"Nature's contributions to people" and peoples' moral obligations to nature

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has become influential in biodiversity conservation. Its research is published widely and has been adopted by the United Nations and the Convention for Biological Diversity. This platform includes discussion about how values relate to biodiversity conservation. The IPBES emphasizes "relational values", connecting these with living a "good life," and "nature's contributions to people" (NCP); building upon ecosystem services (ES), which have dominated nature valuation for 15+ years. Although the IPBES acknowledges instrumental and intrinsic natural values, they purport that by adopting relational values, conservation will become more socially- and culturally-inclusive, moving beyond the "unhelpful dichotomy" between instrumental and intrinsic values. We wholeheartedly agree that conservation should become more inclusive - it should, in fact, morally include nonhuman nature. We argue that far from being half of an unhelpful dichotomy, intrinsic natural values are incontrovertible elements of any honest effort to sustain Earth's biodiversity. We find NCP to be mainly anthropocentric, and relational values to be largely instrumental. The "good life" they support is a good life for humans, and not for nonhuman beings or collectives. While passingly acknowledging intrinsic natural values, the current IPBES platform gives little attention to these, and to corresponding ecocentric worldviews. In this paper we demonstrate the important practical implications of operationalizing intrinsic values for conservation, such as ecological justice, i.e., "peoples' obligations to nature". We urge the IPBES platform, in their future values work, to become much more inclusive of intrinsic values and ecocentrism

“Nature's contributions to people” and peoples' moral obligations to nature

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has become influential in biodiversity conservation. Its research is published widely and has been adopted by the United Nations and the Convention for Biological Diversity. This platform includes discussion about how values relate to biodiversity conservation. The IPBES emphasizes “relational values”, connecting these with living a “good life,” and “nature's contributions to people” (NCP); building upon ecosystem services (ES), which have dominated nature valuation for 15+ years. Although the IPBES acknowledges instrumental and intrinsic natural values, they purport that by adopting relational values, conservation will become more socially- and culturally- inclusive, moving beyond the “unhelpful dichotomy” between instrumental and intrinsic values. We wholeheartedly agree that conservation should become more inclusive – it should, in fact, morally include nonhuman nature. We argue that far from being half of an unhelpful dichotomy, intrinsic natural values are incontrovertible elements of any honest effort to sustain Earth's biodiversity. We find NCP to be mainly anthropocentric, and relational values to be largely instrumental. The “good life” they support is a good life for humans, and not for nonhuman beings or collectives. While passingly acknowledging intrinsic natural values, the current IPBES platform gives little attention to these, and to corresponding ecocentric worldviews. In this paper we demonstrate the important practical implications of operationalizing intrinsic values for conservation, such as ecological justice, i.e., “peoples' obligations to nature”. We urge the IPBES platform, in their future values work, to become much more inclusive of intrinsic values and ecocentrism

Protecting Half the Planet and Transforming Human Systems Are Complementary Goals

The unfolding crises of mass extinction and climate change call for urgent action in response. To limit biodiversity losses and avert the worst effects of climate disruption, we must greatly expand nature protection while simultaneously downsizing and transforming human systems. The conservation initiative Nature Needs Half (or Half Earth), calling for the conservation of half the Earth's land and seas, is commensurate with the enormous challenges we face. Critics have objected to this initiative as harboring hardship for people near protected areas and for failing to confront the growth economy as the main engine of global ecological destruction. In response to the first criticism, we affirm that conservation policies must be designed and implemented in collaboration with Indigenous and local communities. In response to the second criticism, we argue that protecting half the Earth needs to be complemented by downscaling and reforming economic life, humanely and gradually reducing the global population, and changing food production and consumption. By protecting nature generously, and simultaneously contracting and transforming the human enterprise, we can create the conditions for achieving justice and well-being for both people and other species. If we fail to do so, we instead accept a chaotic and impoverished world that will be dangerous for us all

Effect of the Pre-erythrocytic Candidate Malaria Vaccine RTS,S/AS01E on Blood Stage Immunity in Young Children

(See the article by Greenhouse et al, on pages 19-26.

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense