Funded by an ERC Starting Grant to Nils Bunnefeld for 5 years from 1st September 2016
Find out more about the project and how to apply for the postdocs here
Conservation conflict in which people disagree about how to manage biodiversity and parties are perceived to assert their point of view to the detriment of others is an example of a Wicked Problem. Actually, to be honest there are loads of issues in conservation science that can be dropped into the complex, bubbling bucket of wicked problems. Are you dealing with lots of uncertainty? Finding the boundaries of the problem difficult to define? Lacking clear solutions that don’t cause problems elsewhere? Experiencing multiple feedback loops which interact with non-linear dynamics? Yep, you’ve got yourself a wicked problem there, pal.
In 2014, Game et al., described conservation challenges as operating under wicked problem conditions, providing a starter list as to where the common, conventional, “tame” approaches to conservation science were falling short (see also DeFries & Nagendra, 2017). Game et al., also helpfully, provided some wicked alternatives, pointing researchers towards the complexity-type thinking required.
So far, so abstract. But in 2016, the first Interdisciplinary Conservation Network workshop was hosted by ICCS at the University of Oxford, in conjunction with STICS and CBCS at the University of Queensland. This workshop for early career researchers (the second incarnation of ICN is happening later in 2018) included wicked problem thinking for conservation conflict as one of three topics discussed at the event. Eight researchers, each with knowledge of a specific conservation conflict, were joined by mentors to discuss how the conventional and wicked processes were being implemented in the real world. And if they weren’t currently being used, are wicked thinking approaches even appropriate or feasible for these conservation conflicts?
Our output from the workshop has now been published in Conservation Letters.
We found that for each conflict case study, wicked problem thinking was not applied even though over three-quarters were deemed both appropriate and feasible. For half of the case studies not one wicked approach had been tried.
Having used our case studies to assess the current state of play as regards wicked problem thinking for conservation conflict management, we moved onto thinking how could we fill out those wicked approaches a little more. We came up with five emerging themes worthy of further study.
1. Distributed decision-making
Wicked problem thinking aims to achieve a greater devolution of decision-making to suit the uniqueness and dynamism of different conflicts. This may not always be straight-forward if governance structures or existing policy will not allow transfer of powers.
2. Diverse opinions
Embracing diverse voices can form an important route to foster creativity. Research into the links between knowledge co-production, creativity and conflict are required to fully understand the potential value of diverse voices.
3. Pattern-based predictions
Recognising patterns in ecological dynamics, human behaviour and links between the two can reveal processes acting as conflict triggers, such as the alienation of certain groups. Pattern-recognition analyses can make use of widely available sources of data.
4. Trade-off based objectives
Objectives guided by trade-offs between the interests of different stakeholders are likely to produce fairer outcomes than those based on a single group’s interests.
5. Reporting of failures
No case-studies shared failures transparently even though failures are inevitable, due to the complexities of socio-ecological systems. Communicating these openly can optimise management. It may be possible to encourage open communication by requiring different parties to formally commit to sharing risk and viewing failures as transient features of a wicked problem. While it is tough to develop ‘safe-fail’ cultures in conservation, honest discussions between managers and stakeholders about failures – and the potential to learn from them – provide an important step forward.
A thread which runs through all five themes is one of admission of complexity. Conservation scientists cannot solve these problems with conventional methods and to tame them we must share power with and get help from others whilst admitting that we don’t always have the solutions and will sometimes fall short. Wicked problems are a nightmare to manage, but by thinking and working holistically, we can be optimistic about their taming.
As part of the ConFooBio project at the University of Stirling, my colleagues and I have released the new R package GMSE, with v0.2.2.7 currently available on CRAN and GitHub. The GMSE package generalises management strategy evaluation (MSE), an adaptive management framework that incorporates social-ecological system dynamics, the process of observing and monitoring the social-ecological system, and the assessment and decision-making processes of managers (Bunnefeld, Hoshino, and Milner-Gulland 2011). Our generalisation includes a game-theoretic component in which both managers and stakeholders dynamically update their decision-making to maximise their own utilities. The GMSE package thereby simulates all aspects of MSE and uses genetic algorithms (Hamblin 2013) to find adaptive solutions for manager and stakeholder decision-making.
In keeping with the MSE approach (Bunnefeld, Hoshino, and Milner-Gulland 2011), GMSE does not attempt to find optimal strategies or solutions for management. Instead, genetic algorithms within the GMSE package find manager policies and stakeholder actions that are adaptive, meaning that policies and actions that are found reflect heuristic strategies of managers and stakeholders that are adopted within a set of constraints and potentially changing circumstances. A wide range of parameter values can be specified by the software user to simulate management (for a full list, see the available arguments to the gmse() function in the reference manual, or run help(gmse) after loading the GMSE package). The gmse() function simulates all aspects of management, including the natural resources (population) model, the process by which resources are sampled or observed, the process by which managers make decisions about policy, and the process by which stakeholders decide how to act in response to policy (see Figure 1 below). The latter two processes (upper left and right boxes in Figure 1, respectively) each call the genetic algorithm to find adaptive solutions in each time step.
All sub-models (boxes in Figure 1) are individual-based (i.e., agent-based). The natural resources model simulates a single time step of resources (e.g., a managed population) that can undergo movement, reproduction, and death. The observation model simulates the monitoring of the resources (e.g., through capture-mark-recapture techniques). The manager model uses data from the observation model to set policy for stakeholders (e.g., how costly culling, scaring, or feeding resources should be), and the user model uses manager policy to determine actions to maximise their own utilities.
The GMSE package can be installed from CRAN or GitHub. The easiest way to install is through CRAN using the install.packages() function in R (Note, R version 3.3.3 or higher is required for GMSE).
To install this package from GitHub, the devtools library first needs to be installed.
Use install_github() to install using devtools.
A simple example illustrates the use of GMSE below.
Consider a population of managed resources that is hunted by four stakeholders (GMSE allows for any number of stakeholders; the default number is four to keep things simple). Assume that the population has a carrying capacity of res_death_K = 600 adults, and the manager wants to keep the population at manage_target = 400 individuals. The manager will use a capture-mark-recapture method of monitoring the population in each time step (observe_type = 1). Further assume that all other parameter values are set to default values (see the reference manual). We can run this scenario using the code below.
sim <- gmse(observe_type = 1, manage_target = 400, res_death_K = 600, plotting = FALSE);
##  'Initialising simulations ... ' ##  'Generation 33 of 100' ##  'Generation 62 of 100' ##  'Generation 95 of 100'
To avoid automatic plotting, I have set plotting = FALSE. The output to sim is a very large data structure that includes output from each sub-model (natural resource, observation, manager, and user) in each of 100 (default) time steps. The results can be plotted using the plot_gmse_results function.
plot_gmse_results(res = sim$resource, obs = sim$observation, land = sim$land, agents = sim$agents, paras = sim$paras, ACTION = sim$action, COST = sim$cost);
Figure 2 above shows the simulation dynamics over time for the starting parameter values set in sim. The upper left panel of the plot shows the positions of resources on the simulated landscape in the last time step (had we set plotting = TRUE, the movement of resources over time would be observable). The upper right panel is entirely blue, representing public land that is not owned by stakeholders (to simulate stakeholders that own land and attempt to maximise land values — e.g., crop yield –, use the option land_ownership = TRUE. Land ownership will then be represented by different landscape colours).
The middle left plot shows how both actual and estimated resource abundance changes over time. The solid black line illustrates the actual abundance of resources (i.e., the number of individuals) alive in each time step; the solid blue line illustrates how many resources are estimated based on the observation model and the simulated mark-recapture technique of the manager (blue shading illustrates 95 percent confidence intervals, but these are for display only and are not used in manager decision-making). The black dotted line identifies the management target (manage_target = 400), and the red dotted line illustrates the population carrying capacity (res_death_K = 600); note
that carrying capacity is enacted on adult mortality (but see also res_birth_K), so abundance can increase over carrying capacity given a sufficient number of juvenile and adult resources (where juveniles are defined as individuals born in the same time step as the current time step). The orange line shows the mean percent yield (right axis) of landscape production, which may be decreased by resources if resources consume crops on the landscape (see res_consume). The middle right panel would show the percent yield of each individual stakeholder’s given land_ownership = TRUE, with solid line colours reflecting yield from identically coloured plots in the upper right panel.
The lower left and right plots show manager policy and stakeholder actions over time, respectively. Under default simulation parameter values, only culling is available as a management policy and a stakeholder action, but (as indicated by the legend) other policy options can be set, including any combination of the following: scaring (moving resources), culling (killing resources), castration (removing resource’s ability to reproduce), feeding (increasing a resource’s survival probability), helping (increasing a resource’s reproduction), tend crop (increasing the yield from landscape cells), and kill crop (destroying yield of a landscape cell). Managers create policy by setting the cost of stakeholders performing available actions on resources (i.e., all actions except tending and killing crops), as constrained by the manger’s budget (manager_budget) and a minimum cost of performing actions (minimum_cost). In the above plot, there are time steps in which the manager has set culling costs to be high or low, based on the degree to which the estimated abundance of the population (blue line in middle left panel) is above or below the manager’s target. In response, stakeholders (in this case hunters) cull a lot when the manager sets culling to be cheap, but very little when culling costs are set to be high (‘costs’ in this case might be interpreted as quotas, or as strongly enforced prohibitions if the cost of culling is higher than the stakeholders budget, user_budget).
The gmse() function thereby provides a method of simulating multiple aspects of population management. This simple example is only one of many possible scenarios that can be simulated.
The code underlying GMSE has been flexibly developed for future expansion and new features. If there is something that would be useful to add that does not appear to be available by setting parameter values in gmse(), then chances are it can already be done by a few tweaks to the source code, or would require only a bit of additional coding. For example, the code already allows for future expansions to any number of resource types, allowing for multiple populations and structured populations — this is also true for agents.
Bunnefeld, Nils, Eriko Hoshino, and Eleanor J Milner-Gulland. 2011. “Management strategy evaluation: A powerful tool for conservation?” Trends in Ecology and Evolution 26 (9): 441–47. doi:10.1016/j.tree.2011.05.003.
Hamblin, Steven. 2013. “On the practical usage of genetic algorithms in ecology and evolution.” Methods in Ecology and Evolution 4 (2): 184–94. doi:10.1111/2041-210X.12000.
April’s Conservation Conversation was inspired by the Future of Conservation Project. This joint project hosted by UN WCMC and including researchers from universities Cambridge, Leeds & Edinburgh looks to explore the views of conservationists across a range of issues. The rearchers wonder if current conservation science paradigms are actually monopolised by a few loud voices and the project aims to find out how the values of conservation scientists en masse, align with those voices or otherwise.
The researchers’ previous work (Holmes, Sandbrook & Fisher, 2016) grouped conservation scientists around subjective positions. This then informed the building of a web-based questionnaire which scores respondents on two parameters: Conservation & Capitalism and People & Nature. The website then helpfully plots these two scores (along with those of a bunch of other recent respondents) and places the user in one of four positions: New Conservationist, Traditional Conservationist, Market Biocentrist and, Critical Social Scientist. Full methods & detailed descriptions of each of these positions are available on the project website.
Here at StiCS we asked our Conservation Conversationists to complete the questionnaire then come along with their lunch and discuss the aims, positions used and potential opportunities the resulting data could provide. We also had Tunnock’s Tea Cakes.
Here’s where our group ended up:
Rather than concentrating on where we fell individually and collectively on the plot, our discussion turned in two other directions: How we felt we answered the questions; and the relationship between values and interventions.
How we answered the questions
We discussed how it was often difficult to decide whether to answer more aspirationally (this is how it should be) or more realistically (this is how it should be, given the circumstances). The questions seemed designed to be context neutral, but many of us found it hard to decouple our general views from real world examples. Bold ethical questions were not apparent in the questionnaire: would we prefer to keep a person alive for 1 year or save a thousand hectares of rainforest? This may well be an unrealistic choice but such a question could encourage deeper thinking about how we value nature and people on such a linear scale.
Values & interventions
Can certain interventions be mapped onto the four positions of conservationist? A carbon trading scheme such as EU ETS may clearly fall onto the top half of the plot, using capitalist tools to achieve a conservation objective. However, rewilding which may at first glance seem like a Traditional Conservationist movement, could equally attract those from other positions (Table 1).
A rewilding enterprise financing facility, promoting the business case for investing in natural capital (European Commission, 2017)
Rewilding paid for by a business as part of a biodiversity offset scheme managed to maximise returns for biodiversity (Maron et al., 2016)
|Critical Social Scientist
An area for rewilding selected to maximise the ecosystem services it can provide (Cerqueira, et al, 2015)
An area for rewilding is selected to maximise returns for biodiversity
Devisive / Inclusive
One can imagine that categorising individuals into defined positions could have both divisive and inclusive outcomes. Divisive, from building potentially opposing camps into which people feel they should choose one position over another. Inclusive, from a clarification of values which can be used to gain a meaningful appreciation of alternate viewpoints.
The Future of Conservation survey was a fascinating spark for our group to explore why we do conservation and subsequently, what we do for conservation. Those behind the project have thus achieved the goal of stimulating discussion! We at StiCS look forward to seeing the results which come out of the project, particularly how demographic characteristics (age, gender, geography, education) impact conservationist values.
We suspect this won’t be the last time that the Future of Conservation Project is discussed at the Conservation Conversation!
We weren’t the first group to blog about this project! Check out how the Conservation Science group at RMIT in Melbourne got on in their discussion, here.
Biological and Environmental Sciences (BES) here at Stirling is full of international researchers and collaborations. Since the Brexit vote, there has been uncertainty over how we, as UK- and non-UK, EU- and non-EU nationals, will continue to work on EU-funded projects, begin new international collaborations, and remain as residents in the UK. Read More
The grand finale of the BBC’s Planet Earth II showcased the ingenious strategies that some animals use to thrive in urban environments. Though impressive, these species are in the minority. As the number of people living in cities around the world continues to rise, we should really be turning our attention to those animals that find city living too hard to handle. Read More
The human race is extremely resourceful, particularly when resources are limited. Inevitably, when poor rural communities are given access to a new asset they will find a number of uses for them. Anti-malarial bednets – the fine-mesh nets used to protect people from mosquito bites while they sleep – are a good case in point.
“I arrived confused about this topic and I will leave as confused as ever.” This was the parting comment from the only MP in the room and not the outcome we were hoping for! The event was a scientific briefing about neonicotinoid pesticides and pollinators, organised by the Soil Association in a classy venue in Westminster. Unfortunately, such confusion seems typical of the political response to an issue that has generated passionate controversy in many other sections of society. Read More
Funded by an ERC Starting Grant to Nils Bunnefeld for 5 years from 1st September 2016
Find out more about the project and how to apply for the postdocs here
Applications now being received for the 2016 Interdisciplinary Conservation Network (ICN) workshop!
Date of workshop: 26-28 June 2016
Application deadline: 31 March 2016
The Interdisciplinary Centre for Conservation Science (ICCS), Stirling Conservation Science (STI-CS) and the Centre for Biodiversity and Conservation Science (CBCS) are pleased to invite PhD students and early-career researchers in the field of conservation science to apply to participate in a three-day workshop to be held at the University of Oxford, UK.
The aim of this workshop is to provide early-career researchers with an opportunity to collaborate with other researchers from around the world, including leading figures in their field, and to learn key skills for the development of their careers.
More information about the workshop and how to apply is available here.
In a post-apocalyptic future, what might happen to life if humans left the scene? After all, humans are very likely to disappear long before the sun expands into a red giant and exterminates all living things from the Earth. Read More