Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters
Abstract Spring mowing in May and June is one of the main causes of mortality of roe deer fawns in agricultural regions. Knowing the exact birth distribution of fawns is important to guide farmers in their pre‐mowing precautions to avoid fawn deaths. Wildlife volunteers searching fields prior to mow...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | Ecological Solutions and Evidence |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2688-8319.12225 |
| _version_ | 1797784026414579712 |
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| author | Johanna Kauffert Sophie Baur Michael Matiu Andreas König Wibke Peters Annette Menzel |
| author_facet | Johanna Kauffert Sophie Baur Michael Matiu Andreas König Wibke Peters Annette Menzel |
| author_sort | Johanna Kauffert |
| collection | DOAJ |
| description | Abstract Spring mowing in May and June is one of the main causes of mortality of roe deer fawns in agricultural regions. Knowing the exact birth distribution of fawns is important to guide farmers in their pre‐mowing precautions to avoid fawn deaths. Wildlife volunteers searching fields prior to mowing can act as citizen scientists by producing data sets of rescued fawns and their approximate age at find. However, due to weather‐dependent searches, the corresponding birth distributions can be highly skewed. We simulated virtual field data to examine the shortcomings of such data sources and introduced two algorithms for reconstructing reliable birth distribution parameters (mean and standard deviation) based on skewed samples. We found that weather‐dependent search data biased the calculated means and standard deviations by up to 14 and 5 days, respectively. However, the use of the proposed advanced algorithms (Grid Search and Machine Learning) resulted in better estimates of the sample means and standard deviations by reducing the root‐mean‐square error by 65% and 80% respectively. Furthermore, the Grid Search algorithm was able to capture birth distribution parameters based on real citizen science data in Bavaria, Germany, from 2021, which are close to the results of more systematic samples of the same year. The simulation exercise highlighted the shortcomings and discrepancies of using non‐probabilistic samples, for example on the occasion of mowing activities, to study demographic parameters compared to the true simulated distribution. Yet, the proposed algorithms can address these drawbacks and potentially turn citizen science data into an important data source for wildlife studies. This could ultimately help reduce wildlife losses during the mowing season by better knowing the distribution of births in a region. |
| first_indexed | 2024-03-13T00:34:08Z |
| format | Article |
| id | doaj.art-a749a1c48a2a42a58a56079fbd032f9e |
| institution | Directory Open Access Journal |
| issn | 2688-8319 |
| language | English |
| last_indexed | 2024-03-13T00:34:08Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecological Solutions and Evidence |
| spelling | doaj.art-a749a1c48a2a42a58a56079fbd032f9e2023-07-10T07:36:35ZengWileyEcological Solutions and Evidence2688-83192023-04-0142n/an/a10.1002/2688-8319.12225Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parametersJohanna Kauffert0Sophie Baur1Michael Matiu2Andreas König3Wibke Peters4Annette Menzel5Ecoclimatology, TUM School of Life Sciences Technical University of Munich Hans‐Carl‐von‐Carlowitz‐Platz 2 D‐85354 Freising GermanyEcoclimatology, TUM School of Life Sciences Technical University of Munich Hans‐Carl‐von‐Carlowitz‐Platz 2 D‐85354 Freising GermanyDepartment of Civil, Environmental and Mechanical Engineering University of Tento I‐38122 Trento ItalyWildlife Biology and Management Unit, TUM School of Life Sciences Technical University of Munich Hans‐Carl‐von‐Carlowitz‐Platz 2 D‐85354 Freising GermanyBavarian State Institute of Forestry (LWF) Hans‐Carl‐von‐Carlowitz‐Platz 1 D‐85354 Freising GermanyEcoclimatology, TUM School of Life Sciences Technical University of Munich Hans‐Carl‐von‐Carlowitz‐Platz 2 D‐85354 Freising GermanyAbstract Spring mowing in May and June is one of the main causes of mortality of roe deer fawns in agricultural regions. Knowing the exact birth distribution of fawns is important to guide farmers in their pre‐mowing precautions to avoid fawn deaths. Wildlife volunteers searching fields prior to mowing can act as citizen scientists by producing data sets of rescued fawns and their approximate age at find. However, due to weather‐dependent searches, the corresponding birth distributions can be highly skewed. We simulated virtual field data to examine the shortcomings of such data sources and introduced two algorithms for reconstructing reliable birth distribution parameters (mean and standard deviation) based on skewed samples. We found that weather‐dependent search data biased the calculated means and standard deviations by up to 14 and 5 days, respectively. However, the use of the proposed advanced algorithms (Grid Search and Machine Learning) resulted in better estimates of the sample means and standard deviations by reducing the root‐mean‐square error by 65% and 80% respectively. Furthermore, the Grid Search algorithm was able to capture birth distribution parameters based on real citizen science data in Bavaria, Germany, from 2021, which are close to the results of more systematic samples of the same year. The simulation exercise highlighted the shortcomings and discrepancies of using non‐probabilistic samples, for example on the occasion of mowing activities, to study demographic parameters compared to the true simulated distribution. Yet, the proposed algorithms can address these drawbacks and potentially turn citizen science data into an important data source for wildlife studies. This could ultimately help reduce wildlife losses during the mowing season by better knowing the distribution of births in a region.https://doi.org/10.1002/2688-8319.12225Capreolus capreolusmowing deathnon‐probabilistic samplingparturition timingsimulation |
| spellingShingle | Johanna Kauffert Sophie Baur Michael Matiu Andreas König Wibke Peters Annette Menzel Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters Ecological Solutions and Evidence Capreolus capreolus mowing death non‐probabilistic sampling parturition timing simulation |
| title | Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| title_full | Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| title_fullStr | Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| title_full_unstemmed | Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| title_short | Fawn birthdays: From opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| title_sort | fawn birthdays from opportunistically sampled fawn rescue data to true wildlife demographic parameters |
| topic | Capreolus capreolus mowing death non‐probabilistic sampling parturition timing simulation |
| url | https://doi.org/10.1002/2688-8319.12225 |
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