Denise Klinkenbuß

@article{Lattuada2020,

title = {Endemic Caspian Sea mollusks in hotspot and non-hotspot areas differentially affected by anthropogenic pressures},

author = {Matteo Lattuada and Christian Albrecht and Frank P. Wesselingh and Denise Klinkenbuß and Maxim V. Vinarski and Pavel Kijashko and Niels Raes and Thomas Wilke},

doi = {10.1016/j.jglr.2019.12.007},

issn = {0380-1330},

year  = {2020},

date = {2020-10-00},

urldate = {2020-10-00},

journal = {Journal of Great Lakes Research},

volume = {46},

number = {5},

pages = {1221--1226},

publisher = {Elsevier BV},

abstract = {The Caspian Sea is renowned for its endemic mollusk biodiversity. However, over the past decades, increasing anthropogenic pressures have caused decreases in abundances and even extinction of species. Both key pressures and endemic taxa are distributed spatially unevenly across the Caspian Sea, suggesting that ecologically different taxa such as gastropods and bivalves are also affected differentially. In addition, hotspot and non-hotspot areas for these taxa might differ quantitatively in pressure scores and qualitatively in key individual anthropogenic pressures. To test this working hypothesis, hotspot areas for endemic bivalve and gastropod species were identified using stacked species ranges. Cumulative and individual pressure scores were estimated for hotspot and non-hotspot areas of bivalves and gastropods. Differences in cumulative and individual pressure scores were tested for significance using non-parametric MANOVA and Wilcoxon rank sum tests, respectively. We identified various mollusk biodiversity hotspots across locations and depths, which are differentially affected both in terms of cumulative pressure scores and in the composition of the contributing individual pressures. Similarly, hotspot and non-hotspot areas for both bivalves and gastropods are differentially affected by anthropogenic pressures. By defining endemic hotspot areas and the respective anthropogenic pressures, this study provides an important baseline for mollusk-specific conservation strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Klinkenbuß2020,

title = {Performance of 3D Morphological Methods in the Machine Learning Assisted Classification of Closely Related Fossil Bivalve Species of the Genus Dreissena},

author = {Denise Klinkenbuß and Olivia Metz and Jessica Reichert and Torsten Hauffe and Thomas A. Neubauer and Frank P. Wesselingh and Thomas Wilke},

doi = {10.4002/040.063.0109},

issn = {0076-2997},

year  = {2020},

date = {2020-08-01},

urldate = {2020-08-01},

journal = {Malacologia},

volume = {63},

number = {1},

publisher = {Institute of Malacology},

abstract = {In recent years, 3D analyses, new indices to describe the complexity of morphological structures and sophisticated machine learning approaches have advanced morphometrical analyses to assist species determination. However, the applicability of these modern approaches to the determination of cryptic species or fossil taxa has rarely been investigated.



In this study, fossil and subfossil specimens of the four modern Dreissena species in the Caspian Sea are used to test the performance of 3D-based morphological approaches for machine learning assisted species identification. Specifically, 3D scans of the shells were used to construct 3D models for calculating “traditional” shell dimensions and “modern” shell complexity parameters. Finally, two machine learning approaches were applied to test the determination performance of shell measurements vs. shell complexity and individual vs. combined shell parameters.



The results show that (i) there is no superior machine learning approach to species determination based on shell characters, (ii) shell complexity parameters are not per se more suitable for species identification than shell dimensions, (iii) a combination of shell parameters increases determination performance and reduces their species dependence and (iv) shell characters alone do not allow precise determination of all Dreissena species studied.



These findings suggest that the most appropriate machine learning approach, the most informative shell characters and the best combination of characters need to be tested individually for different data sets. However, considering that it is difficult even for experts to distinguish Dreissena species based on shell characters, the machine learning assisted classification in the current study has performed comparatively well. Future analyses based on machine learning may therefore help experts to process large sample sizes efficiently and non-specialists to assess species level information with reasonable certainty.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}