On the Inadequacy of Species Distribution Models for Modelling the Spread of SARS-CoV-2: Response to Araújo and Naimi

The ongoing pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing significant damage to public health and economic livelihoods, and is putting significant strains on healthcare services globally. This unfolding emergency has prompted the preparation and dissemination of the article “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate” by Araújo and Naimi (2020). The authors present the results of an ensemble forecast made from a suite of species distribution models (SDMs), where they attempt to predict the suitability of the climate for the spread of SARS-CoV-2 over the coming months. They argue that climate is likely to be a primary regulator for the spread of the infection and that people in warm-temperate and cold climates are more vulnerable than those in tropical and arid climates. A central finding of their study is that the possibility of a synchronous global pandemic of SARS-CoV-2 is unlikely. Whilst we understand that the motivations behind producing such work are grounded in trying to be helpful, we demonstrate here that there are clear conceptual and methodological deficiencies with their study that render their results and conclusions invalid. What follows is a response to the Araújo and Naimi article centered around three main criticisms: 1) Given the fact that SARS-CoV-2 has a primary infection pathway of direct contact, it is in an active spreading phase, and remains largely underreported in the Global South, it represents an inappropriate system for analysis using the SDM framework. 2) Even if we were to accept that an SDM framework would be applicable here, the methodology presented in the article strays far from best-practice guidelines for the application of SDMs. 3) The dissemination strategy of the authors failed to respect the frameworks of risks adhered to in other academic disciplines pertaining to public health, resulting in erroneous but well-publicised claims with broad policy implications before any scientific oversight could be applied

An Updated Algorithm for the Generation of Neutral Landscapes by Spectral Synthesis

Background: Patterns that arise from an ecological process can be driven as much from the landscape over which the process is run as it is by some intrinsic properties of the process itself. The disentanglement of these effects is aided if it possible to run models of the process over artificial landscapes with controllable spatial properties. A number of different methods for the generation of so-called ‘neutral landscapes’ have been developed to provide just such a tool. Of these methods, a particular class that simulate fractional Brownian motion have shown particular promise. The existing methods of simulating fractional Brownian motion suffer from a number of problems however: they are often not easily generalisable to an arbitrary number of dimensions and produce outputs that can exhibit some undesirable artefacts. Methodology: We describe here an updated algorithm for the generation of neutral landscapes by fractional Brownian motion that do not display such undesirable properties. Using Monte Carlo simulation we assess the anisotropic properties of landscapes generated using the new algorithm described in this paper and compare it against a popular benchmark algorithm. Conclusion/Significance: The results show that the existing algorithm creates landscapes with values strongly correlated in the diagonal direction and that the new algorithm presented here corrects this artefact. A number of extensions of the algorithm described here are also highlighted: we describe how the algorithm can be employed to generate landscapes that display different properties in different dimensions and how they can be combined with an environmental gradient to produce landscapes that combine environmental variation at the local and macro scales

Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes

Good blood glucose control is important to people with type 1 diabetes to prevent diabetes-related complications. Too much blood glucose (hyperglycaemia) causes long-term micro-vascular complications, while a severe drop in blood glucose (hypoglycaemia) can cause life-threatening coma. Finding the right balance between quantity and type of food intake, physical activity levels and insulin dosage, is a daily challenge. Increased physical activity levels often cause changes in blood glucose due to increased glucose uptake into tissues such as muscle. To date we have limited knowledge about the minute by minute effects of exercise on blood glucose levels, in part due to the difficulty in measuring glucose and physical activity levels continuously, in a free-living environment. By using a light and user-friendly armband we can record physical activity energy expenditure on a minute-by-minute basis. Simultaneously, by using a continuous glucose monitoring system we can record glucose concentrations. In this paper, Gaussian Processes are used to model the glucose excursions in response to physical activity data, to study its effect on glycaemic contro

Daily energy expenditure, cardiorespiratory fitness and glycaemic control in people with type 1 diabetes

Objective: encouraging daily physical activity improves cardiorespiratory fitness and many cardiovascular risk factors. However, increasing physical activity often creates a challenge for people with type 1 diabetes, because of difficulties maintaining euglycemia in the face of altered food intake and adjustments to insulin doses. Our aim was to examine the triangular relationship between glucose control measured by continuous glucose monitoring system (CGMS), objective measures of total daily energy expenditure (TEE) recorded by a multi-sensory monitoring device, and cardiorespiratory fitness (CRF), in free-living subjects with type 1 diabetes.Research design and methods: twenty-three individuals (12 women) with type 1 diabetes who were free from micro- and macrovascular complications were recruited. TEE and glucose control were monitored simultaneously for up to 12 days, using a multi-sensory device and CGMS respectively. CRF was recorded as V02 max from a maximal treadmill test with the Bruce protocol.Results: subjects (mean±SD) were aged 37±11 years, with BMI = 26.5±5.1 kg.m?2, HbA1c = 7.7±1.3% (61±14 mmol/mol) and V02 max (ml.min?1.kg?1) = 39.9±8.4 (range 22.4 – 58.6). TEE (36.3±5.5 kcal.kg?1.day?1) was strongly associated with CRF(39.9±8.4 ml.min?1.kg?1) independently of sex (r = 0.63, p&lt;0.01). However, neither TEE (r = ?0.20, p = 0.36) nor CRF (r = ?0.20, p = 0.39; adjusted for sex), were significantly associated with mean glycaemia measured by CGMS.Conclusion: higher levels of energy expenditure (due to a more active lifestyle) are associated with increased cardiorespiratory fitness, but not necessarily better glycaemic control. Since increased levels of energy expenditure and good glycaemic control are both needed to protect against diabetes-related complications our data suggest they need to be achieved independently<br/

Metabolic regulation during constant moderate physical exertion in extreme conditions in Type 1 diabetes

Background Constant moderate intensity physical exertion in humid environments at altitude poses a considerable challenge to maintaining euglycaemia with Type 1 diabetes. Blood glucose concentrations and energy expenditure were continuously recorded in a person trekking at altitude in a tropical climate to quantify changes in glucose concentrations in relation to energy expenditure. Case report Blood glucose concentrations and energy expenditure were continuously monitored with a Guardian® real-time continuous glucose monitoring system (CGMS) and a SenseWear® Pro3 armband (BodyMedia Inc., USA), in a 27-year-old woman with Type 1 diabetes, during her climb up Mount Kinabalu in Borneo (c. 4095m). Comparative control data from the same person was collected in the UK (temperate climate at sea level) and Singapore (tropical climate at sea level). Maximum physical effort during the climb was &lt;60% VO 2MAX (maximal oxygen consumption). Mean daily calorific intakes were 2300kcal (UK), 2370kcal (Singapore) and 2274kcal (Mount Kinabalu), and mean daily insulin doses were 54U (UK), 40U (Singapore) and 47U (Mount Kinabalu). Despite markedly increased energy expenditure during the climb [4202kcal (Mount Kinabalu) vs. 2948kcal (UK) and 2662kcal (Singapore)], mean blood glucose was considerably higher during the trek up Mount Kinabalu [13.2±5.9mmol/l, vs. 7.9±3.8mmol/l (UK) and 8.6±4.0mmol/l (Singapore)]. Conclusion Marked unexpected hyperglycaemia occurred while trekking on Mount Kinabalu, despite similar calorie consumption and insulin doses to control conditions. Because of the risk of unexpected hyperglycaemia in these conditions, we recommend that patients embarking on similar activity holidays undertake frequent blood glucose monitoring.</p

How will the greening of the Arctic affect an important prey species and disturbance agent? Vegetation effects on arctic ground squirrels

Increases in terrestrial primary productivity across the Arctic and northern alpine ecosystems are leading to altered vegetation composition and stature. Changes in vegetation stature may affect predator-prey interactions via changes in the prey's ability to detect predators, changes in predation pressure, predator identity and predator foraging strategy. Changes in productivity and vegetation composition may also affect herbivores via effects on forage availability and quality. We investigated if height-dependent effects of forage and non-forage vegetation determine burrowing extent and activity of arctic ground squirrels (Urocitellus parryii). We collected data on burrow networks and activity of arctic ground squirrels across long-term vegetation monitoring sites in Denali National Park and Preserve, Alaska. The implications of height-specific cover of potential forage and non-forage vegetation on burrowing behaviour and habitat suitability for arctic ground squirrels were investigated using hierarchical Bayesian modelling. Increased cover of forbs was associated with more burrows and burrow systems, and higher activity of systems, for all forb heights. No other potential forage functional group was related to burrow distribution and activity. In contrast, height-dependent negative effects of non-forage vegetation were observed, with cover over 50-cm height negatively affecting the number of burrows, systems and system activity. Our results demonstrate that increases in vegetation productivity have dual, potentially counteracting effects on arctic ground squirrels via changes in forage and vegetation stature. Importantly, increases in tall-growing woody vegetation (shrubs and trees) have clear negative effects, whereas increases in forb should benefit arctic ground squirrels

Species distribution models are inappropriate for COVID-19

Species distribution models are a powerful tool for ecological inference, but not every use is biologically justified. Applying these tools to the COVID-19 pandemic is unlikely to yield new insights, and could mislead policymakers at a critical moment

A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data

Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers and for individual detections conditional on the locations of these activity centers. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway

3-D Scatter plot for cardiorespiratory fitness (CRF), mean daily total energy expenditure (TEE) and mean blood glucose (MBG) of study participants.

<p>Blue diamonds show the relationship between CRF and TEE with the solid red line showing the linear fit (CRF  =  TEE + 2.3, R²  =  0.47). Blue circle markers show the corresponding MBG for each individual.</p

Baseline characteristics of of N = 23 participants with type 1 diabetes.

<p>Data are mean ± SD for normally distributed variables and median ± IQR (range) for non-normally distributed variables. HDL cholesterol, triglyceride and time spent in vigorous activities, BG<4 and BG>11 were non-normally distributed.</p