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Abstract Planktonic foraminifera are key contributors to the oceanic carbon cycle. In pelagic environments, carbonate production by planktonic biomineralizers regulates ocean-atmosphere carbon dioxide exchange and exports surface carbon to the deep ocean. Here we compare shell traits of three planktonic foraminifera species from the central Atlantic with a suite of environmental parameters to discern the factors underlying their variations. Our analysis revealed that calcification in foraminifera is associated with seawater density and depends on species habitat depth, whereas foraminifera bulk shell densities may serve as a seawater density proxy, regardless of species. We observe that their shell weights increased with habitat depth, enabling the living cells to adjust their overall density to match that of the surrounding liquid. This suggests that calcification in nonmotile organisms has a buoyancy regulatory function and will respond to the anthropogenically driven reductions in ocean density (oceanic rarefication), with potential consequences for the carbon cycle. 

Island mammals have influenced ecological and evolutionary theory since Darwin, and many of them provide textbook examples of the dramatic morphological evolution that often occurs in island communities. However, patterns of evolution in the climatic niches of island mammals have yet to be fully explored. Several hypotheses explaining niche divergence in island species have been introduced, linking niche evolution to increased competition among closely related or sympatric species, and as a by‐product of morphological evolution or geographical patterns. Here, we evaluate these hypotheses using closely related species pairs (sister taxa). We characterized the climatic niches of island endemic species and their closest relatives and calculated two metrics of niche divergence between the species (niche overlap and centroid distance). We compared these metrics between island endemics that have island‐dwelling sister taxa and those that have mainland‐dwelling sister taxa. We then related the degree of niche divergence to phylogenetic relatedness between the sister taxa, sympatry, morphological trait differences and island characteristics (isolation, size, age). Overall, despite significant niche divergence across species pairs, we found little evidence that competition or biotic interactions drive large‐scale climatic niche evolution in island mammals. Niche divergence in island‐endemic mammals is not driven by sympatry with their closest relatives, nor is it linked to phylogenetic relatedness. Furthermore, the phenotypic evolution of island‐endemic species does not lead to corresponding evolution in climatic niches. Instead, abiotic, geographical patterns appear to drive niche divergence in these species. Sister taxa that were more geographically isolated from each other had significantly lower niche overlaps. Island‐endemic mammals that live in montane regions likewise diverged from their closest relatives. These results suggest that competition between related species on islands may lead to niche partitioning only on local scales and that niche evolution in island‐endemic mammals may occur primarily in response to geographical patterns. 

Endemic (small-ranged) species are distributed non-randomly across the globe. Regions of high topography and stable climates have higher endemism than flat, climatically unstable regions. However, it is unclear how these environmental conditions interact with and filter mammalian traits. Here, we characterize the functional traits of highly endemic mammalian assemblages in multiple ways, testing the hypothesis that these assemblages are trait-filtered (less functionally diverse) and dominated by species with traits associated with small range sizes. Compiling trait data for more than 5000 mammal species, we calculated assemblage means and multidimensional functional metrics to evaluate the distribution of traits across each assemblage. We then related these metrics to the endemism of global World Wildlife Fund ecoregions using linear models and phylogenetic fourth-corner regression. Highly endemic mammalian assemblages had small average body masses, low fecundity, short lifespans and specialized habitats. These traits relate to the stable climate and rough topography of endemism hotspots and to mammals' ability to expand their ranges, suggesting that the environmental conditions of endemism hotspots allowed their survival. Furthermore, species living in endemism hotspots clustered near the edges of their communities’ functional spaces, indicating that abiotic trait filtering and biotic interactions act in tandem to shape these communities. 

Species distribution models (SDMs), which relate recorded observations (presences) and absences or background points to environmental characteristics, are powerful tools used to generate hypotheses about the biogeography, ecology, and conservation of species. Although many researchers have examined the effects of presence and background point distributions on model outputs, they have not systematically evaluated the effects of various methods of background point sampling on the performance of a single model algorithm across many species. Therefore, a consensus on the preferred methods of background point sampling is lacking. Here, we conducted presence-background SDMs for 20 vertebrate species in North America under a variety of background point conditions, varying the number of background points used, the size of the buffer used to constrain the background points around the occurrences, and the percentage of background points sampled within the buffer (“spatial weighting”). We evaluated the accuracy and transferability of the models using Boyce index, overlap with expert-generated range maps, and area overpredicted and underpredicted by the SDM (and AUC for comparability with other studies). SDM performance is highly dependent on the species modelled but is affected by the number and spread of background points. Models with little spatial weighting had high accuracy (overlap values), but extreme extrapolation errors and overprediction. In contrast, SDMs with high transferability (high Boyce index values and low overprediction) had moderate-to-high spatial weighting. These results emphasize the importance of both background points and evaluation metric selection in SDMs. For other, more successful metrics, using many background points with spatial weighting may be preferred for models with large extents. These results can assist researchers in selecting the background point parameters most relevant for their research question, allowing them to fine-tune their hypotheses on the distribution of species through space and time. 

Protected areas serve to preserve the remaining biodiversity on our planet. However, today, only about 14% of terrestrial lands are protected, which will not be sufficient to support the planet’s fabric of life into the future ( 1 , 2 ). Humans continue to encroach on the habitats of many plants and animals. Simultaneously, the environmental conditions within protected areas are changing because of shifting climates, pollution, and invasive species, which all fundamentally alter ecosystems globally. To effectively conserve biodiversity, researchers and policy-makers must critically reexamine both the lands being preserved and the protection strategies being used in conservation. On pages 1094 and 1101 of this issue, Allan et al. ( 3 ) and Brennan et al. ( 4 ), respectively, evaluate the preservation capacity of today’s protected areas in different but complementary ways. Allan et al. estimate the minimum land area necessary to support today’s terrestrial biodiversity, whereas Brennan et al. identify the connectedness necessary to allow wildlife to successfully adapt to global change.