The three-dimensional rarity typology proposed by Rabinowitz in 1981, based on geographic range, habitat specificity, and local abundance, is among the most widely used frameworks for describing rarity in ecological and conservation research. While this framework is descriptive and is not meant to explain the causes of rarity, recent advances in ecology may be leveraged to add explanatory power. We propose a modification of Rabinowitz’s typology to better distinguish between the dimensions of rarity and the ecological processes that drive them, and explore the conservation implications of our modified framework. We suggest replacing habitat specificity with occupancy (the proportion of occupied sites within a species’ range), yielding a modified classification based on range size, occupancy, and local abundance. Habitat specificity remains embedded in our framework, but as a driver of rarity rather than a rarity axis. Under our framework, habitat specialists are no longer considered rare if they are widespread and abundant; we argue that this modification more accurately identifies truly rare species, as habitat specialists may be common if their habitat is abundant. Finally, we draw on the functional and theoretical literature to identify the key processes and associated traits that drive each rarity axis. In this respect, we identify four processes (environmental filtering, movement, demography, and interactions), and hypothesise that range size and occupancy are primarily driven by environmental filtering and movement, whereas local abundance is more strongly influenced by demography and interactions. We further use ecological theory to hypothesise the conservation concerns associated with each rarity axis, and propose conservation measures that may be suitable for conserving different types of rare species. Our work may provide a basis for developing hypotheses about the causes of rarity of particular focal taxa or groups, and inform the development of targeted conservation strategies.

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.

The three-dimensional rarity typology proposed by Rabinowitz in 1981, based on range size, habitat specificity, and local abundance, is perhaps the most widely used framework for describing rarity in ecological and conservation research. While this framework is descriptive and does not explain the causes of rarity, recent advances in ecology may be leveraged to add explanatory power. We propose a modification of Rabinowitz’s typology to better distinguish between the dimensions of rarity and the processes that drive them and explore the conservation implications of our modified framework. We suggest replacing habitat specificity, which is arguably a cause of rarity, with occupancy (the proportion of occupied sites within a species’ range), yielding a modified classification based on range size, occupancy, and local abundance. Abundant, widespread habitat specialists are no longer considered rare; however, we argue that this modification more accurately identifies truly rare species, as habitat specialists may be common if their habitat is abundant. Finally, we draw on the functional literature to identify the key processes and associated traits that drive each rarity axis. In this respect, we identify four processes (environmental filtering, movement, demography, and interactions), and hypothesise that range size and occupancy are primarily driven by environmental filtering and movement, whereas local abundance is more strongly influenced by demography and interactions. Our work aims at providing a basis for developing hypotheses about the causes of rarity in particular taxa and identifying suitable conservation measures targeting different types of rare species.

There is a rich amount of information in co-occurrence data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. The time is now ripe to step back from the statistical developments and critically assess whether co-occurrence data really is a proxy for ecological interactions. In this paper we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or the lack of) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.