The Social Implications of Conservation – with Prof Dan Brockington

In recent decades, conservation science has begun to pay greater attention to local people’s rights, livelihoods, and well-being. Yet the growing availability of data and advanced technologies raises new justice concerns for conservation science. This episode explores the justice dimensions of conservation in a data-driven world.

This episode features Professor Dan Brockington from the Universitat Autònoma de Barcelona, interviewed by Leverhulme Early Career Fellow Thuy Duong Khuu.

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Intro music Anna Banana by Eaters

Transcript

Speaker 1 [00:00:02] Welcome to the Global Development Institute podcast. Based at the University of Manchester, we're Europe's largest research and teaching institute addressing poverty and inequality. Each episode, we'll bring you the latest thinking, insights, and debate in development study. 

Speaker 2 [00:00:22] Hello, I'm Duong Khuu, a Leverhiem Early Career Fellow, working with the Sustainable Forest Transition Project at the University of Manchester. Today we will look at a topic that is critically important to the future of all life on Earth, conservation. The past decades, conservation science has gradually shifted from a focus on preserving irreplaceable places for biodiversity towards more inclusive strategies that begin to account for the livelihoods and well-being of local people, thanks to decades of research by anthropologists, political ecologists and conservation social scientists, which has shed light on the often unintended social consequences of conservation initiatives around the world. As conservation science continues to evolve, it is increasingly shaped by the growing availability of large-scale data and advanced technology, such as remote sensing, machine learning, and predictive modelling. These tools bring enormous potential for global prioritisation and monitoring, but they also raise significant questions. While much attention has been given to what these technologies can do, less has been paid to the implications of how they are used, who controls them, and whose knowledge they recognise. In this episode, we explore the justice dimensions of conservation data, examining how data shape power relations represent people and ecosystem, and how we, as scientists, can navigate these challenges to use data more responsibly and ethically. This is why we have a great pleasure of speaking with Professor Dan Brockington today. 

Speaker 3 [00:02:13] Hi Dan, thanks for being here today with us. Could you please introduce yourself to the audience and what motivated you to do research on data justice and conservation? 

Speaker 4 [00:02:28] Thank you very much, indeed, for this invite, Duong. It's great to be talking with you again. So I am Dan Brockington. I am an ICREA research professor based at ICTU-UAB in Barcelona, at the Universitat d'Autonomie de Barcelona. And yes, I do work on data justice and conservation. I'm a conservation social scientist. And I've been doing this since my PhD really, which was in the mid 1990s. What motivated it, me? It's... The fact that I think conservation is really important. We need more of it. And conservation needs to be fairer and more just. Because currently, there are quite powerful forms of conservation, which have been predicated upon various forms of injustice. And That is not a good service to the conservation cause. And it doesn't reflect at all the values of many, many conservationists. And so I'm interested to understand better what those injustices are, how they arise, and how they might be addressed. 

Speaker 3 [00:03:47] Thank you for the introduction, which makes me understand more about yourself and your interesting work so far. In a data-driven world, the concept of data justice is increasingly important, especially in conservation. So to start, could you please explain what is counted as data and what data justice means? Can we consider data justice a new? Critical framework for understanding the social implications of conservation initiatives around the world. 

Speaker 4 [00:04:23] Hmm, now these are really important questions. The short answer to the second question, is it a new critical framework, is yes, but. Because there's qualifications. But let's go back first of all to consider the question of what's counted as data and what data justice means. And this is important because one of the battles in conservation affairs is whose knowledge counts, whose view and idea of the world counts. And traditionally in conservation, the discipline of conservation biology arose amongst natural scientists trained in the West, trained very well. To understand ecology, biology, zoology, and the... Incredible glorious complexities of the world around us. And there are other ways of knowing the world, which people living in particular places and landscapes, many of these are indigenous peoples, some of these rural communities have been in place for decades, if not centuries, and their knowledges can be obscured or not privileged or not thought to be important. And in some conservation decision making. So. What counts as data? Well, probably not enough of the world views that need to be counted as providing important information about the world. And this gets all the more important when we turn to questions of data justice, because the challenges of data justice have tended to arise in a world where there are more and more big data sets which present more and more information in greater detail about greater numbers of people or places. These data sets are complex. The models using them are also complex. Data justice is attentive to the bias that can creep into these data sets and models through problematic assumptions, through distortions in the way the data are collected, which can be so easily missed because these data are so powerful. And so that data justice has tended to focus upon the large-scale quantitative data, which again requires an attention to forms of data which don't privilege and don't fit easily necessarily with our other epistemic worldviews and other ontologies. So, your second question, can we consider data just as a new critical framework? Well, yes, because new data sets are being employed, new geospatial data, new social media data, new social economic data are being deployed to implement conservation and can be used to understand their social implications. And the but is that It still tends to require many of the questions that have been asked for a long time by conservation social scientists about the justice implications of conservation affairs. 

Speaker 3 [00:07:57] Let's get a great foundation of data justice, which makes me think that is a really important field of study. So from your point of view, how does the rise of large-scale prioritisation studies, those using remote sensing, Earth observation and machine learnings fit within the broader political and institutional trajectories of conservation science and international development. 

Speaker 4 [00:08:27] I think that the key part of your question here is this qualification large scale. Because for the large scale studies, this is plainly part of a trajectory which privileges global knowledge, which privileges complex models, very sophisticated science and quite prestigious science. It gets published in the best academic journals. It's driven by the most powerful and wealthiest conservation organisations. These sorts of prioritisation studies had really began to take off in the late 1980s, I think the hotspots was one of the first to become popular and widely known. And there have been a plethora since then, so much so that the late Georgina Mace and others wrote into Nature to complain, saying that we need to. Work together to identify key conservation priorities. They were prompted by a new iteration of hotspots and they were saying, well, this is focusing on one particular conservation organisation, conservation and international priorities. And there are other ways of looking at the world, working at what matters for conservation. It's a trajectory which has focused on the larger organisations, Western trained generally Western based scientists and which is not been about the more community-focused and community-based approaches to conservation. There can be good reason for taking a global approach and large scale planning requires that if you're interested in endemics, then by definition, they have you have to have a global approach because an endemic is defined as something which is found in only one place in the entire world. And endemics matter a huge amount to conservation. And all of this means that a smaller scale prioritisation could well be possible in ways which are much more meaningful to local groups and the communities in whom conservation could be based. 

Speaker 3 [00:10:53] Yeah, I'm using privatisation in my research as well. So this makes me think about how to make it more accountable for local communities. Thanks for that. 

Speaker 4 [00:11:08] Well, that's fascinating, because there are groups who say, well, not accountable. And actually, it's not really intended to be. This is a global conservation community, which is trying out models, trying out possibilities, and trying to see at the larger scale, how one could reimagine configurations of the economy, society, and politics, which was all well and good. But If it is to have any practical implications, it does need to be grounded somewhere and somehow and very quickly and depart from the global scale. 

Speaker 3 [00:11:44] Okay, thanks. So now the shift toward data-driven and computational methods is transforming the way that landscapes are observed, assessed, and managed. So in your opinion, how does this data infrastructure shift the power dynamics among global institutions, national governments, and local communities. And what implication does it have for decision making and adaptability in conservation practise? 

Speaker 4 [00:12:19] So I think that's a leading question because I think it encourages us to say that this conflict, I think infrastructure and methods and data and all their difficulties do indeed shift the power away from local communities and towards the global institutions and national conservation departments, with the implication that This will mean that power shifts upwards and accountability moves upwards. Let me challenge therefore the leading question in a couple of ways. Firstly, is it really a shift? Because that's where the power already lies. It may strengthen the hand somewhat. It's fascinating looking at the various attempts to devolve power to local communities over forest wildlife. And habitat and the way in which that is so often obstructed and delayed by powerful interests. So it may just be more of the same in that respect. The other thing is that we shouldn't underestimate at all the sophistication and complication of many of these rural communities and their approach to this sort of. That the affordances of these sorts of data. And you'll see this in Naomi Milder's work on drones. In the communities that she works in, drones are welcome. And Naomi's fascinating on this because she doesn't like drones. Drones are a piece of military tech which helps surveillance. And she has to recognise that they are being, and this is something which rural communities do so often. Turn and work things for their own purposes, for their own advantage. 

Speaker 3 [00:14:21] Okay, that's an important point about power dynamics. So the next question. Many scholars have argued that less-scale data practises in conservation often reproduce older colonial and or technocratic logics like managing landscape distantly. Privileging external expertise and abstracting ecosystem from the live reality of local communities. So how does this dynamic resonate with broader issues of at least a bit justice in conservation, especially when it comes to whose knowledge is seen as legitimate and who is ignored. 

Speaker 4 [00:15:13] I find the ideas of epistemic justice and injustice absolutely fascinating. I didn't read the book when it first came out and I found it a brilliant text and I'm not at all surprised that it has resonated so broadly with so many people and come to have such influence seems to be fully deserved. But let's remember that there are two aspects to epistemic injustice that Miranda Fricker talked about. She spent most of her time talking about testimonial injustices, which is the injustice and individual experiences when some bias or prejudice on behalf of their listeners prevents them from being credited as believable, as being correct. Well, not necessarily correct, but certainly Believable, credible. And she focuses on the case in To Kill a Mockingbird, where the witness in the court case is not believed because they're black and the audience is white. Now There are obvious parallels in many aspects of data justice and in conservation itself, when we think about whose knowledge counts and whose knowledge matters. And she also talks about hermeneutic injustices, and these refer to the ability of a group of people to express themselves and to be heard and believed. This may arise either because they themselves do not have the language, the concepts to express what is happening to them, or because their listeners collectively cannot appreciate the perspective of this group of people and what they're saying about their lives. And here she uses the extraordinary case of sexual harassment. Before that term was used, what did women set, what language could women use to describe this strange, oppressive and disgusting behaviour that they experienced from some of their male colleagues? And it was only when the term was invented that it provided the language to talk about this form of abuse. And again, there are clear parallels where conservation policies will either not recognise or fail to privilege and treat it treat appropriately, and particular forms of ways of knowing living and being in the world, which discriminate against or which marginalise whole groups of people. So In this sense, where large scale data practises do then privilege ways of knowing which can fail to see other people's life pathways and life ways, then this can indeed introduce forms of hermeneutic injustice. Or rather, this can be a form of hermeneutic injustice, which are surfacing again. My challenge, of course, is that this is not necessarily a new phenomenon. It's just something which has been going on all the time. Hence, yes, it is. It could well be reproducing other colonial technocratic logics. And once again, because this is also as a leading question, John, let me again push back. What is really interesting about these cases is not so much the way in which the old prejudices and marginalizations are reproduced, but the way that reproduction is challenged by the groups who are experiencing this injustice. 

Speaker 3 [00:19:35] Okay, that's interesting. Good to know that broadens my understanding a lot. Thanks. So now, given all we have discussed so far, I have a final question. What steps can we as scientists take to ensure that conservation data practises become more equitable, inclusive and accountable to the people and ecosystem they aim to provide for? 

Speaker 4 [00:20:08] Well, as phrased, we can't. We can't ensure this at all. We are scientists, not practitioners. We don't make the policy. We advise. We lobby where possible. But it's quite good for scientists, Um, not necessarily to have all that power. They don't necessarily know how to wield it. That's not what we're trained for. It's not our roles. What we can do to try to empower the practises that we find most just, I think, is asking the right questions. And I think... Putting ourselves beyond our comfort zones and having those uncomfortable conversations, which challenge the way and the things that we think should be the case, which challenged our norms. This is what data conservation data justice does. It takes standard data sets, which are used all over all over the place, and with great frequency and without sufficient interrogation to say, well, Who is left out here? Who is missing? What are the assumptions and biases? How might marginalisation be reproduced? And we can do it in several different ways. We can take the data in their own terms, and we can say, well, we know these data sets are flawed. We know these geospatial data have problems. But nonetheless, let's use these data to interrogate each other. In order to see what patterns and processes become clearer when we juxtapose these data. But this is what we did in the paper with JT Erba where we looked at how many people might be found in forests that could be restored, to respond to the idea that forests should be restored to their natural condition, which meant. That they should tend to have people in them. Now, we think that a natural forest can have people. It was important to understand how many people might be there. We know the map of forest that could be restored is problematic. We know the maps of population are problematic, but nonetheless, you can learn a great deal by putting them together. That's precisely what Chris Sandbrook and Javid Fajardo were doing in their current SNAP project, looking at the social implications of prioritisation exercises. You take the geospatial data on their own terms. Second approach is to say, Well. Hmm These geospatial data, when we look into their detail, what do we find about their origins in construction? This is where you begin to look into the ways in which, for example, population data are full of inaccuracies, especially in rural areas, if it's derived from an algorithm which is basically based on construction. And if the buildings at the. The buildings that can be seen tend to be square with metal roofs or stone roofs. This means that people who live in circular buildings with batched roofs will not be counted and it's obvious where that discriminant bias will form. It means that you look, for example, into the light well. Widely used and generally accepted data such as ecoregion boundaries begin to ask, well, these ecoregens are profoundly wrong in a number of cases. A considerable proportion of the world's lakes, I think amounting to an area the size of Iraq, disappear under ecoregins. They appear as land. The volcano where my wife was born and brought up, which is three and a half thousand metres high and incredibly steep, is rendered as a halophytic grassland. It's a flooded plain in the Ica Regions. And moreover, the source map which the Icar Regions is derived from in the case of Tanganyika, sorry, the case in Tanzania, is itself derived from a vegetation map that was in 1949 for Tanganyika and then simply digitised. And then marginal adjustments were made to it, which means that the assumptions and ideas about what vegetation should be and could look like in Tanganyika in the 1940s are also, sorry, could well be, we are still looking into this, could well the assumptions or models which are used for eco-regions in the 2020s. There are, And the extraordinary thing is that if you look at the map of Tanganyika that was drawn in 1949 and the ecoregion boundaries in so many places, they are exactly the same. When we look, so the point is that when we look forensically into the detail and construction of particular geospatial data, then you can begin to again, to understand how particular assumptions or biases or views of the world get sustained with those data. 

Speaker 3 [00:25:54] Wow. Okay. And, you know, I used to use the ecoregion data during my employment at the conservation NGO back in Vietnam, like long time ago. I only care about the availability of data, but I didn't look into so much into detail, like how those data are produced and achieved. So... Now we are coming to the end of our sessions. Is there anything you'd like to share with our audience, like a final thought, a question to leave us with, or something that we haven't discussed? And how can the audience follow your current and future work, or get in touch if they like to learn more. 

Speaker 4 [00:26:42] Look, I'm on email. So that's the best way of getting in touch. And you can follow the Conservation Data Justice Research Project, CONJUST, C-O-N-D-J-U-S-T, C- O-N D-J U-S T, by our website, which will come up in a Google search and to find out about updates and our blogs of how we're getting on. And the thing I think that the final thought is that, as you've just alluded to, understanding in all its depths and richness and complexity and idiosyncrasy, the origins and provenance of our data is a bit of a luxury. Many practitioners, many scientists who aren't given the time to explore these data as we are and therefore it's beholden on those who do explore these data, and by that I mean myself and my colleagues, effectively to communicate the issues that arise and that we respond. And I really am not interested in the sort of science that says, huh, that's a mistake here. You didn't notice that, did you? It's a map. A map is a generalisation. A map a simplification. Therefore it will miss some of the detail and spotting missed detail is not particularly interesting science. The bigger challenge is to show which of these details matter, why they matter and how that knowledge can be made easily accessible. To people who need to be able to see it as they are downloading or accessing the map. The warnings, the caveats need to be much more prominent and made available in a user-friendly way. And that's a challenge to us. 

Speaker 3 [00:28:34] Okay. So thank you so much Dan for joining us today. 

Speaker 4 [00:28:41] Thank you very much. It's been a pleasure to talk to you. 

Speaker 3 [00:28:45] I really look forward to talking to you soon in the near future. 

Speaker 4 [00:28:50] And I too, be well. 

Speaker 3 [00:28:53] Bye bye! Goodbye!