
landscapes.
Mechanism design offers a powerful framework to account for
the
strategic behavior of agents towards the public good provision
and
provides information on the conditions for a successful
implementation
of conservation programs. This will ultimately depend on the

re-
lationship between the social value and costs for providing
forest bio-
diversity, as well as the capacity of the government to levy
funds to
finance an increased biodiversity supply. The creation of s uch
me-
chanisms will be key to maintain the provision of multi-
functionality of
temperate forests in the face of climate change.
Acknowledgements
We acknowledge the funding of this research to the German
Research Foundation, ConFoBi project (number GRK 2123).
Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.jenvman.2019.05.089.
Appendix A. Forest optimization model.
A description of sets, data and variables used in the
optimization model is provided in Table A1 hereafter.
=MaxZ NPV (A1)
≤ ∑ ∑ ∑ ∑
+ ∑ ∑ ∑
− ∑ ∑ ∑ − ∑ ∑ ∑
− ∑

∈ ∈ ∈ ∈ +
∈ ∈ ∈ +
∈ ∈ ∈ ∈ ∈ ∈ +
∈ +
NPV vol x price
volfin x price
volini x price planting x
fixed
i S j M t PH k T ijtk ij tk ir
i S j M k T ijk ij PHk ir
i S j M k T ijk ij k i S j M t PH ijt ij ir
t PH t ir
1
(1 )
1
(1 )
1
1
(1 )
1

(1 )
t
PH
t
t (A2)
∑ ∑ ≥
∈ ∈
bio x Biodiversity
i S j M
ijPH ij
(A3)
Management 246 (2019) 706–716
714
∑ ∑ ∑ ≥ ∀ ∈
∈ ∈ ∈
vol x b t PH0.7
i S j M k T
ijtk ij
(A4)

∑ ∑ ∑ ≤ ∀ ∈
∈ ∈ ∈
vol x b t PH1.3
i S j M k T
ijtk ij
(A5)
∑ ∑ ∑ ∑ ∑ ∑≤
∈ ∈ ∈ ∈ ∈ ∈
volini x volfin x
i S j M k T
ijk ij
i S j M k T
ijk ij
(A6)
∑ ≤ ∀ ∈
∈
x area i S
j M
ij i
(A7)
∑ ≤
∈
x totarea0.5
i S

i1
(A8)
Table A1
Sets, variables and input data used in the forest optimization
model.
Sets Description
S Set of stands
M Set of management regimes
PH Set of periods
T Set of tree species
Variables
NPV Total NPV of forest management
xij Area of stand i to be management under regime j
b Wood production bound
Data
volijtk Volume of species k produced in period t in stand i
under management j
pricetk Price of species k in period t
ir Interest rate
volfinijk Final volume of species k in stand i under management
j
voliniijk Initial volume of species k in stand i under
management j
plantingijt Planting cost of stand i under management j in
period t
fixedt Fixed cost in period t
bioijt Bird indicator abundance in stand i under management j
in period t
Biodiversity Total bird indicator abundance bound
areai Area of stand i
totarea Total forest area

The objective function (Eq. (A1)) targets the maximization of
forest NPV. Constraint (Eq. (A2)) assigns to the variable NPV
the total NPV of the
forest investment along the planning horizon, computed trough
the discounted sum of thinning revenues, the difference in
standing stock value at the
beginning and at the end of the simulation period, the planting
costs and administering costs. Constraint (Eq. (A3)) requires
that the bird indicator
abundance at the end of the period is higher than the
boundBiodiversity. Constraints (Eq. (A4)) and (Eq. (A5)) are
wood flow constraints (Bettinger
et al., 2016) and enforce that the harvested volume in every
period respects a ± 30% variation compared to the endogenously
determined volume
boundb. The bound b was a free variable in the optimization
model, enabling to achieve the highest NPV while maintaining
the wood flow stability.
Constraint (Eq. (A6)) requires that the standing volume at the
end of the simulation period to be at least equal to the standing
volume at the
beginning of the simulation period, i.e. a sustainability criteria
regarding the forest utilization rate. Constraint (Eq. (A7))
guarantees that the
managed area of each stand is bounded by the stands’ total area.
Constraint (Eq. (A8)) requires that the conversion of Norway
spruce to European
beech stands do not extend over the 50% of the total forest area,
which is the forest cover of Norway spruce in the study region.
We constructed the optimization model in Lingo 17.0 optimizer
(https://www.lindo.com), solving it multiple times, increasing
the required level
of bird abundance (Biodiversity), and establishing the efficient
frontier between NPV and biodiversity. The cost for
biodiversity provision was

subsequently calculated based on the NPV loss to attain an
increased bird indicator abundance, compared to the maximum
attainable NPV. The
solution process to the optimization problem described by Eq.
(A1) to Eq. (A8) was obtained in 7 min and 30 s for each bird
abundance level enforce
by constraint Eq. (A3).
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716
https://www.tagesschau.de/inland/einwohnerzahl-deutschland-
107.html

https://sdw.ecb.europa.eu/browse.do?node=9691124
https://sdw.ecb.europa.eu/browse.do?node=9691124
http://refhub.elsevier.com/S0301-4797(19)30715-7/sr0005

http://refhub.elsevier.com/S0301-4797(19)30715-
7/sref51Climate change and the provision of biodiversity in
public temperate forests – A mechanism design approach for the
implementation of biodiversity conservation
policiesIntroductionMaterial and methodsDataForest simulation
and biodiversity supplyCosts of biodiversity provision and
optimal management under climate changeBiodiversity
benefitsA second-best mechanism for biodiversity

provisionResultsCosts of biodiversity provision under climate
changeSecond-best mechanism for biodiversity
provisionSensitivity analysis and management
solutionsDiscussionCosts of biodiversity provision under
climate changeSecond-best mechanism for biodiversity
provisionOptimal forest
managementLimitationsConclusionsAcknowledgementsSupplem
entary dataAppendix A. Forest optimization model.References
NR326 Mental Health Nursing
RUA: Scholarly Article Review Guidelines
NR326 RUA Scholarly Article Review Guideline 1
Purpose
The student will review, summarize, and critique a scholarly
article related to a mental health topic.
Course outcomes: This assignment enables the student to meet
the following course outcomes.
(CO 4) Utilize critical thinking skills in clinical decision-
making and implementation of the nursing process for
psychiatric/mental health clients. (PO 4)
(CO 5) Utilize available resources to meet self-identified goals
for personal, professional, and educational
development appropriate to the mental health setting. (PO 5)
(CO 7) Examine moral, ethical, legal, and professional
standards and principles as a basis for clinical decision-making.

(PO 6)
(CO 9) Utilize research findings as a basis for the development
of a group leadership experience. (PO 8)
Due date: Your faculty member will inform you when this
assignment is due. The Late Assignment Policy applies to
this assignment.
Total points possible: 100 points
Preparing the assignment
1) Follow these guidelines when completing this assignment.
Speak with your faculty member if you have questions.
a. Select a scholarly nursing or research article, published
within the last five years, related to mental health
nursing. The content of the article must relate to evidence-based
practice.
• You may need to evaluate several articles to find one that is
appropriate.
b. Ensure that no other member of your clinical group chooses
the same article, then submit your choice for
faculty approval.
c. The submitted assignment should be 2-3 pages in length,
excluding the title and reference pages.
2) Include the following sections (detailed criteria listed below
and in the Grading Rubric must match exactly).
a. Introduction (10 points/10%)
• Establishes purpose of the paper
• Captures attention of the reader
b. Article Summary (30 points/30%)
• Statistics to support significance of the topic to mental health

care
• Key points of the article
• Key evidence presented
• Examples of how the evidence can be incorporated into your
nursing practice
c. Article Critique (30 points/30%)
• Present strengths of the article
• Present weaknesses of the article
• Discuss if you would/would not recommend this article to a
colleague
d. Conclusion (15 points/15%)
• Provides analysis or synthesis of information within the body
of the text
• Supported by ides presented in the body of the paper
• Is clearly written
e. Article Selection and Approval (5 points/5%)
• Current (published in last 5 years)
• Relevant to mental health care
• Not used by another student within the clinical group
• Submitted and approved as directed by instructor
f. APA format and Writing Mechanics (10 points/10%)
2
NR326 RUA Scholarly Article Review Guideline 2

• Correct use of standard English grammar and sentence
structure
• No spelling or typographical errors
• Document includes title and reference pages
• Citations in the text and reference page
For writing assistance (APA, formatting, or grammar) visit the
APA Citation and Writing page in the online library.
Please note that your instructor may provide you with additional
assessments in any form to determine that you fully
understand the concepts learned in the review module.
NR326 RUA Scholarly Article Review Guideline 4 3
Grading Rubric Criteria are met when the student’s application
of knowledge demonstrates achievement of the outcomes for
this assignment.
Assignment Section and
Required Criteria
(Points possible/% of total points available)
Highest Level of
Performance

High Level of
Performance
Satisfactory Level
of Performance
Unsatisfactory
Level of
Performance
Section not
present in paper
Introduction
(10 points/10%)
10 points 8 points 0 points
Required criteria
1. Establishes purpose of the paper
2. Captures attention of the reader
Includes 2 requirements for section. Includes 1
requirement for
section.
No requirements for this section presented.
Article Summary
(30 points/30%)
30 points 25 points 24 points 11 points 0 points
Required criteria

1. Statistics to support significance of the topic to
mental health care
2. Key points of the article
3. Key evidence presented
4. Examples of how the evidence can be incorporated
into your nursing practice
Includes 4
requirements for
section.
Includes 3
requirements for
section.
Includes 2
requirements for
section.
Includes 1
requirement for
section.
No requirements for
this section
presented.
Article Critique
(30 points/30%)
30 points 25 points 11 points 0 points
Required criteria
1. Present strengths of the article

2. Present weaknesses of the article
3. Discuss if you would/would not recommend this
article to a colleague
requirements for
section.
Includes 1
requirement for
section.
No requirements for
this section
presented.
Conclusion
(15 points/15%)
15 points 11 points 6 points 0 points
1. Provides analysis or synthesis of information within
the body of the text
2. Supported by ides presented in the body of the paper
3. Is clearly written
requirements for
section.
Includes 1
requirement for
section.

No requirements for
this section
presented.
Article Selection and Approval
(5 points/5%)
1. Current (published in last 5 years) Includes 4 Includes 3
Includes 2 Includes 1 No requirements for
NR326 RUA Scholarly Article Review Guideline 4 4
2. Relevant to mental health care
3. Not used by another student within the clinical group
4. Submitted and approved as directed by instructor
requirements for
section.
requirements for
section.
requirements for
section.
requirement for
section.
this section

presented.
APA Format and Writing Mechanics
(10 points/10%)
1. Correct use of standard English grammar and
sentence structure
2. No spelling or typographical errors
3. Document includes title and reference pages
4. Citations in the text and reference page
Includes 4
requirements for
section.
Includes 3
requirements for
section.
Includes 2
requirements for
section.
Includes 1
requirement for
section.
No requirements for
this section
presented.
Total Points Possible = 100 points

PurposePreparing the assignmentGrading Rubric Criteria are
met when the student’s application of knowledge demonstrates
achievement of the outcomes for this assignment.
2 7 J u l y 2 0 1 7 | V O l 5 4 7 | N A T u R E | 4 4 1
lETTER
doi:10.1038/nature23285
Global forest loss disproportionately erodes
biodiversity in intact landscapes
Matthew G. Betts1,2*, Christopher Wolf1,2*, William J.
Ripple1,2, Ben Phalan1,3, Kimberley A. Millers4, Adam
Duarte5,
Stuart H. M. Butchart3,6 & Taal levi1,4
Global biodiversity loss is a critical environmental crisis, yet
the lack
of spatial data on biodiversity threats has hindered conservation
strategies1. Theory predicts that abrupt biodiversity declines are
most likely to occur when habitat availability is reduced to very
low
levels in the landscape (10–30%)2–4. Alternatively, recent
evidence
indicates that biodiversity is best conserved by minimizing
human
intrusion into intact and relatively unfragmented landscapes5.
Here we use recently available forest loss data6 to test
deforestation
effects on International Union for Conservation of Nature Red
List
categories of extinction risk for 19,432 vertebrate species
worldwide.

As expected, deforestation substantially increased the odds of a
species being listed as threatened, undergoing recent upgrading
to a higher threat category and exhibiting declining populations.
More importantly, we show that these risks were
disproportionately
high in relatively intact landscapes; even minimal deforestation
has had severe consequences for vertebrate biodiversity. We
found
little support for the alternative hypothesis that forest loss is
most
detrimental in already fragmented landscapes. Spatial analysis
revealed high-risk hot spots in Borneo, the central Amazon and
the
Congo Basin. In these regions, our model predicts that 121–219
species will become threatened under current rates of forest loss
over the next 30 years. Given that only 17.9% of these high-risk
areas are formally protected and only 8.9% have strict
protection,
new large-scale conservation efforts to protect intact forests7,8
are
necessary to slow deforestation rates and to avert a new wave of
global extinctions.
A critical question in global efforts to reduce biodiversity loss
is
how best to allocate scarce conservation resources. To what
extent
should conservation be focused on modified and fragmented
land-
scapes where threats are potentially greatest, versus landscapes
that
are largely intact9? Although it is expected that both approaches
have
value, in some human-influenced habitats, many species seem
sur-
prisingly resilient to habitat loss and fragmentation, and can

coexist
with humans in highly modified landscapes10,11, provided that
habitat
loss does not exceed critical thresholds2. Theory predicts that
abrupt
biodiversity declines are most likely to occur when habitat
availability is
reduced to very low levels in the landscape (10–30%)3,4,12.
Alternatively,
recent evidence indicates biodiversity is best conserved by
minimizing
human intrusion into intact and relatively unfragmented
landscapes,
which implies concentrating the impacts of anthropogenic
disturbance
elsewhere5,13. This is because initial intrusion may result in
rapid deg-
radation of intact landscapes, not only via the direct effects of
habitat
loss, but also the coinciding effects of overhunting, wildfires,
selective
logging, biological invasions and other stressors5. Such
evidence has
led to recent calls to increase the protection of substantial intact
areas
of the Earth’s terrestrial ecosystems14,15. Testing the extent to
which
these alternative hypotheses explain patterns of extinction risk
globally
can improve the effectiveness of conservation efforts and
inform the
formulation of policies, affecting the future of life on Earth.
Recent advances in remote sensing have enabled the
development

of a spatially explicit, high-resolution global dataset on rates of
forest
change6, which provide the capacity to quantify the effects of
contem-
porary global forest loss on biodiversity16. We quantified the
association
between global-scale forest loss and gain within the ranges of
19,432
species and their International Union for Conservation of
Nature
(IUCN) Red List category of extinction risk, recent genuine
changes
in extinction risk, and overall population trend direction. The
species
spanned three vertebrate classes, and included 4,396 (22.6%)
listed as
threatened (Vulnerable, Endangered, or Critically Endangered)
and
15,214 (78.3%) associated with forest habitats. Under the
‘habitat
threshold’ hypothesis, we expected the effects of recent forest
loss to
be most detrimental for species that have already lost a
substantial
proportion of forest within their ranges. Under the ‘initial
intrusion’
hypothesis, we expected species with relatively intact forest
within their
ranges to show the most severe effects of deforestation.
We obtained range maps for amphibians and mammals from the
IUCN Red List17 and those for birds from BirdLife
International and
NatureServe18. We classified species as ‘non-forest’, ‘forest-
optional’,
and ‘forest-exclusive’ based on the IUCN Red List habitat

classifica-
tion data17. Within each species’ range, we used fine-resolution
forest-
change data (2000–2014)6 to calculate the amount of recent
forest
cover, loss, and gain (Fig. 1). Given that many species were
assessed
for the Red List in the early period of our recent forest-loss data
(or even before this; Methods), it would be ideal to have
contemporary
forest loss data from before 2000. The most spatially contiguous
dataset
for 1990–200019 covered > 80% of the ranges for only 58.7% of
the
species in our analyses. However, locations of forest loss were
highly
spatiotemporally correlated at the scale of species’ ranges
between
1990–2000 and 2000–2014 (Methods, Intermediate-term forest
change;
Extended Data Fig. 7).
We also expected that historical deforestation over much longer
temporal scales could influence species vulnerability, a
phenomenon
known as ‘extinction debt’20,21. We calculated historical forest
loss as
the difference between the extents of area within species’
ranges that
historically supported forest cover and the area that remained
forested
in the year 20006 (Fig. 1). We also calculated the mean ‘human
foot-
print’ value22 within each species’ range, because forest loss
could be
confounded with other broad-scale anthropogenic pressures

(Fig. 1).
Using these data, we fit a spatial autologistic regression model
to test
whether forest loss within species’ ranges is associated with the
like-
lihood that a species: (i) is listed as threatened; (ii) has
qualified for
uplisting to a higher category of extinction risk in recent
decades (see
Methods); and (iii) has a declining population trend (as
classified by
IUCN Red List assessors).
1Forest Biodiversity Research Network, Department of Forest
Ecosystems and Society, Oregon State University, Corvallis,
Oregon 97331, USA. 2Global Trophic Cascades Program,
Department of
Forest Ecosystems and Society, Oregon State University,
Corvallis, Oregon 97331, USA. 3Department of Zoology,
University of Cambridge, Downing Street, Cambridge CB2 3EJ,
UK. 4Department of
Fisheries and Wildlife, Oregon State University, Corvallis,
Oregon 97331, USA. 5Oregon Cooperative Fish and Wildlife
Research Unit, Department of Fisheries and Wildlife, Oregon
State University,
Corvallis, Oregon 97331, USA. 6BirdLife International, David
Attenborough Building, Pembroke Street, Cambridge CB2 3QZ,
UK.
* These authors contributed equally to this work.
© 2017 Macmillan Publishers Limited, part of Springer Nature.
All rights reserved.
http://www.nature.com/doifinder/10.1038/nature23285

letterreSeArCH
4 4 2 | N A T u R E | V O l 5 4 7 | 2 7 J u l y 2 0 1 7
As expected, we found a strong association between rate of
recent
forest loss and each response variable. The odds of threatened
status,
declining population trends, and uplisting increased by 5.06%
(95%
confidence interval: 1.01–9.27), 11.34% (6.45–16.45), and
8.39%
(1.53–15.70), respectively, for each 1% increase in recent forest
loss for
forest-exclusive species. This is not surprising, given that
estimated or
inferred rates of habitat loss are used to inform IUCN Red List
assess-
ments under criterion A2, particularly for species lacking direct
data
on population trends17. Nevertheless, our results confirm that
previous
categorical estimates of habitat decline (based on a mixture of
inference,
qualitative and quantitative analysis) match with our global,
systematic
analysis of quantitative data on forest loss16.
More importantly, we found strong support for the initial
intrusion
hypothesis for both forest-optional and forest-exclusive species.
Species
were more likely to be threatened, exhibit declining popul ation
trends
and have been uplisted if their ranges contained intact
landscapes

(> 90% forest cover) with high rates of recent forest loss (Fig.
2). Evidence
for this lies in the strong positive statistical interaction between
forest
loss and cover (that is, forest loss × cover, Figs 2a, 3) on all
response
variables for both forest-exclusive and forest-optional species
(maximum
false discovery rate (FDR)-adjusted P = 0.025, minimum z =
2.51,
Fig. 2a, Supplementary Table 3). For example, at high
proportions of
initial forest cover (90%), the odds of a forest-exclusive species
being
uplisted were 15.78% (95% confidence interval: 6.99–25.30)
greater
for each 1% increase in deforestation. At average proportions of
forest
cover (57%), the equivalent increase in deforestation was much
smaller,
with the odds of a forest-exclusive species being uplisted
reduced to
3.45% (95% confidence interval: − 3.91 to 11.36) (Fig. 3).
These results
were generally similar across vertebrate classes, but amphibi ans
showed
the strongest and most consistent effects across response
variables
(Extended Data Fig. 2). Predictably, forest loss and its
interaction with
forest cover had little effect on non-forest species (Figs 2, 3).
Historical forest loss also exhibited a strong negative influence
on
vertebrate biodiversity (Fig. 2), which may be evidence of an
extinc-

tion debt in which some species are capable of persisting in
landscapes
long after initial forest loss has occurred, but subsequently
decline.
0
25
50
75
100
Cell
mean (%)
a Forest cover (2000)
0
20
40
60
Cell
mean (%)
b Recent forest loss (2000−2014)
0
10

20
30
40
Cell
mean (%)
c Recent forest gain (2000−2012)
0
25
50
75
Cell
mean (%)
d Human footprint
0
10
20
30
40
50

Cell
mean (%)
e Forest loss × cover
0
25
50
75
Cell
mean (%)
f Historical forest loss
Figure 1 | Spatial distribution of the six variables used to
predict
species’ IUCN Red List response variables. a, b–d, Forest cover
in the
year 2000 (a), forest loss between 2000–2014 (b), forest gain
(2000–2012)
(c), and human footprint (d). e, The interaction term ‘forest loss
× cover’
tested alternative hypotheses that forest loss exerts the greatest
negative
influence on biodiversity at low versus high initial levels of
forest cover.
High values of this variable (shown in e) correspond to regions
of both
high forest cover and loss. f, Historical forest loss represents
long-term

forest loss in years preceding 2000. Values plotted are averages
taken over
0.4° grid cells. The maps are derived from current forest change
maps6
(a–c, e, f), an intact forest landscapes map32 (f), biomes of the
world33
(f), and human footprint22 (d).
Bene�cial effect
on biodiversity
a
c
Detrimental effect
on biodiversity
b
d
Historical forest loss Human footprint
Forest loss × cover Forest gain
−0.5 0.0 0.5 −0.5 0.0 0.5
Forest-exclusive
Forest-optional
Non-forest
Forest-exclusive
Forest-optional

Non-forest
Standardized coef�cient
Response Threatened status Declining trend
Uplisted in
threatened status
FDR adjusted P value 0 < P ≤ 0.05 0.05 < P ≤ 0.1 P > 0.1
Figure 2 | Effects of four predictors on the status of 19,432
vertebrate
species worldwide. a, Positive ‘forest loss × cover’ terms
indicate that
the negative effects of forest loss are amplified in landscapes
with greater
initial forest cover. b–d, Forest gain tended to have a positive
effect on
forest optional and exclusive species (b), whereas historical
forest loss
(c) and human footprint (d) tended to have negative effects.
‘Threatened
status’ refers to IUCN Red List categories of ‘Vulnerable’,
‘Endangered’,
or ‘Critically Endangered’. ‘Uplisted in threatened status’
means that the
most recent genuine Red List category change for a speci es has
been in the
direction of higher endangerment. Forest loss and cover
variables were
included as main effects, but coefficient estimates are not
shown here as
they are not readily interpretable in the presence of the
interaction term.
Error bars represent 95% confidence intervals. Categories for P

values
are listed as ranges (that is, 0 < P ≤ 0.05, 0.05 < P ≤ 0.1, P >
0.1), and
sample sizes (also given in Supplementary Table 1) for non-
forest/forest-
optional/forest-exclusive are 4,218/3,430/4,218,
10,457/8,827/10,457,
4,757/4,073/4,757 for Threatened status, Declining trend, and
Uplisted in
threatened status, respectively.
© 2017 Macmillan Publishers Limited, part of Springer Nature.
All rights reserved.
letter reSeArCH
2 7 J u l y 2 0 1 7 | V O l 5 4 7 | N A T u R E | 4 4 3
Predictably, increased human footprint has had a generally
negative
influence on the status of vertebrates associated with forest and
non-
forest systems (Fig. 2). We also found recent forest gain
decreased the
likelihood of threatened status (forest-exclusive and forest-
optional
species) and declining population trend (forest-optional species;
Fig. 2).
However, amphibians primarily drove these relationships; bird
and
mammal biodiversity did not show statistically significant
responses
to forest gain (Extended Data Fig. 2), indicating that young

secondary
forest does not appear to be ameliorating biodiversity declines
for
these taxa8.
Overall, the global spatial autologistic regression model
performed
remarkably well (area under the receiver operating
characteristic
curve (AUC) = 0.78; Extended Data Fig. 1), even when we
conser-
vatively excluded entire regions one at a time (Africa,
Americas,
Asia, Oceania) and evaluated models on these independent data
(AUC = 0.74). Furthermore, results remained consistent when
we
statis tically accounted for phylogenetic dependencies, latitude,
and time
since each species was initially described Extended Data Fig. 3.
We also
applied alternative approaches to account for spatial
autocorrelation
and excluded species designated as threatened due to
characteristically
small and declining or fragmented ranges (that is, under IUCN
Red List
criterion B) (Extended Data Figs 3, 6). Results were also robust
to degree
of threat; Critically Endangered, Endangered and Vulnerable
species all
showed similar patterns in response to forest loss (Extended
Data Fig. 9).
Strong support for the initial intrusion hypothesis may be
surprising,
given existing theory3,23 and that a considerable number of

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