Search

A longstanding debate exists on whether ecological phenomena should be modelled ‘top-down’ (modelling patterns that arise from mechanisms) or ‘bottom-up’ (modelling mechanisms to generate pattern). Recently, the discussion re-emerged in the context of modelling the spread of SARS-CoV-21 . Simply put, the point made by Carlson et al. was that top-down correlative models are inappropriate, whereas bottom-up epidemiological models are fine. Rather than opposing families of models, we argue that all have strengths and limitations and that judicious use of all available tools is the way forward.

As new cases of COVID-19 are being confirmed worldwide pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub-Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

The widespread observation that heat tolerance is less variable than cold tolerance (‘cold-tolerance asymmetry’) leads to the prediction that species exposed to temperatures near their thermal maxima should have reduced evolutionary potential for adapting to climate warming. However, the prediction is largely supported by species-level global studies based on single estimates of both physiological metrics per taxon. We ask if cold-tolerance asymmetry holds for Iberian lizards after accounting for intraspecific variation in critical thermal maxima (CTmax) and minima (CTmin). To do so, we quantified CTmax and CTmin for 58 populations of 15 Iberian lizard species (299 individuals). Then, we randomly selected one population from each study species (population sample = 15 CTmax and CTmin values), tested for variance homoscedasticity across species, and repeated the test for thousands of population samples as if we had undertaken the same study thousands of times, each time sampling one different population per species. The ratio of variances in CTmax to CTmin across species varied up to 16-fold depending on the populations chosen. Variance ratios show how much CTmax departs from the cross-species mean compared to CTmin, with a unitary ratio indicating equal variance of both thermal limits. Sampling one population per species was six times more likely to result in the observation of greater CTmax variance (‘heat-tolerance asymmetry’) than cold-tolerance asymmetry. The null hypothesis of equal variance was twice as likely for cases of cold-tolerance asymmetry than for the opposite scenario. Range-wide, population-level studies that quantify heat and cold tolerance of individual species are urgently needed to ascertain the global prevalence of cold-tolerance asymmetry. While broad latitudinal clines of cold tolerance have been strongly supported, heat tolerance might respond to smaller-scale climatic and habitat factors hence go unnoticed in global studies. Studies investig

Species are reportedly shifting their distributions poleward and upward in several parts of the world in response to climate change. The extent to which other factors might play a role driving these changes is still unclear. Land-cover change is a major cause of distributional changes, but it cannot be discarded that distributional dynam-ics might be at times caused by other mechanisms (e.g. dispersal, ecological drift). Using observed changes in the distribution of 82 breeding birds in Great Britain be-tween three time periods 1968–72 (t1), 1988–91 (t2) and 2007–2011 (t3), we exam-ine whether observed bird range shifts between t1-t2 and t1-t3 are best explained by climate change or land-cover change, or whether they are not distinguishable from what would be expected by chance. We found that range shifts across the rear edge of northerly distributed species in Great Britain are best explained by climate change, while shifts across the leading edge of southerly distributed species are best explained by changes in land-cover. In contrast, at the northern and southern edges of Great Britain, range dynamics could not be distinguished from that expected by chance. The latter observation could be a consequence of boundary effects limiting the direction and magnitude of range changes, stochastic demographic mechanisms neither associated with climate nor land-cover change or with complex interactions among factors. Our results reinforce the view that comprehensive assessments of climate change effects on species range shifts need to examine alternative drivers of change on equal footing and that null models can help assess whether observed patterns could have arisen by chance alone.

Understanding how species’ thermal limits have evolved across the tree of life is central to predicting species’ responses to climate change. Here, using experimentally-derived estimates of thermal tolerance limits for over 2000 terrestrial and aquatic species, we show that most of the variation in thermal tolerance can be attributed to a combination of adaptation to current climatic extremes, and the existence of evolutionary ‘attractors’ that reflect either boundaries or optima in thermal tolerance limits. Our results also reveal deep-time climate legacies in ectotherms, whereby orders that originated in cold paleoclimates have presently lower cold tolerance limits than those with warm thermal ancestry. Conversely, heat tolerance appears unrelated to climate ancestry. Cold tolerance has evolved more quickly than heat tolerance in endotherms and ectotherms. If the past tempo of evolution for upper thermal limits continues, adaptive responses in thermal limits will have limited potential to rescue the large majority of species given the unprecedented rate of contemporary climate change.

Studies have documented climate change–induced shifts in species distributions but uncertainties associated with data and methods are typically unexplored. We reviewed 240 reports of climate-related species-range shifts and classified them based on three criteria. We ask whether observed distributional shifts are compared against random expectations, whether multicausal factors are examined on equal footing, and whether studies provide sufficient documentation to enable replication. We found that only ~12.1% of studies compare distributional shifts across multiple directions, ~1.6% distinguish observed patterns from random expectations, and ~19.66% examine multicausal factors. Last, ~75.5% of studies report sufficient data and results to allow replication. We show that despite gradual improvements over time, there is scope for raising standards in data and methods within reports of climate-change induced shifts in species distribution. Accurate reporting is important because policy responses depend on them. Flawed assessments can fuel criticism and divert scarce resources for biodiversity to competing priorities.

1. Accurate quantification of biodiversity can be demanding and expensive. Although environmental DNA (eDNA) metabarcoding can facilitate biodiversity assessments through non-invasive, cost-efficient, and rapid surveys, the approach struggles to outperform traditional morphological approaches in providing reliable quantitative estimates for surveyed species (e.g., abundance and biomass)., 2. We present an integrated methodology for improving biodiversity surveys that pairs eDNA metabarcoding with morphological data, following a series of taxonomic and geographic filters. We demonstrate its power by applying it to a new spatiotemporal dataset generated on an Iberian-wide distributed aquatic mesocosm infrastructure that spans a wide biogeographic gradient., 3. By building upon the strengths that these two approaches offer, our framework improved taxonomic resolution for 30% of the taxa and enabled species’ traits (e.g., body-size) and abundance to be assigned to 85% of the taxa in hybrid datasets., 4. These results indicate that eDNA-based assessments can complement, but not always replace, conventional approaches. Integrating conventional and modern eDNA metabarcoding approaches, already available in the ecologist’s toolbox, will greatly enhance biodiversity assessments.

[Aim]: Invasive alien species (IAS) can cause profound impacts on ecosystem function and diversity, human health, well- being and livelihoods. Climate change is an important driver of biological invasions, so it is critical to develop models and climate- driven scenarios of IAS range shifts to establish preventive measures. In this study, we analyse how projected changes in the frequency and magnitude of climate extreme events could affect the spread of the six most widely distributed invasive vertebrate species in the Iberian Peninsula., [Location]: Iberian Peninsula., [Taxa]: Red avadavat (Amandava amandava), common waxbill (Estrilda astrild), monk parakeet (Myiopsitta monachus), rose- ringed parakeet (Psittacula krameri), American mink (Neovison vison) and pond slider (Trachemys scripta)., [Methods]: We followed best- practice standards for species distribution models (SDMs) regarding handling of the response and predictor variables, model building and evaluation using metrics that assess different facets of model performance. We used an ensemble approach with four modelling methods of varying complexity, including both regression- based and tree- based machine- learning algorithms. We analysed five regional models for current (1971– 2000) and future climate (2021– 2050). We used principal components analysis to assess consensus among model outputs and positively weighed predictions from well- performing models., [Results]: Selected models showed high consensus and good predictive capacity on block cross- validation areas. Generalized Linear Models and Generalized Additive Models scored highest in reliability (calibration), but Bayesian Additive Regression Trees provided the best balance between calibration and discrimination capacity. Forecasts include visible changes in environmental favourability, with losses generally outweighing the gains, but with some areas becoming more favourable for several species., [Main conclusions]: Increased frequency and/or intensity of climate extreme events associated with ongoing climate change are projected to reduce overall invasion risk for the species examined although increases in favourability should be expected locally.

Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations.