Person:
Barrientos Yuste, Rafael

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First Name
Rafael
Last Name
Barrientos Yuste
URI
https://hdl.handle.net/20.500.14352/81128
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Biológicas
Department
Biodiversidad, Ecología y Evolución
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2020 - 20212
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Start date: 2020 × Subject: 504.03 ×

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Now showing 1 - 2 of 2
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    The lost road: do transportation networks imperil wildlife population persistence?
    (Perspectives in Ecology and Conservation, 2021) Barrientos Yuste, Rafael; Ascensão, Fernando; D'Amico, Marcello; Grilo, Clara; Pereira, Henrique M.
    The global road network is rapidly growing associated with human economic development. This growth also entails a high toll for biodiversity, with several well-documented negative impacts on different species. However, there is still a great lack of knowledge about the effects of roads on the persistence of wildlife populations. Here, we aimed to summarize our current knowledge on this topic, based on systematic reviews. We found that only a small proportion of studies (8%) focused on the effects of roads on population persistence. Most of these studies were about large mammals and were performed in high-income countries. Furthermore, these works studied only 2% of those species identified by the IUCN Red List as threatened by roads. Overall, our results show that we are far from understanding how roads affect the long-term viability of wildlife populations inhabiting road-effect zones. Addressing this challenge will require modifying our conceptual perspective, from short-term to long-term studies, from single road sections to focusing the landscape scale, and strive to obtain empirical data to support sound analyses to assess how road impacts affect the survival of wildlife populations, namely with information required to perform approaches such as population viability analyses. We highlight some key studies from our reviews that have addressed this global conservation concern with population-oriented approaches.
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    Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
    (Nature Communications, 2020) Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan C.; Amano, Tatsuya; Anton, Álvaro; Baldigo, Barry P.; Barrientos Yuste, Rafael; Bicknell, Jack E.; Buhl, Deborah A.; Cebrian, Just; Ceia, Ricardo S.; Cibils-Martina, Ricardo; Clarke, Sarah; Claudet, Joachim; Craig, Michael D.; Davoult, Dominique; De Backer, Annelies; Donovan, Mary K.; Eddy, Tyler D.; França, Filipe M.; Gardner, Jonathan P. A.; Harris, Bradley P.; Huusko, Ari; Jones, Ian L.; Kelaher, Brendan P.; Kotiaho, Janne S.; López-Baucells, Adrià; Major, Heather L.; Mäki-Petäys, Aki; Martín, Beatriz; Martín De La Calle, Carlos Alfonso; Martin, Philip A.; Mateos-Molina, Daniel; McConnaughey, Robert A.; Meyer, Christoph F. J.; Mills, Kade; Montefalcone, Monica; Noreika, Norbertas; Palacín, Carlos; Pande, Anjali; Pitcher, C. Roland; Ponce, Carlos; Rinella, Matt; Rocha, Ricardo; Ruiz-Delgado, María C.; Schmitter-Soto, Juan J.; Shaffer, Jill A.; Sharma, Shailesh; Sher, Anna A.; Stagnol, Doriane; Stanley, Thomas R.; Stokesbury, Kevin D. E.; Torres, Aurora; Tully, Oliver; Vehanen, Teppo; Watts, Corinne; Zhao, Quingyuan; Sutherland, William J.
    Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.