Regularities in species’ niches reveal the world’s climate regions

Abstract

Climate regions form the basis of many ecological, evolutionary and conservation studies. However, our understanding of climate regions is limited to how they shape vegetation: They do not account for the distribution of animals. Here we develop a network-based framework to identify important climates worldwide based on regularities in realized niches of about 26,000 tetrapods. We show that high-energy climates, including deserts, tropical savannas, and steppes, are consistent across animal- and plant-derived classifications, indicating similar underlying climatic determinants. Conversely, temperate climates differ across all groups, suggesting that these climates allow for idiosyncratic adaptations. Finally, we show how the integration of niche classifications with geographical information enables the detection of climatic transition zones and the signal of geographic and historical processes. Our results identify the climates shaping the distribution of tetrapods and call for caution when using general climate classifications to study the ecology, evolution, or conservation of specific taxa.

PISA data clusters reveal student and school inequality that affects results

“…This type of data where different groups (here countries) are distributed differently in a possibly multidimensional space can be difficult to interpret based on simple correlations, and the large-scale patterns in the data can be difficult to find. A growing body of research is therefore developing, mainly in the field of theoretical ecology [ 24 – 28 ], to employ methods from network theory to find these patterns by, for example, representing species distributions in geographic space [ 24 – 26 ] or in a space of climatic variables [ 27 ] as networks in order to study their structure. We here apply this framework to the PISA data to identify clusters of countries that are connected together due to their students performing similarly in PISA while at the same time having a similar socio-economic background, meaning that their students are distributed similarly in the ESCS-Points space.…”

A natural history of networks: Modeling higher-order interactions in geohistorical data

“…Although high-precision chronological studies have improved our understanding of past biotic crises (Smith et al 2018), compilations of individual geohistorical records are central for studies at larger spatiotemporal scales. Fossil occurrences of the benthic marine invertebrates in the Paleobiology Database (PaleoDB)(Peters and McClennen 2016) have become the benchmark data for network-based research on macroevolution, macroecology, and biogeography (Rojas et al 2017, 2021; Kocsis et al 2018; Muscente et al 2018). In most cases, PaleoDB collections have geographic information and are assigned to a geological stage, enabling the modeling of temporal constraints.…”

“…Here we describe how to use varying Markov time models through the Map Equation framework to overcome some of the limitations of the standard models currently used in paleobiology research. This case study is based on a standard bipartite network relevant for Devonian biogeography, but the approach can be applied directly to higher-order networks (Calatayud et al 2021). We use varying Markov time models to re-examine the bioregionalization of the Middle Devonian Brachiopods from the Old-World Realm (Penn-Clarke and Harper 2020).…”

Cambio climático y variabilidad espacio-temporal (serie 1980-2020) de la temperatura en Tolima, Colombia

“…A nivel global, los diferentes estratos climáticos imponen restricciones generalizadas que dan forma a las reservas de especies adaptadas a climas particulares, por ende, se puede decir que las condiciones abióticas son determinantes para la distribución y diversidad de las especies del planeta (Calatayud et al, 2021). Por lo tanto, el clima rige la base de la vida en la Tierra y puede cambiar a lo largo del tiempo.…”