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629 results on '"Langevelde, Frank"'

6. Seeing biodiversity: perspectives in machine learning for wildlife conservation

Author

Tuia, Devis, Kellenberger, Benjamin, Beery, Sara, Costelloe, Blair R., Zuffi, Silvia, Risse, Benjamin, Mathis, Alexander, Mathis, Mackenzie W., van Langevelde, Frank, Burghardt, Tilo, Kays, Roland, Klinck, Holger, Wikelski, Martin, Couzin, Iain D., van Horn, Grant, Crofoot, Margaret C., Stewart, Charles V., and Berger-Wolf, Tanya

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation.

Published
2021
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8. Modelling honey bee colonies in winter using a Keller-Segel model with a sign-changing chemotactic coefficient

Author

Bastiaansen, Robbin, Doelman, Arjen, van Langevelde, Frank, and Rottschäfer, Vivi

Subjects
Quantitative Biology - Populations and Evolution, Mathematics - Dynamical Systems
Abstract

Thermoregulation in honey bee colonies during winter is thought to be self-organised. We added mortality of individual honey bees to an existing model of thermoregulation to account for elevated losses of bees that are reported worldwide. The aim of analysis is to obtain a better fundamental understanding of the consequences of individual mortality during winter. This model resembles the well-known Keller-Segel model. In contrast to the often studied Keller-Segel models, our model includes a chemotactic coefficient of which the sign can change as honey bees have a preferred temperature: when the local temperature is too low, they move towards higher temperatures, whereas the opposite is true for too high temperatures. Our study shows that we can distinguish two states of the colony: one in which the colony size is above a certain critical number of bees in which the bees can keep the core temperature of the colony above the threshold temperature, and one in which the core temperature drops below the critical threshold and the mortality of the bees increases dramatically, leading to a sudden death of the colony. This model behaviour may explain the globally observed honey bee colony losses during winter., Comment: 20 pages, 12 figures

Published
2019

11. Perspectives in machine learning for wildlife conservation

Author

Tuia, Devis, Kellenberger, Benjamin, Beery, Sara, Costelloe, Blair R., Zuffi, Silvia, Risse, Benjamin, Mathis, Alexander, Mathis, Mackenzie W., van Langevelde, Frank, Burghardt, Tilo, Kays, Roland, Klinck, Holger, Wikelski, Martin, Couzin, Iain D., van Horn, Grant, Crofoot, Margaret C., Stewart, Charles V., and Berger-Wolf, Tanya

Published
2022
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13. Africa's overlooked top predator: Towards a better understanding of martial eagle feeding ecology in the Maasai Mara, Kenya.

Author

Hatfield, Richard Stratton, Davis, Allison G., Buij, Ralph, Cox, John J., Kapila, Shiv, Parmuntoro, Lemein, Thomsett, Simon, Virani, Munir Z., Njoroge, Peter, and van Langevelde, Frank

Subjects
TOP predators, PREDATION, SUPERVISED learning, MACHINE learning, EAGLES, GAME & game-birds
Abstract

Raptors exert top‐down influences on ecosystems via their effects on prey population dynamics and community composition. Most raptors are sympatric with other predators, thus complicating our understanding of their relative influence in these systems. Estimates of kill rates and prey biomass recycling have been used as predation metrics that allow quantitative comparison among species and assessment of the relative role of single species within complex food webs. Few studies have produced findings of kill rates or prey biomass recycling for raptors. We used a supervised machine learning algorithm to behaviourally classify high resolution accelerometer informed GPS locations of tagged adult non‐breeding martial eagles Polemaetus bellicosus in the Maasai Mara region of Kenya to estimate kill rates and prey biomass recycling. Eagle locations classified as feeding were clustered using distance and time thresholds to identify kills and calculate kill rates. Identified kill sites were quickly ground‐truthed to confirm kills and identify prey species. We estimated kill rates for martial eagles at 0.59 kills day‐1 for males and 0.38 kills day‐1 for females, and we estimated biomass recycling per ground‐truthed kill at 1796 g for males and 3860 g for females. From our sample of identified ground‐truthed kills, 'gamebirds' was the most frequently recorded prey category for male eagles and 'small ungulates' was the most frequently recorded prey category for female eagles. These results position martial eagles close to sympatric mammalian top predators in trophic pyramids and provide evidence for their classification as a top predator. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Population recovery and occurrence of the endemic Rhine sculpin (Cottus rhenanus)

Author

Lemmers Pim, Groen Mark, Crombaghs Ben H.J.M., Gubbels Rob E.M.B., de Krom Thomas, van Langevelde Frank, van der Velde Gerard, and Leuven Rob S.E.W.

Subjects
co-occurring species, cottidae, river geul, river restoration, water quality, Aquaculture. Fisheries. Angling, SH1-691
Abstract

The Rhine sculpin (Cottus rhenanus) is a benthic rheophilic fish species that is endemic to tributaries of the rivers Rhine and Meuse in North-western Europe. Little is known about its occurrence and individuals density in relation to habitat characteristics. A core population of C. rhenanus occurs in the River Geul in the Netherlands. Since the late 19th century, this river was heavily polluted by communal and industrial wastewater, causing a strong population decline. As the core population of C. rhenanus is recovering, the status, distribution, and habitat use could be studied to facilitate recovery in other locations. Cottus rhenanus density of individuals significantly increased over the period 2005–2015 and it became one of the most abundant fish species in assemblages. Negative relationships were observed between C. rhenanus densities and a high abundance of boulders (>200 mm), large structures such as woody debris, and water depth. The population increase and recolonization of C. rhenanus coincided with water quality improvement, which suggests that this fish species can be used to assess small streams ecosystem integrity. The recent range expansion of the Ponto-Caspian round goby (Neogobius melanostomus) poses a high risk of negative effects on C. rhenanus populations via food and shelter competition. Further water quality improvement, habitat conservation, and prevention of the spread of invasive gobies could favour C. rhenanus populations within their natural range.

Published
2023
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23. Effects of Grazing and Browsing on Tropical Savanna Vegetation

Author

van Langevelde, Frank, van de Vijver, Claudius A. D. M., Prins, Herbert H. T., Groen, Thomas A., Canadell, Josep G., Series Editor, Díaz, Sandra, Series Editor, Heldmaier, Gerhard, Series Editor, Jackson, Robert B., Series Editor, Levia, Delphis F., Series Editor, Schulze, Ernst-Detlef, Series Editor, Sommer, Ulrich, Series Editor, Wardle, David A., Series Editor, Gordon, Iain J., editor, and Prins, Herbert H. T., editor

Published
2019
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26. AusTraits, a curated plant trait database for the Australian flora

Author

Falster, Daniel, Gallagher, Rachael, Wenk, Elizabeth H., Wright, Ian J., Indiarto, Dony, Andrew, Samuel C., Baxter, Caitlan, Lawson, James, Allen, Stuart, Fuchs, Anne, Monro, Anna, Kar, Fonti, Adams, Mark A., Ahrens, Collin W., Alfonzetti, Matthew, Angevin, Tara, Apgaua, Deborah M. G., Arndt, Stefan, Atkin, Owen K., Atkinson, Joe, Auld, Tony, Baker, Andrew, von Balthazar, Maria, Bean, Anthony, Blackman, Chris J., Bloomfield, Keith, Bowman, David M. J. S., Bragg, Jason, Brodribb, Timothy J., Buckton, Genevieve, Burrows, Geoff, Caldwell, Elizabeth, Camac, James, Carpenter, Raymond, Catford, Jane A., Cawthray, Gregory R., Cernusak, Lucas A., Chandler, Gregory, Chapman, Alex R., Cheal, David, Cheesman, Alexander W., Chen, Si-Chong, Choat, Brendan, Clinton, Brook, Clode, Peta L., Coleman, Helen, Cornwell, William K., Cosgrove, Meredith, Crisp, Michael, Cross, Erika, Crous, Kristine Y., Cunningham, Saul, Curran, Timothy, Curtis, Ellen, Daws, Matthew I., DeGabriel, Jane L., Denton, Matthew D., Dong, Ning, Du, Pengzhen, Duan, Honglang, Duncan, David H., Duncan, Richard P., Duretto, Marco, Dwyer, John M., Edwards, Cheryl, Esperon-Rodriguez, Manuel, Evans, John R., Everingham, Susan E., Farrell, Claire, Firn, Jennifer, Fonseca, Carlos Roberto, French, Ben J., Frood, Doug, Funk, Jennifer L., Geange, Sonya R., Ghannoum, Oula, Gleason, Sean M., Gosper, Carl R., Gray, Emma, Groom, Philip K., Grootemaat, Saskia, Gross, Caroline, Guerin, Greg, Guja, Lydia, Hahs, Amy K., Harrison, Matthew Tom, Hayes, Patrick E., Henery, Martin, Hochuli, Dieter, Howell, Jocelyn, Huang, Guomin, Hughes, Lesley, Huisman, John, Ilic, Jugoslav, Jagdish, Ashika, Jin, Daniel, Jordan, Gregory, Jurado, Enrique, Kanowski, John, Kasel, Sabine, Kellermann, Jürgen, Kenny, Belinda, Kohout, Michele, Kooyman, Robert M., Kotowska, Martyna M., Lai, Hao Ran, Laliberté, Etienne, Lambers, Hans, Lamont, Byron B., Lanfear, Robert, van Langevelde, Frank, Laughlin, Daniel C., Laugier-Kitchener, Bree-Anne, Laurance, Susan, Lehmann, Caroline E. R., Leigh, Andrea, Leishman, Michelle R., Lenz, Tanja, Lepschi, Brendan, Lewis, James D., Lim, Felix, Liu, Udayangani, Lord, Janice, Lusk, Christopher H., Macinnis-Ng, Cate, McPherson, Hannah, Magallón, Susana, Manea, Anthony, López-Martinez, Andrea, Mayfield, Margaret, McCarthy, James K., Meers, Trevor, van der Merwe, Marlien, Metcalfe, Daniel J., Milberg, Per, Mokany, Karel, Moles, Angela T., Moore, Ben D., Moore, Nicholas, Morgan, John W., Morris, William, Muir, Annette, Munroe, Samantha, Nicholson, Áine, Nicolle, Dean, Nicotra, Adrienne B., Niinemets, Ülo, North, Tom, O’Reilly-Nugent, Andrew, O’Sullivan, Odhran S., Oberle, Brad, Onoda, Yusuke, Ooi, Mark K. J., Osborne, Colin P., Paczkowska, Grazyna, Pekin, Burak, Guilherme Pereira, Caio, Pickering, Catherine, Pickup, Melinda, Pollock, Laura J., Poot, Pieter, Powell, Jeff R., Power, Sally A., Prentice, Iain Colin, Prior, Lynda, Prober, Suzanne M., Read, Jennifer, Reynolds, Victoria, Richards, Anna E., Richardson, Ben, Roderick, Michael L., Rosell, Julieta A., Rossetto, Maurizio, Rye, Barbara, Rymer, Paul D., Sams, Michael A., Sanson, Gordon, Sauquet, Hervé, Schmidt, Susanne, Schönenberger, Jürg, Schulze, Ernst-Detlef, Sendall, Kerrie, Sinclair, Steve, Smith, Benjamin, Smith, Renee, Soper, Fiona, Sparrow, Ben, Standish, Rachel J., Staples, Timothy L., Stephens, Ruby, Szota, Christopher, Taseski, Guy, Tasker, Elizabeth, Thomas, Freya, Tissue, David T., Tjoelker, Mark G., Tng, David Yue Phin, de Tombeur, Félix, Tomlinson, Kyle, Turner, Neil C., Veneklaas, Erik J., Venn, Susanna, Vesk, Peter, Vlasveld, Carolyn, Vorontsova, Maria S., Warren, Charles A., Warwick, Nigel, Weerasinghe, Lasantha K., Wells, Jessie, Westoby, Mark, White, Matthew, Williams, Nicholas S. G., Wills, Jarrah, Wilson, Peter G., Yates, Colin, Zanne, Amy E., Zemunik, Graham, and Ziemińska, Kasia

Published
2021
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33. Diet-specific responses of skull traits to aridity gradients in bovids and cervids.

Author

Quibod, Ma. Niña Regina M, Gélin, Uriel, Langevelde, Frank van, and Tomlinson, Kyle W

Subjects
BOVIDAE, SKULL, HERBIVORES, SKULL morphology, PHYLOGENETIC models, INGESTION
Abstract

Feeding efficiency of mammalian herbivores is associated with skull traits known to differ between feeding guilds. As climate is known to affect the architecture and functional traits of plants, skull morphology may change as plant traits change along climate gradients. We tested whether skull traits of more than 70% of the extant bovid and cervid species are related to aridity, and whether these relationships differ across feeding guilds (browser, grazer, mixed feeder). We used muzzle width to represent food ingestion and masseteric fossa length to represent oral processing. For the models without phylogeny, both muzzle width and masseteric fossa length increased towards wetter climates for grazers, but not for mixed feeders or browsers. These patterns suggest that grazer diets may change more dramatically across aridity gradients than the other two groups, possibly linked to changes in both the architecture and toughness of grasses. In models accounting for phylogeny, no changes in either trait across aridity gradient were found, which appeared to be due to subfamily differentiation along the aridity gradient. Our results suggest that deeper insights into the evolution of herbivore skulls may be achieved by using quantified diet data to directly test plant trait effects on skull morphology. [ABSTRACT FROM AUTHOR]

Published
2023
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34. African endemic stingless bees as an efficient alternative pollinator to honey bees in greenhouse cucumber (Cucumis sativus L).

Author

Kiatoko, Nkoba, Pozo, Maria I., Van Oystaeyen, Annette, Musonye, Maurice, Kika, Junior, Wäckers, Felix, van Langevelde, Frank, Hundt, Baerbel, and Jaramillo, Juliana

Abstract

The current honey bee decline necessitates the use of alternative native pollinators to ensure global food security. Here, we compared the pollination behaviour and efficiency of the African honey bee (Apis mellifera) and six African endemic Meliponini (Meliponula bocandei, Dactylurina schmidti, Meliponula lendliana, Hypotrigona gribodoi, Meliponula ferruginea and Meliponula togoensis) in a greenhouse with the non-parthenocarpic cucumber variety Super Marketer. Honey bees and D. schmidti started foraging on introduction in the greenhouse, while M. lendliana and M. togoensis showed the longest delay. In most species, foragers collected nectar and pollen, excepting M. bocandei that specialized in nectar collection, and H. gribodoi and M. togoensis, specialized in pollen collection. African honey bees visited flowers the shortest, while H. gribodoi and D. schmidti had a 2-fold probing time, on average. Most stingless bees species had a lower hive activity with fewer foragers encountered per hour than for honey bees, except D. schmidti. M. bocandei, M. ferruginea, A. mellifera scutellata and H. gribodoi, yielded a seed germination percentage of around 90%. M. lendliana, M. togoensis and D. schmidti yielded a much lower seed germination percentage around 30%, which indicates that the quality of pollination was remarkably lower by using these three species. The highest sugar content was recorded in fruits from flowers pollinated by M. bocandei, African honey bees, D. schimdti or M. togoensis with the same solid content as the gold standard method, i.e., hand cross-pollination. We found that M. bocandei was the most efficient cucumber pollinator of all species tested: because pollination by this species yielded the largest and heaviest fruits and the highest seed numbers. [ABSTRACT FROM AUTHOR]

Published
2023
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