Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.

Summary1. The current availability of large ecological data sets and the computational capacity to handle them have fostered the testing and development of theory at broad spatial and temporal scales. Macroecology has particularly benefited from this era of big data, but tools are still required to help transforming this data into information and knowledge. 2. Here, we present 'letsR', a package for the R statistical computing environment, designed to handle and analyse macroecological data such as species' geographic distributions (polygons in shapefile format and point occurrences) and environmental variables (in raster format). The package also includes functions to obtain data on species' habitat use, description year and current as well as temporal trends in conservation status as provided by the IUCN RedList online data base. 3. 'letsR' main functionalities are based on the presence-absence matrices that can be created with the package's functions and from which other functions can be applied to generate, for example species richness rasters, geographic mid-points of species and species-and site-based attributes. 4. We exemplify the package's functionality by describing and evaluating the geographic pattern of species' description year in tailless amphibians. All data preparation and most analyses were made using the 'letsR' functions. Our example illustrates the package's capability for conducting macroecological analyses under a single computer platform, potentially helping researchers to save time and effort in this endeavour.

Abstract1. Scientific research increasingly calls for open-source software that is flexible, interactive, and expandable, while providing methodological guidance and reproducibility. Currently, many analyses in ecology are implemented with "black box" graphical user interfaces (GUIs) that lack flexibility or command-line interfaces that are infrequently used by non-specialists.2. To help remedy this situation in the context of species distribution modeling, we created Wallace, an open and modular application with a richly documented GUI with underlying R scripts that is flexible and highly interactive.3. Wallace guides users from acquiring and processing data to building models and examining predictions. Additionally, it is designed to grow via community contributions of new modules to expand functionality. All results are downloadable, along with code to reproduce the analysis. 4.Wallace provides an example of an innovative platform to increase access to cutting-edge methods and encourage plurality in science and collaboration in software development. K E Y W O R D Sbiogeography, range, reproducibility, software, spatial analysis, species distribution model

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Aim Species attributes are often used to explain diversity patterns across assemblages/communities. However, repeated species co‐occurrences can generate spatial pattern and strong statistical relationships between aggregated attributes and richness in the absence of biological information. Our aim is to increase awareness of this problem. Location North America. Methods We generated empirical species richness patterns using two data structures: (1) birds gridded from range maps and (2) tree communities from the US Forest Service's Forest Inventory and Analysis. We analysed richness using linear regression, regression trees, generalized additive models, geographically weighted regression and simultaneous autoregression, with ‘random intrinsic variables’ as predictors generated by assigning random numbers to species and calculating averages in assemblages. We then generated simulations in which species with cohesive or patchy distributions are placed with respect to the North American temperature gradient with or without a broad‐scale richness gradient. Random intrinsic variables are again used as predictors of richness. Finally, we analysed one simulated scenario with random intrinsic variables as both response and predictor variables. Results The models of bird and tree richness often explained moderate to large proportions of the variance. Regression trees, geographically weighted regression and simultaneous autoregression were very sensitive to the problem; generalized additive models were moderately affected, as was multiple regression to a lesser extent. In the virtual data, the variance explained increased with increasing species co‐occurrences, but neither range cohesion, a richness gradient nor spatial autocorrelation in predictors had major impacts on the variance explained. The problem persisted when the response variable was also a random intrinsic variable. Main conclusions Repeated species co‐occurrences can generate strong spurious relationships between richness and aggregated species attributes. It is important to realize that models utilizing assemblage variables aggregated from species‐level values, as well as maps illustrating their spatial patterns, cannot be taken at face value.

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19. We found that global connections, represented by countries' importance in the global air transportation network, is the main explanation for the growth rate of COVID-19 in different countries. Climate, geographic distance and socioeconomics did not affect this big picture analysis. Geographic distance and climate were significant barriers in the past but were surpassed by the human engine that allowed us to colonize almost every corner on Earth. Based on our global analysis, the global network of air transportation could lead to a worst-case scenario of synchronous global pandemic if board control measures in international airports were not taken and are not sustained during this pandemic. Despite all limitations of a global analysis, our results indicate that the current claims that the growth rate of COVID-19 may be lower in tropical countries should be taken very carefully, at risk to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to collapse of national health systems. This is the case of Brazil, a well-connected tropical country that presents the second highest increase rate of COVID-19 and might experience a serious case of human-induced disasters if decision makers take into consideration unsupported claims of the growth rate of COVID-19 might be lower in tropical countries. significant effect in this model (p = 0.054), with a positive coefficient (i.e. drier countries have lower growth rates), although effect size is at least two times lower than the effect of countries importance in global transportation (Table 1). Statistical coefficients were not upward biased by spatial autocorrelation.

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine, led to hundreds of thousands of deaths and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19 on its exponential phase. We found that population size and global connections, represented by countries’ importance in the global air transportation network, are the main explanations for the early growth rate of COVID-19 in different countries. Climate and socioeconomics had no significant effect in this big picture analysis. Our results indicate that the current claims that the growth rate of COVID-19 may be lower in warmer and humid countries should be taken very carefully, risking to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to the collapse of national health systems.

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