Rewriting results sections in the language of evidence Muff, Stefanie; Nilsen, Erlend B.; O’Hara, Robert B. ...
Trends in ecology & evolution,
March 2022, 2022-03-00, 20220301, Letnik:
37, Številka:
3
Journal Article
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Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is ...likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the ‘good practices’ that exist in other fields.
It has been known for decades that there are severe problems associated with null-hypothesis significance testing (NHST) based on arbitrary P-value thresholds (e.g., P = 0.05).A small literature review indicates that much of the current research in ecology and evolution is still disregarding the warnings and frequently relies on binary decisions based on P-values to report statistical significance.While the P-value itself is a sound mathematical concept that does not have to be banned when used correctly, we should stop using the term ‘statistical significance’ and replace it with a gradual notion of evidence.Language matters and ‘evidence’ is an intuitive concept that honestly reflects what the data really tell us.To facilitate rewriting scientific results, we offer generic examples of how to translate (binary) significance language into a gradual language of evidence.
1 In predator–prey theory, habitat heterogeneity can affect the relationship between kill rates and prey or predator density through its effect on the predator's ability to search for, encounter, ...kill and consume its prey. Many studies of predator–prey interactions include the effect of spatial heterogeneity, but these are mostly based on species with restricted mobility or conducted in experimental settings. 2 Here, we aim to identify the patterns through which spatial heterogeneity affects predator–prey dynamics and to review the literature on the effect of spatial heterogeneity on predator–prey interactions in terrestrial mammalian systems, i.e. in freely moving species with high mobility, in non‐experimental settings. We also review current methodologies that allow the study of the predation process within a spatial context. 3 When the functional response includes the effect of spatial heterogeneity, it usually takes the form of predator‐dependent or ratio‐dependent models and has wide applicability. 4 The analysis of the predation process through its different stages may further contribute towards identifying the spatial scale of interest and the specific spatial mechanism affecting predator–prey interactions. 5 Analyzing the predation process based on the functional response theory, but separating the stages of predation and applying a multiscale approach, is likely to increase our insight into how spatial heterogeneity affects predator–prey dynamics. This may increase our ability to forecast the consequences of landscape transformations on predator–prey dynamics.
We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously ...been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.
Selective harvesting regimes are often implemented because age and sex classes contribute differently to population dynamics and hunters show preferences associated with body size and trophy value. ...We reviewed the literature on how such cropping regimes affect the demography of the remaining population (here termed demographic side effects ). First, we examined the implications of removing a large proportion of a specific age or sex class. Such harvesting strategies often bias the population sex ratio toward females and reduce the mean age of males, which may consequently delay birth dates, reduce birth synchrony, delay body mass development, and alter offspring sex ratios. Second, we reviewed the side effects associated with the selective removal of relatively few specific individuals, often large trophy males. Such selective harvesting can destabilize social structures and the dominance hierarchy and may cause loss of social knowledge, sexually selected infanticide, habitat changes among reproductive females, and changes in offspring sex ratio. A common feature of many of the reported mechanisms is that they ultimately depress recruitment and in some extreme cases even cause total reproductive collapse. These effects could act additively and destabilize the dynamics of populations, thus having a stronger effect on population growth rate than first anticipated. Although more experimental than observational studies reported demographic side effects, we argue that this may reflect the quite subtle mechanisms involved, which are unlikely to be detected in observational studies without rigorous monitoring regimes. We call for more detailed studies of hunted populations with marked individuals that address how the expression of these effects varies across mating systems, habitats, and with population density. Theoretical models investigating how strongly these effects influence population growth rates are also required.
Abstract
The successful mitigation of emerging wildlife diseases may involve controversial host culling. For livestock, ‘preemptive host culling’ is an accepted practice involving the removal of ...herds with known contact to infected populations. When applied to wildlife, this proactive approach comes in conflict with biodiversity conservation goals. Here, we present an alternative approach of ‘proactive hunting surveillance’ with the aim of early disease detection that simultaneously avoids undesirable population decline by targeting demographic groups with (1) a higher likelihood of being infected and (2) a lower reproductive value. We applied this harvesting principle to populations of reindeer to substantiate freedom of chronic wasting disease (CWD) infection. Proactive hunting surveillance reached 99% probability of freedom from infection (<4 reindeer infected) within 3–5 years, in comparison to ~10 years using ordinary harvest surveillance. However, implementation uncertainties linked to social issues appear challenging also with this kind of host culling.
The Open Science (OS) movement is rapidly gaining traction among policy-makers, research funders, scientific journals and individual scientists. Despite these tendencies, the pace of implementing OS ...throughout the scientific process and across the scientific community remains slow. Thus, a better understanding of the conditions that affect OS engagement, and in particular, of how practitioners learn, use, conduct and share research openly can guide those seeking to implement OS more broadly. We surveyed participants at an OS workshop hosted by the Living Norway Ecological Data Network in 2020 to learn how they perceived OS and its importance in their research, supervision and teaching. Further, we wanted to know what OS practices they had encountered in their education and what they saw as hindering or helping their engagement with OS. The survey contained scaled-response and open-ended questions, allowing for a mixed-methods approach. We obtained survey responses from 60 out of 128 workshop participants (47%). Responses indicated that usage and sharing of open data and code, as well as open access publication, were the most frequent OS practices. Only a minority of respondents reported having encountered OS in their formal education. A majority also viewed OS as less important in their teaching than in their research and supervisory roles. The respondents' suggestions for what would facilitate greater OS engagement in the future included knowledge, guidelines, and resources, but also social and structural support. These are aspects that could be strengthened by promoting explicit implementation of OS practices in higher education and by nurturing a more inclusive and equitable OS culture. We argue that incorporating OS in teaching and learning of science can yield substantial benefits to the research community, student learning, and ultimately, to the wider societal objectives of science and higher education.
Sustainable wildlife harvest is challenging due to the complexity of uncertain social-ecological systems, and diverse stakeholder perspectives of sustainability. In these systems, semi-complex ...stochastic simulation models can provide heuristics that bridge the gap between highly simplified theoretical models and highly context-specific case-studies. Such heuristics allow for more nuanced recommendations in low-knowledge contexts, and an improved understanding of model sensitivity and transferability to novel contexts. We develop semi-complex Management Strategy Evaluation (MSE) models capturing dynamics and variability in ecological processes, monitoring, decision-making, and harvest implementation, under a diverse range of contexts. Results reveal the fundamental challenges of achieving sustainability in wildlife harvest. Environmental contexts were important in determining optimal harvest parameters, but overall, evaluation contexts more strongly influenced perceived outcomes, optimal harvest parameters and optimal harvest strategies. Importantly, simple composite metrics popular in the theoretical literature (e.g. focusing on maximizing yield and population persistence only) often diverged from more holistic composite metrics that include a wider range of population and harvest objectives, and better reflect the trade-offs in real world applied contexts. While adaptive harvest strategies were most frequently preferred, particularly for more complex environmental contexts (e.g. high uncertainty or variability), our simulations map out cases where these heuristics may not hold. Despite not always being the optimal solution, overall adaptive harvest strategies resulted in the least value forgone, and are likely to give the best outcomes under future climatic variability and uncertainty. This demonstrates the potential value of heuristics for guiding applied management.
Abstract
Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and ...reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.
1. The effects of harvest on the annual and seasonal survival of willow ptarmigan Lagopus lagopus L. were tested in a large-scale harvest experiment. Management units were randomly assigned to one of ...three experimental treatments: 0%, 15% or 30% harvest. Seasonal quotas were based on the experimental treatment and estimates of bird density before the hunting season. Survival rates and hazard functions for radio-marked ptarmigan were then estimated under the competing risks of harvest and natural mortality. 2. The partially compensatory mortality hypothesis was supported: annual survival of ptarmigan was 0·54 ± 0·08 SE under 0% harvest, 0·47 ± 0·06 under 15% harvest, and was reduced to 0·30 ± 0·05 under 30% harvest. Harvest mortality increased linearly from 0·08 ± 0·05, 0·27 ± 0·05 and 0·42 ± 0·06 from 0% to 30% harvest, whereas natural mortality was 0·38 ± 0·08, 0·25 ± 0·05 and 0·28 ± 0·06 under the same treatments. 3. Realized risk of harvest mortality was 0·08-0·12 points higher than our set harvest treatments of 0-30% because birds were exposed to risk if they moved out of protected areas. The superadditive hypothesis was supported because birds in the 30% harvest treatment had higher natural mortality during winter after the hunting season. 4. Natural mortality was mainly because of raptor predation, with two seasonal peaks in fall and spring. Natural and harvest mortality coincided during early autumn with little potential for compensation during winter months. Peak risk of harvest mortality was 5× higher than natural mortality. Low natural mortality during winter suggests that most late season harvest would be additive mortality. 5. Environmental correlates of natural mortality of ptarmigan included seasonal changes in snow cover, onset of juvenile dispersal, and periods of territorial activity. Natural mortality of ptarmigan was highest during autumn movements and nesting by gyrfalcons Falco rusticolus L. Mortality was low when gyrfalcons had departed for coastal wintering sites, and during summer when ptarmigan were attending nests and broods. 6. Our experimental results have important implications for harvest management of upland gamebirds. Seasonal quotas based on proportional harvest were effective and should be set at ≤15% of August populations for regional management plans. Under threshold harvest of a reproductive surplus, 15% harvest would be sustainable at productivity rates ≥2·5 young per pair. Impacts of winter harvest could be minimized by closing the hunting season in early November or by reducing late season quotas.
Applied ecological research is increasingly inspired by the open science movement. However, new challenges about how we define our science when biodiversity data are being shared and re‐used are not ...solved. Among these challenges is the risk associated with blurring the distinction between research that mainly seeks to explore patterns with no a‐priori articulated hypotheses (exploratory research), and research that explicitly tests a‐priori formulated hypotheses (confirmatory research).
A rapid screening of a random selection of the peer‐reviewed literature suggests that neither experimental protocols nor hypothesis‐testing sensu stricto are common in applied ecological research. In addition, most experiments are carried out on small spatial scales, which contrast with current global policy needs and research trends towards addressing large spatial and temporal scales. This latter trend makes it unfeasible for policy to rely mainly on insights gained from experimental research.
To solve fundamental local, regional and global societal challenges, we need both exploratory and confirmatory research. However, the fundamental roles that confirmatory research testing a‐priori hypothesis play in establishing causal relationships need to be revaluated in applied ecological research.
A clearer distinction between exploratory and confirmatory research is currently needed, and could be facilitated by allocating journal sections to different types of research; by embracing new tools offered by the open science era, such as pre‐registration of hypothesis; by establishing new systems where post‐hoc hypotheses emerging through exploration can also be registered for later testing; and by more broad adoption of causal inference methods that foster more structured testing of hypotheses about causal mechanisms from observational biodiversity data.
Synthesis and applications. To gain the full benefits of the open science era, researchers, funding bodies and journal editors should explicitly consider approaches and incentives that encourage openness about methods and approaches, as well as value the plurality of scientific approaches needed to address questions in applied ecology and conservation science.
To gain the full benefits from the open science era, researchers, funding bodies and journal editors should explicitly consider approaches and incentives that encourage openness about methods and approaches, as well as value the plurality of scientific approaches needed to address questions in applied ecology and conservation science.
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