To promote climate adaptation and mitigation, it is crucial to understand stakeholder perspectives and knowledge gaps on land use and climate changes. Stakeholders across 21 European islands were consulted on climate and land use change issues affecting ecosystem services. Climate change perceptions included temperature, precipitation, humidity, extremes, and wind. Land use change perceptions included deforestation, coastal degradation, habitat protection, renewable energy facilities, wetlands, and others. Additional concerns such as invasive species, water or energy scarcity, infrastructure problems, and austerity were also considered. Climate and land use change impact perceptions were analysed with machine learning to quantify their influence. The predominant climatic characteristic is temperature, and the predominant land use characteristic is deforestation. Water-related problems are top priorities for stakeholders. Energy-related problems, including energy deficiency and issues with wind and solar facilities, rank high as combined climate and land use risks. Stakeholders generally perceive climate change impacts on ecosystem services as negative, with natural habitat destruction and biodiversity loss identified as top issues. Land use change impacts are also negative but more complex, with more explanatory variables. Stakeholders share common perceptions on biodiversity impacts despite geographic disparity, but they differentiate between climate and land use impacts. Water, energy, and renewable energy issues pose serious concerns, requiring management measures.

Large language models that use retrieval augmented generation have the potential to unlock valuable knowledge for researchers, policymakers, and the public by making long and technical climate-related documents more accessible. While this approach can help alleviate factual hallucinations by relying on retrieved passages as additional context, its effectiveness depends on whether the model's output remains faithful to these passages. To address this, we explore the automatic assessment of faithfulness of different models in this setting. We then focus on ClimateGPT, a large language model specialised in climate science, to examine which factors in its instruction fine-tuning impact the model's faithfulness. By excluding unfaithful subsets of the model's training data, we develop ClimateGPT Faithful+, which achieves an improvement in faithfulness from 30% to 57% in supported atomic claims according to our automatic metric.

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.

Understanding and quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. The advancement of remote sensing technology and machine learning techniques has greatly facilitated this process. Yet, ground truth labels, such as biodiversity, are very difficult and expensive to measure. In addition, more easily obtainable proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem. In this paper, we demonstrate how land use proxy labels can be implemented with a soft, multi-label classifier to predict ecosystem services with complex heterogeneity.

Everybody who visits Kasanka National Park in Zambia during "bat season" agrees that the evening emergence of African straw-coloured fruit bats from their roost site is one of the wildlife wonders of the world. The bats (Eidolon helvum) arrive at Kasanka every year around October. The numbers swell rapidly until they peak in November. By January they are gone again. Once they recover from the shock of the breathtaking spectacle, everyone also converges on the same question – how many bats are there?

During the past decade, the RL has envisioned and built the ARIES (ARtificial Intelligence for Environment and Sustainability) platform, a technology that integrates network-available data and model components through semantics and machine reasoning. Its underlying open-source software (k.LAB) handles the full end-to-end process of integrating data and with multiple model integration types to predict complex change. It also supports selection of the most appropriate data and models using cloud technology and following an open data paradigm: the resulting insight remains open and available to society at large, and becomes a base for further computations, contributing to an ever-increasing knowledge base. For the first time, it is possible to consistently characterize and publish data and models for their integration in predictive models, building and field-testing technologies that have eluded researchers to date. We are looking for an individual who can support strategic activities related to integrated data science and collaborative, integrated modelling on the semantic web (semantic meta-modelling).

Experts can make crucial decisions about future biodiversity management by using artificial intelligence to learn from past environmental change, according to research at the University of Birmingham. A team, led by the University's School of Biosciences, has proposed a'time machine framework' that will help decision-makers effectively go back in time to observe the links between biodiversity, pollution events and environmental changes such as climate change as they occurred and examine the impacts they had on ecosystems. In a new paper, published in Trends in Ecology and Evolution, the team sets out how these insights can be used to forecast the future of ecosystem services such as climate change mitigation, food provisioning and clean water. Using this information, stakeholders can prioritise actions which will provide the greatest impact. Principal investigator, Dr Luisa Orsini, is an Associate Professor at the University of Birmingham and Fellow of The Alan Turing Institute.

Forests as natural reservoirs and filters can store, release, and purify water through their interactions with hydrological processes. For humans, a clean, stable, and predictable water supply is one of the most valuable ecosystem services provided by forests. Yet, globally, forests have undergone many changes driven by human activities (logging, reforestation, afforestation, agriculture, and urbanization) and natural disturbances (wildfires and insect infestations). From 2010 to 2015, tropical forests declined by 5.5 million ha year −1 , whereas temperate forests expanded by 2.2 million ha year−1 ([ 1 ][1]). The effects of both deforestation and forestation (reforestation and afforestation) on water supply have generated serious concerns and debates ([ 2 ][2], [ 3 ][3]), particularly after recent catastrophic fires in Australia and the western United States. However, hydrological consequences of forest changes are never simple, and future research and watershed management require a systematic approach that considers key contributing factors and a broad spectrum of response variables related to hydrological services. Zhang et al. showed the consistent tendency of deforestation to increase annual streamflow ([ 4 ][4]). More than 80% of deforested watersheds had annual streamflow increases ranging from 0.4 to 599.1%, mainly owing to reduced evapotranspiration after 1.7 to 100% forest cover loss ([ 4 ][4]). The large variations in the magnitude of changes depend on the scale, type, and severity of forest disturbance, climate, and watershed properties ([ 4 ][4], [ 5 ][5]). Larger-scale disturbance tends to cause greater increase in annual streamflow. Hydrological response to fire is similar to the response to logging, but the severity of the impact varies with climate, fuel accumulation, fire intensity, overstory tree mortality, and climate. Fires often cause hydrophobic soils, with reduced soil infiltration and acceleration of surface runoff and soil erosion. In a recent national assessment of the contiguous United States, forest fires had the greatest increase in annual streamflow in semiarid regions, followed by warm temperate and humid continental climate regions, with insignificant responses in the subtropical Southeast ([ 6 ][6]). The hydrological impact of insect infestation is likely less pronounced than those of other disturbances. Large-scale beetle outbreaks in the western United States and British Columbia, Canada, over recent decades were predicted to increase streamflow, with reduced evapotranspiration because of the death of infested trees ([ 5 ][5]). However, further evidence showed negligible impacts of beetle infestation on annual streamflow, owing to increased evapotranspiration of surviving trees and understory vegetation ([ 7 ][7]). Forestation can either reduce annual streamflow or increase it ([ 4 ][4], [ 8 ][8]). Zhang et al. ([ 4 ][4]) found that 60% of the forestation watersheds had annual streamflow reduced by 0.7 to 65.1% with 0.7 to 100% forest cover gain, whereas 30% of them (mostly small watersheds) had annual streamflow increased by 7 to 167.7% with 12 to 100% forest cover gain. Variations in annual streamflow response to forestation are even greater than those caused by deforestation, possibly owing to site conditions prior to forestation and tree species selected. Planting with a single fast-growing exotic species can have greater reduction in annual streamflow than with native species ([ 8 ][8]). Streamflow reductions after forestation are more common in semiarid and arid regions than in the humid subtropics and tropics ([ 4 ][4], [ 5 ][5]). Large-scale reforestation programs in the semiarid Loess Plateau in China caused substantial streamflow reductions that consequently approached water resource limits ([ 9 ][9]). Dry-season low flow is critical for water supply, particularly in the face of more severe droughts under climate change. Low-flow response to forest change can be positive, neutral, or negative ([ 5 ][5], [ 10 ][10]). The variable low-flow responses are mainly attributed to low-flow generation processes, forest characteristics (age, species, and regeneration), forestry practices (retention of riparian buffers, logging methods, and silviculture), changes in soil conditions, and choice of low-flow metrics (daily or 7-day minimum flow). Nevertheless, negative low-flow response is commonly expected if soil water storage and infiltration capacities are impaired by forest disturbances (soil compaction and erosion from logging, and soil water repellency following severe fires), and their recovery through reforestation could take much longer, because of the difficulty in restoring damaged soils ([ 10 ][10]). Generally, climate, watershed properties, forest characteristics, and their interactions are the major drivers for large variations in hydrological responses to forest change ([ 2 ][2], [ 4 ][4]). Zhou et al. assessed global land-cover effects on annual streamflow, based on a general theoretical framework ([ 11 ][11]). They found that hydrological sensitivity to land-cover change was determined by watershed properties (watershed size, slope, configuration, and soil), climate (precipitation or potential evaporation), and their interactions, where land cover and watershed properties jointly indicate water retention ability. Land cover or forest change can cause greater hydrological responses in drier watersheds or those with low water retention capacity. Similarly, McDonnell et al. ([ 12 ][12]) recommended studying watershed storages and water movements in the vertical zone that includes forest canopy, soil, fresh bedrock, and the bottom of groundwater ([ 13 ][13]), to further reveal the mechanisms for variable hydrological response to forest change. The feedback between forests and climate may also introduce complexity. Forests can supply atmospheric moisture through evapotranspiration and potentially increase precipitation (precipitation recycling) locally and in downwind directions. Therefore, forest change affects not only downstream river flow, but also precipitation and water supply downwind ([ 5 ][5]). Lawrence and Vandecar revealed variable rainfall responses to tropical deforestation across landscapes, depending on deforestation thresholds, such as reduced rainfall by large-scale deforestation and increased rainfall by small clearings ([ 14 ][14]). The effects of forest change on precipitation are likely related to topography, prevailing wind, and climate, because they affect moisture residence time, moisture transportation, and precipitation generation. The lack of observational evidence highlights the need for research on the feedback between climate and forest change at regional or continental scales. Time scale is important for understanding these variations. Hydrological effects of forest change can vary with time as forests regrow. Coble et al. reviewed long-term responses of low flows to logging in 25 small catchments in North America ([ 10 ][10]). They identified dynamic low-flow responses over three distinct time periods associated with the development of forest canopy leaf area index and corresponding evapotranspiration: consistent increase in the first 5 to 10 years, variable responses (increase, no change, or decline) during the next 10 to 20 years, and substantial decline in some (16 out of 25) watersheds multiple decades later. However, no decline in low flows was found in nine watersheds during the third period—likely dependent on similar factors previously identified for variations in low-flow response. The dynamic hydrological responses suggest that long-term studies are critical for fully capturing possible trends and variations in the effects of forest change on water supply ([ 5 ][5]). ![Figure][15] The complex influence of forests on water supply Forests in watersheds play a critical role in regulating downstream water supply and associated ecosystem services. GRAPHIC: N. DESAI/ SCIENCE The consistencies and large variations over space and time in streamflow responses to forest change call for a systematic perspective to elucidate both explanatory (factors affecting hydrological functions) and response (hydrological functions) variables in future studies (see the figure). In the systematic context, explanatory variables, including climate, forest, watershed properties, and their interactions and feedback across multiple spatial-temporal scales that jointly control streamflow responses, should all be assessed. To better clarify the response, a more complete spectrum of hydrological variables, including the magnitude, duration, timing, frequency, and variability of flows, which collectively determine river flow conditions, aquatic functions, and thus ecosystem services such as water supply, should be included in an assessment ([ 15 ][16]). Nonetheless, water-supply assessments often use limited hydrological variables (such as annual mean flows), which could underestimate total hydrological functions or even produce misleading conclusions resulting from different or contrasting responses of various flow variables. A systematic assessment of the effects of deforestation and forestation on water supply requires multidisciplinary collaborations. The classic paired watershed experiment (PWE: one watershed as a control and the others as the treatment) ([ 12 ][12]), mainly designed to assess streamflow response to forest change, has limitations to evaluate interactions and feedback among water, forests, climate, and watershed properties. Future PWEs should systematically consider more variables and processes (flow pathways, water storage and retention, and hydrological sensitivity) with various approaches (isotopic tracing, telemetering, and modeling). With long-term in situ monitoring and growing remote-sensing data, the forest-water nexus at larger spatial scales should be explored using advanced analytical tools (machine learning, and coupled climatic-ecohydrological modeling) within a systematic context. Future assessment should also focus on watershed management tools such as payments for ecosystem services, with the inclusion of more representative water variables to support synergies or trade-offs between hydrological and other ecosystem services provided by forests in a changing environment. 1. [↵][17]1. R. J. Keenan et al ., For. Ecol. Manage. 352, 9 (2015). [OpenUrl][18] 2. [↵][19]1. X. Wei et al ., Glob. Change Biol. 24, 786 (2018). [OpenUrl][20] 3. [↵][21]1. K. D. Holl, 2. P. H. S. Brancalion , Science 368, 580 (2020). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. M. Zhang et al ., J. Hydrol. (Amst.) 546, 44 (2017). [OpenUrl][25] 5. [↵][26]1. I. F. Creed, 2. M. van Noordwijk 1. I. F. Creed et al ., in Forest and Water on a Changing Planet: Vulnerability, Adaptation and Governance Opportunities. A Global Assessment Report, I. F. Creed, M. van Noordwijk, Eds. (International Union of Forest Research Organizations, 2018). 6. [↵][27]1. D. W. Hallema et al ., Nat. Commun. 9, 1307 (2018). [OpenUrl][28] 7. [↵][29]1. K. M. Slinski, 2. T. S. Hogue, 3. A. T. Porter, 4. J. E. McCray , Environ. Res. Lett. 11, 074010 (2016). [OpenUrl][30] 8. [↵][31]1. S. Filoso, 2. M. O. Bezerra, 3. K. C. B. Weiss, 4. M. A. Palmer , PLOS ONE 12, e0183210 (2017). [OpenUrl][32] 9. [↵][33]1. X. Feng et al ., Nat. Clim. Chang. 6, 1019 (2016). [OpenUrl][34] 10. [↵][35]1. A. A. Coble et al ., Sci. Total Environ. 730, 138926 (2020). [OpenUrl][36] 11. [↵][37]1. G. Zhou et al ., Nat. Commun. 6, 5918 (2015). [OpenUrl][38] 12. [↵][39]1. J. McDonnell et al ., Nat. Sustain. 1, 378 (2018). [OpenUrl][40] 13. [↵][41]1. G. Grant, 2. W. Dietrich , Water Resour. Res. 53, 2605 (2017). [OpenUrl][42] 14. [↵][43]1. D. Lawrence, 2. K. Vandecar , Nat. Clim. Chang. 5, 27 (2015). [OpenUrl][44] 15. [↵][45]1. N. L. Poff, 2. J. K. H. Zimmerman , Freshw. Biol. 55, 194 (2010). [OpenUrl][46] Acknowledgments: This paper was supported by China National Science Foundation (no. 31770759). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: pending:yes [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DFor.%2BEcol.%2BManage.%26rft.volume%253D352%26rft.spage%253D9%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DGlob.%2BChange%2BBiol.%26rft.volume%253D24%26rft.spage%253D786%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DHoll%26rft.auinit1%253DK.%2BD.%26rft.volume%253D368%26rft.issue%253D6491%26rft.spage%253D580%26rft.epage%253D581%26rft.atitle%253DTree%2Bplanting%2Bis%2Bnot%2Ba%2Bsimple%2Bsolution%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aba8232%26rft_id%253Dinfo%253Apmid%252F32381704%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjgvNjQ5MS81ODAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUzMy85OTAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DJ.%2BHydrol.%2B%2528Amst.%2529%26rft.volume%253D546%26rft.spage%253D44%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: #xref-ref-5-1 "View reference 5 in text" [27]: #xref-ref-6-1 "View reference 6 in text" [28]: {openurl}?query=rft.jtitle%253DNat.%2BCommun.%26rft.volume%253D9%26rft.spage%253D1307%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [29]: #xref-ref-7-1 "View reference 7 in text" [30]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D11%26rft.spage%253D074010%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: #xref-ref-8-1 "View reference 8 in text" [32]: {openurl}?query=rft.jtitle%253DPLOS%2BONE%26rft.volume%253D12%26rft.spage%253De0183210%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: #xref-ref-9-1 "View reference 9 in text" [34]: {openurl}?query=rft.jtitle%253DNat.%2BClim.%2BChang.%26rft.volume%253D6%26rft.spage%253D1019%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: #xref-ref-10-1 "View reference 10 in text" [36]: {openurl}?query=rft.jtitle%253DSci.%2BTotal%2BEnviron.%26rft.volume%253D730%26rft.spage%253D138926%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [37]: #xref-ref-11-1 "View reference 11 in text" [38]: {openurl}?query=rft.jtitle%253DNat.%2BCommun.%26rft.volume%253D6%26rft.spage%253D5918%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: #xref-ref-12-1 "View reference 12 in text" [40]: {openurl}?query=rft.jtitle%253DNat.%2BSustain.%26rft.volume%253D1%26rft.spage%253D378%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: #xref-ref-13-1 "View reference 13 in text" [42]: {openurl}?query=rft.jtitle%253DWater%2BResour.%2BRes.%26rft.volume%253D53%26rft.spage%253D2605%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-14-1 "View reference 14 in text" [44]: {openurl}?query=rft.jtitle%253DNat.%2BClim.%2BChang.%26rft.volume%253D5%26rft.spage%253D27%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [45]: #xref-ref-15-1 "View reference 15 in text" [46]: {openurl}?query=rft.jtitle%253DFreshw.%2BBiol.%26rft.volume%253D55%26rft.spage%253D194%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

ESF thus beckons the valuation of ecosystem services (VES) as a means to signalling nature's contribution to the (re)production of value (Barbier et al., 2009; Villa et al., 2009; Fisher et al., 2010; Gómez-Baggethun et al., 2016); for value is the central category of modern capitalist societies, and the valorisation of value -- i.e., economic growth sublimated into economic development -- their driving force (see, e.g., Mankiw (2016) and Holden et al. (2017)). VES is, in this sense, inscribed in an interpretive approach to modern capitalist praxis, not only invoking assumptions that are instrumentally validated in a retroactive manner, but also taking for granted precisely those historical and material conditions which VES is meant to interpret and, in doing so, reproduce. Overlooking the historical basis of ESF and VES has important practical consequences. When VES practitioners elicit value, a moment or specific field of the social praxis embodied in the valorisation of value is inaugurated, allowing value to mediate other social constructs built around the idea of nature. Since the patterns of actions that make up the capitalist social praxis are presupposed within this new ambit, value takes on a transhistorical quality that justifies its allencompassing and unreflective usage (see, e.g., Badura et al. (2016) and Gómez-Baggethun and Martín-López (2015)).

A cross-disciplinary group of researchers used AI as part of an analysis of photos posted online that recognizes an association between happiness, life satisfaction, and nature. Researchers from universities in Australia and Singapore say the analysis demonstrates the biophilia hypothesis that humans are naturally attracted to nature and people around the world have a preference for nature in their fun activities, vacations, and honeymoons. The analysis of more than 31,000 photos also found that people in nations with high life satisfaction scores like Costa Rica and Finland tend to take a higher proportion of photographs during fun activities like weddings or recreation. Nature also appears prominently in vacation and honeymoon photos. The frequency of nature in different activities varied widely across countries.