A comparison of diversity estimators applied to a database of host–parasite associations

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second-order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.     

On the reporting and review requirements of ISO 14044

The International Journal of Life Cycle Assessment -     

Leaf traits and performance vary with plant age and water availability in Artemisia californica

Background and AimsLeaf functional traits are strongly tied to growth strategies and ecological processes across species, but few efforts have linked intraspecific trait variation to performance across ontogenetic and environmental gradients. Plants are believed to shift towards more resource-conservative traits in stressful environments and as they age. However, uncertainty as to how intraspecific trait variation aligns with plant age and performance in the context of environmental variation may limit our ability to use traits to infer ecological processes at larger scales.MethodsWe measured leaf physiological and morphological traits, canopy volume and flowering effort for Artemisia californica (California sagebrush), a dominant shrub species in the coastal sage scrub community, under conditions of 50, 100 and 150 % ambient precipitation for 3 years.Key ResultsPlant age was a stronger driver of variation in traits and performance than water availability. Older plants demonstrated trait values consistent with a more conservative resource-use strategy, and trait values were less sensitive to drought. Several trait correlations were consistent across years and treatments; for example, plants with high photosynthetic rates tended to have high stomatal conductance, leaf nitrogen concentration and light-use efficiency. However, the trade-off between leaf construction and leaf nitrogen evident in older plants was absent for first-year plants. While few traits correlated with plant growth and flowering effort, we observed a positive correlation between leaf mass per area and performance in some groups of older plants.ConclusionsOverall, our results suggest that trait sensitivity to the environment is most visible during earlier stages of development, after which intraspecific trait variation and relationships may stabilize. While plant age plays a major role in intraspecific trait variation and sensitivity (and thus trait-based inferences), the direct influence of environment on growth and fecundity is just as critical to predicting plant performance in a changing environment.     

Correction to: Experimental evidence of minimal effects on octocoral hosts caused by the introduced ophiuroid Ophiothela mirabilis

Coral Reefs - A correction to this paper has been published: https://doi.org/10.1007/s00338-021-02110-0     

Fine-scale time series surveys reveal new insights into spatio-temporal trends in coral cover (2002–2018), of a coral reef on the Southern Great Barrier Reef

Reef monitoring programmes often focus on limited sites, predominantly on reef slope areas, which do not capture compositional variability across zones. This study assessed spatial and temporal changes in hard coral cover at four hierarchical spatial scales. ~ 55,000, geo-referenced photoquadrats were collected annually from 2002 to 2018 and analysed using artificial intelligence for 31 sites across reef flat and reef slope zones on Heron Reef, Southern Great Barrier Reef, Australia. Trends in hard coral cover were examined at three spatial scales: (1) “reef scale”, all data; (2) “geomorphic zone scale”—north/south reef slope, inner/outer reef flat; and (3) “site scale”—31 sites. Coral cover trajectories were also examined at: (4) “sub-site scale”—sub-division of sites into 567 sub-sites, to estimate variability in coral cover trajectories via spatial statistical modelling. At reef scale coral cover increased over time to 25.6 ± 0.4 SE % in 2018 but did not recover following disturbances caused by disease (2004–2008), cyclonic conditions (2009) or severe storms (2015) to the observed pre-disturbance level (44.0 ± 0.7 SE %) seen in 2004. At geomorphic zone scale, the reef slope had significantly higher coral cover than the reef flat. Trends of decline and increase were visible in the slope zones, and the southern slope recovered to pre-decline levels. Variable coral cover trends were visible at site scale. Furthermore, sub-site spatial modelling captured eight years of coral recovery that occurred at different times and magnitudes across the four geomorphic zones, effectively estimating variability in the trajectory of the reef’s coral community. Derived spatial predictions for the entire reef show patchy coral recovery, particularly on the southern slope, and that recovery hotspots are distributed across the reef. These findings suggest that to fully understand and interpret coral decline or recovery on a reef, more accurate assessment can be achieved by examining sites distributed within different geomorphic zones to capture variation in exposure, depth and consolidation.

     

lca_algebraic: a library bringing symbolic calculus to LCA for comprehensive sensitivity analysis

The International Journal of Life Cycle Assessment - In this paper, we present new tools to ease the analysis of the effect of variability and uncertainty on life cycle assessment (LCA) results....     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.