Biology subjects

Nater, C. R.

Publications and source records attributed to Nater, C. R..

4 recordsLinked to original sources

bioRxiv2021

Applying And Promoting Open Science In Ecology - Surveyed Drivers And Challenges

Open Science (OS) comprises a variety of practices and principles that are broadly intended to improve the quality and transparency of research, and the concept is gaining traction. Since OS has multiple facets and still lacks a unifying definition, it may be interpreted quite differently among practitioners. Moreover, successfully implementing OS broadly throughout science requires a better understanding of the conditions that facilitate or hinder OS engagement, and in particular, how practitioners learn OS in the first place. We addressed these issues by surveying OS practitioners that attended a workshop hosted by the Living Norway Ecological Data Network in 2020. The survey contained scaled-response and open-ended questions, allowing for a mixed-methods approach. Out of 128 registered participants we obtained survey responses from 60 individuals. Responses indicated usage and sharing of data and code, as well as open access publications, as the OS aspects most frequently engaged with. Men and those affiliated with academic institutions reported more frequent engagement with OS than women and those with other affiliations. When it came to learning OS practices, only a minority of respondents reported having encountered OS in their own formal education. Consistent with this, a majority of respondents viewed OS as less important in their teaching than in their research and supervision. Even so, many of the respondents suggestions for what would help or hinder individual OS engagement included more knowledge, guidelines, resource availability and social and structural support; indicating that formal instruction can facilitate individual OS engagement. We suggest that the time is ripe to incorporate OS in teaching and learning, as this can yield substantial benefits to OS practitioners, student learning, and ultimately, the objectives advanced by the OS movement.

scientific communication and education↗

bioRxiv2019

Estimating cause- and size- specific mortality hazard rates using mark-recapture-recovery data

O_LIEvidence-based management of natural populations under strong human influence frequently requires not only estimates of survival but also knowledge about how much mortality is due to anthropogenic versus natural causes. This is the case particularly when individuals vary in their vulnerability to different causes of mortality due to traits, life-history stages, or locations. C_LIO_LIHere, we estimated harvest and background (other cause) mortality of a landlocked migratory salmonid over half a century. In doing so, we quantified among-individual variation in vulnerability to cause-specific mortality resulting from differences in body size and spawning location relative to a hydropower dam. C_LIO_LIWe constructed a multistate mark-recapture model to estimate harvest and background mortality hazard rates as functions of a discrete state (spawning location) and an individual time-varying covariate (body size). We further accounted for among-year variation in mortality and migratory behavior and fit the model to a unique 50-year time-series of mark-recapture-recovery data on brown trout (Salmo trutta) in Norway. C_LIO_LIHarvest mortality was highest for intermediate-sized trout, and outweighed background mortality for most of the observed size range. Background mortality decreased with body size for trout spawning below the dam and increased for those spawning above. All vital rates varied substantially over time, but a trend was evident only in estimates of fishers reporting rate, which decreased from over 50% to less than 10% throughout the study period. C_LIO_LIWe highlight the importance of body size for cause-specific mortality and demonstrate how this can be estimated using a novel hazard rate parameterisation for mark-recapture models. Our approach allows estimating effects of individual traits and environment on cause-specific mortality without confounding, and provides an intuitive way to estimate temporal patterns within and correlation among different mortality sources. C_LI

ecology↗

bioRxiv2019

A 50-year series of mark-recapture data of large-sized brown trout (Salmo trutta) from Lake Mjosa, Norway

BackgroundLong-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs, and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural, and economical value. New informationThis paper describes two unique publicly available time series of individual-based data originating from a 51-year mark-recapture study of a land-locked population of large-sized migratory brown trout (Salmo trutta) in Norway: the Hunder trout. In the period 1966-2015, nearly 14,000 adult Hunder trout have been captured and individually marked during their spawning migration from Lake Mjosa to the river Gubrandsdalsl[a]gen. Almost a third of those individuals were later recaptured alive during a later spawning run and/or captured by fishermen and reported dead or alive. This has resulted in the first data series: a mark-recapture-recovery dataset spanning half a century and more than 18,000 capture records. The second data series consists of additional data on juvenile and adult growth and life-history schedules from half of the marked individuals, obtained by means of scale sample analysis. The two datasets offer a rare long-term perspective on individuals and population dynamics and provide unique opportunities to gain insights into questions surrounding management, conservation, and restoration of migratory salmonid populations and freshwater ecosystems.

ecology↗

bioRxiv2017

Study mortality with hazard rates, not probabilities

O_LIMortality is a key process in ecology and evolution, and much effort is spent on statistical and theoretical modelling of this process. Mortality hazard rates describe individuals instantaneous ability to survive at different times, whereas survival probabilities are defined for time-intervals of a given length. In this commentary, we argue that it is often more meaningful to model and interpret interval-specific time-averaged mortality hazard rates than survival probabilities, also when using discrete-time models.\nC_LIO_LIWe discuss three topics where the concept of hazard rates is essential for sound biological inference, but nevertheless often not used: (i) modelling of covariate effects on survival probabilities or mortality hazard rates, (ii) modelling of multiple sources of mortality and competing risks, and (iii) elasticity analyses of population growth rate to demographic parameters. To facilitate estimation of cause-specific mortality hazard rates, we provide amendments to R package marked.\nC_LIO_LIBy focusing on modelling mortality hazard rates, one avoids several inherent problems of comparing survival or mortality probabilities. In particular, interpretations about relative differences between mortality hazard rates (measurements on a ratio scale), or effects of relative changes in mortality hazard rates on population growth (elasticities), are more meaningful than similar interpretations involving survival (or mortality) probabilities or odds (measurements on an absolute scale).\nC_LIO_LIThe concept of hazard rates is essential for understanding ecological and evolutionary processes and we give an intuitive explanation for this, using several examples. We provide some practical guidelines and suggestions for further methods developments.\nC_LI

ecology↗