Modelling present and future global distributions of razor clams (Bivalvia: Solenidae)

Abstract

Razor clams (Pharidae and Solenidae) are deep-burrowing bivalves that inhabit shallow waters of the tropical, subtropical, and temperate seas. Using 'maximum entropy' , a species distribution modelling software, we predicted the most suitable environments for the entire family and 14 Solen species to indicate their present and future geographic distributions. Distance to land, depth, and sea surface temperature (SST) were the most important environmental variables in training and creating the present and future distribution models both at the family and species level. In the present distribution models at the family level, the most suitable environment was where distance to land was between 0 and 100 km, a depth of 0-150 m, wave height of 5-7 m, a mean chlorophyll-a concentration about 0.7 mg m −3, and mean SST between 12 and 28 °C. Comparison with the future distribution models at the species level, found that most species were predicted to shift their distribution ranges poleward under the future environmental scenarios; i.e. species in the northern hemisphere would shift northward and southern species southward. Models also predicted that half of the species would expand their distribution ranges, 29% of species would not change their distribution, and 21% of species would shrink their distribution ranges under future climate change. Expanding geographic ranges would result in overlap in species ranges and thus greater species richness at regional scales. Model results predict that the mid-latitude peaks of species richness will move further apart, increasing the dip in richness near the equator, due to global climate change.

Marine biogeographic realms and species endemicity

“…In addition, while species distribution models can predict species ranges (e.g. refs 58 , 63 ), they may not be appropriate for determining biogeographic realms and boundaries 49 . Our preliminary analysis of modeled species ranges from AquaMaps 64 returned patterns mirroring the environmental variables used to generate the models rather than patterns of endemicity.…”

Peer Review #1 of "Global marine biodiversity in the context of achieving the Aichi Targets: ways forward and addressing data gaps (v0.1)"

“…For example, several studies have utilized open-access marine species distribution records to discover and confirm large-scale biodiversity patterns. These findings include observations that global species richness is bimodal with latitude, and that species richness decreases with depth (Chaudhary et al 2016a;Chaudhary et al 2017;Costello & Chaudhary 2017a;Saeedi et al 2017a;Saeedi & Costello 2019a;Saeedi & Costello 2019b;Saeedi et al 2017b). We conclude that there has been noticeable progress towards achieving the potential of Aichi Target 19 through establishing global databases and taxonomic resources.…”

“…The distribution and diversity of deep-sea fauna thus still remains poorly-known due to the size and remoteness of deep-sea ecosystems. For example, recent studies have shown that the global latitudinal marine species richness gradient follows a bi-modal pattern related to temperature and habitat availability (Chaudhary et al 2016a;Chaudhary et al 2017;Saeedi et al 2017a;Saeedi & Costello 2019a;Saeedi & Costello 2019b;Saeedi et al 2017b). This finding is supported by the fossil record, which shows reduced species richness at the equator in warm periods (Kiessling & Aberhan 2007).…”

“…Understanding how abiotic drivers influence species distributions can contribute to filling spatial gaps of biodiversity hotspots and endangered areas (McHenry et al 2017;Saeedi et al 2017a;Saeedi & Costello 2019a;Saeedi & Costello 2019b;Saeedi et al 2017b). Since some of these drivers can be observed by satellite imagery, it is possible to model some community assemblages in difficult-to-access locations.…”

Predicting ecological responses in a changing ocean: the effects of future climate uncertainty

“…Examples of poor awareness of the uncertainties in climate data were also found. There were cases in which basic information regarding the number of and name(s) of the ESM(s) used in the study were unreported (Joo et al 2015 ; Seebens et al 2016 ), and one article which also failed to report the emission scenario used (Saeedi et al 2016 ). Each of these studies also failed to discuss how the climate data used to represent future conditions may have affected the ecological results they present.…”