MeCP2 binds to nucleosome free (linker DNA) regions and to H3K9/H3K27 methylated nucleosomes in the brain

Methyl-CpG-binding protein 2 (MeCP2) is a chromatin-binding protein that mediates transcriptional regulation, and is highly abundant in brain. The nature of its binding to reconstituted templates has been well characterized in vitro. However, its interactions with native chromatin are less understood. Here we show that MeCP2 displays a distinct distribution within fractionated chromatin from various tissues and cell types. Artificially induced global changes in DNA methylation by 3-aminobenzamide or 5-aza-2′-deoxycytidine, do not significantly affect the distribution or amount of MeCP2 in HeLa S3 or 3T3 cells. Most MeCP2 in brain is chromatin-bound and localized within highly nuclease-accessible regions. We also show that, while in most tissues and cell lines, MeCP2 forms stable complexes with nucleosome, in brain, a fraction of it is loosely bound to chromatin, likely to nucleosome-depleted regions. Finally, we provide evidence for novel associations of MeCP2 with mononucleosomes containing histone H2A.X, H3K9me2 and H3K27me3 in different chromatin fractions from brain cortex and in vitro. We postulate that the functional compartmentalization and tissue-specific distribution of MeCP2 within different chromatin types may be directed by its association with nucleosomes containing specific histone variants, and post-translational modifications

A comprehensive analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman´s rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ((Formula presented.)) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing (Formula presented.). To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal´s movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small (Formula presented.). While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an (Formula presented.) >1,000, where 30% had an (Formula presented.) <30. In this frequently encountered scenario of small (Formula presented.), AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.Fil: Noonan, Michael J.. National Zoological Park; Estados Unidos. University of Maryland; Estados UnidosFil: Tucker, Marlee A.. Senckenberg Gesellschaft Für Naturforschung; . Goethe Universitat Frankfurt; AlemaniaFil: Fleming, Christen H.. University of Maryland; Estados Unidos. National Zoological Park; Estados UnidosFil: Akre, Thomas S.. National Zoological Park; Estados UnidosFil: Alberts, Susan C.. University of Duke; Estados UnidosFil: Ali, Abdullahi H.. Hirola Conservation Programme. Garissa; KeniaFil: Altmann, Jeanne. University of Princeton; Estados UnidosFil: Antunes, Pamela Castro. Universidade Federal do Mato Grosso do Sul; BrasilFil: Belant, Jerrold L.. State University of New York; Estados UnidosFil: Beyer, Dean. Universitat Phillips; AlemaniaFil: Blaum, Niels. Universitat Potsdam; AlemaniaFil: Böhning Gaese, Katrin. Senckenberg Gesellschaft Für Naturforschung; Alemania. Goethe Universitat Frankfurt; AlemaniaFil: Cullen Jr., Laury. Instituto de Pesquisas Ecológicas; BrasilFil: de Paula, Rogerio Cunha. National Research Center For Carnivores Conservation; BrasilFil: Dekker, Jasja. Jasja Dekker Dierecologie; Países BajosFil: Drescher Lehman, Jonathan. George Mason University; Estados Unidos. National Zoological Park; Estados UnidosFil: Farwig, Nina. Michigan State University; Estados UnidosFil: Fichtel, Claudia. German Primate Center; AlemaniaFil: Fischer, Christina. Universitat Technical Zu Munich; AlemaniaFil: Ford, Adam T.. University of British Columbia; CanadáFil: Goheen, Jacob R.. University of Wyoming; Estados UnidosFil: Janssen, René. Bionet Natuuronderzoek; Países BajosFil: Jeltsch, Florian. Universitat Potsdam; AlemaniaFil: Kauffman, Matthew. University Of Wyoming; Estados UnidosFil: Kappeler, Peter M.. German Primate Center; AlemaniaFil: Koch, Flávia. German Primate Center; AlemaniaFil: LaPoint, Scott. Max Planck Institute für Ornithologie; Alemania. Columbia University; Estados UnidosFil: Markham, A. Catherine. Stony Brook University; Estados UnidosFil: Medici, Emilia Patricia. Instituto de Pesquisas Ecológicas (IPE) ; BrasilFil: Morato, Ronaldo G.. Institute For Conservation of The Neotropical Carnivores; Brasil. National Research Center For Carnivores Conservation; BrasilFil: Nathan, Ran. The Hebrew University of Jerusalem; IsraelFil: Oliveira Santos, Luiz Gustavo R.. Universidade Federal do Mato Grosso do Sul; BrasilFil: Olson, Kirk A.. Wildlife Conservation Society; Estados Unidos. National Zoological Park; Estados UnidosFil: Patterson, Bruce. Field Museum of National History; Estados UnidosFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Ramalho, Emiliano Esterci. Institute For Conservation of The Neotropical Carnivores; Brasil. Instituto de Desenvolvimento Sustentavel Mamirauá; BrasilFil: Rösner, Sascha. Michigan State University; Estados UnidosFil: Schabo, Dana G.. Michigan State University; Estados UnidosFil: Selva, Nuria. Institute of Nature Conservation of The Polish Academy of Sciences; PoloniaFil: Sergiel, Agnieszka. Institute of Nature Conservation of The Polish Academy of Sciences; PoloniaFil: Xavier da Silva, Marina. Parque Nacional do Iguaçu; BrasilFil: Spiegel, Orr. Universitat Tel Aviv; IsraelFil: Thompson, Peter. University of Maryland; Estados UnidosFil: Ullmann, Wiebke. Universitat Potsdam; AlemaniaFil: Ziḝba, Filip. Tatra National Park; PoloniaFil: Zwijacz Kozica, Tomasz. Tatra National Park; PoloniaFil: Fagan, William F.. University of Maryland; Estados UnidosFil: Mueller, Thomas. Senckenberg Gesellschaft Für Naturforschung; . Goethe Universitat Frankfurt; AlemaniaFil: Calabrese, Justin M.. National Zoological Park; Estados Unidos. University of Maryland; Estados Unido

Diurnal timing of nonmigratory movement by birds: the importance of foraging spatial scales

Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64 570 days of GPS movement data collected between 2002 and 2019 for local (non‐migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad‐scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout all 24 hrs of the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.We thank the Nature Conservancy, the Bailey Wildlife Foundation, the Bluestone Foundation, the Ocean View Foundation, Biodiversity Research Institute, the Maine Outdoor Heritage Fund, the Davis Conservation Foundation and The U.S. Department of Energy (DE‐EE0005362), and the Darwin Initiative (19-026), EDP S.A. ‘Fundação para a Biodiversidade’ and the Portuguese Foundation for Science and Technology (FCT) (DL57/2019/CP 1440/CT 0021), Enterprise St Helena (ESH), Friends of National Zoo Conservation Research Grant Program and Conservation Nation, ConocoPhillips Global Signature Program, Maryland Department of Natural Resources, Cellular Tracking Technologies and Hawk Mountain Sanctuary for providing funding and in-kind support for the GPS data used in our analyses

Indigenous Protocol and Artificial Intelligence Position Paper

This position paper on Indigenous Protocol (IP) and Artificial Intelligence (AI) is a starting place for those who want to design and create AI from an ethical position that centers Indigenous concerns. Each Indigenous community will have its own particular approach to the questions we raise in what follows. What we have written here is not a substitute for establishing and maintaining relationships of reciprocal care and support with specific Indigenous communities. Rather, this document offers a range of ideas to take into consideration when entering into conversations which prioritize Indigenous perspectives in the development of artificial intelligence. It captures multiple layers of a discussion that happened over 20 months, across 20 time zones, during two workshops, and between Indigenous people (and a few non-Indigenous folks) from diverse communities in Aotearoa, Australia, North America, and the Pacific. Indigenous ways of knowing are rooted in distinct, sovereign territories across the planet. These extremely diverse landscapes and histories have influenced different communities and their discrete cultural protocols over time. A single ‘Indigenous perspective’ does not exist, as epistemologies are motivated and shaped by the grounding of specific communities in particular territories. Historically, scholarly traditions that homogenize diverse Indigenous cultural practices have resulted in ontological and epistemological violence, and a flattening of the rich texture and variability of Indigenous thought. Our aim is to articulate a multiplicity of Indigenous knowledge systems and technological practices that can and should be brought to bear on the ‘question of AI.’ To that end, rather than being a unified statement this position paper is a collection of heterogeneous texts that range from design guidelines to scholarly essays to artworks to descriptions of technology prototypes to poetry. We feel such a somewhat multivocal and unruly format more accurately reflects the fact that this conversation is very much in an incipient stage as well as keeps the reader aware of the range of viewpoints expressed in the workshops

Kaʻina Hana ʻŌiwi a me ka Waihona ʻIke Hakuhia Pepa Kūlana

He wahi hoʻomaka kēia pepa kuana no ke Kaʻina Hana ʻŌiwi (KHʻO) a me ka Waihona ʻike Hakuhia (WʻIH) no ka poʻe e ake nei e haku a hana he WʻIK mai ke kuanaʻike kūpono e hoʻokele ʻia nei e ka manaʻo ʻŌiwi. He kiʻina hana ko kēlā a me kēia kaiāulu ʻŌiwi i nā nīnau a mākou e ui aʻe ai. ʻAʻole kēia mea a mākou i kākau ai he pani i ke kūkulu a mālama ʻana i ka pilina kākoʻo kekahi i kekahi me kekahi mau kaiāulu ʻŌiwi. Eia naʻe, hāpai aʻe kēia palapala i kekahi mau manaʻo e noʻonoʻo ai ke komo i kēia mau kamaʻilio ʻana ʻo ka hoʻomaka koho ʻana i ke kuanaʻike ʻŌiwi i ka haku ʻana he waihona ʻike hakuhia. He hoʻāʻo kēia wahi pepa kūlana e hōʻiliʻili i nā ʻano kamaʻilio like ʻole no 20 mahina, no 20 kāʻei hola, no ʻelua hālāwai hoʻonaʻauao, a ma waena hoʻi o kekahi mau poʻe ʻŌiwi (a ʻŌiwi ʻole hoʻi) no nā kaiāulu like ʻole i Aotearoa, Nū Hōlani, ʻAmelika ʻĀkau a me ka Pākīpika. ʻO ke kia nō naʻe, ʻaʻole ʻo ka hoʻolōkahi ʻana he leo. Paʻa nō ka ʻike ʻŌiwi i kekahi mau ʻāina a aupuni kikoʻī a puni ka honua. Hoʻohuli aku kēia mau ʻāina a mōʻaukala like ʻole i nā kaiāulu ʻokoʻa a me ko lākou mau kaʻina hana ʻŌiwi i ke au o ka manawa. ʻAʻohe “kuanaʻike ʻŌiwi hoʻokahi”, a hoʻomau a haku ʻia nā kālaikuhiʻike e ka hoʻokumu ʻana o kekahi mau kaiāulu kikoʻī i loko o kahi mau ʻāina. Ma mua, he hopena ulūlu o ke kālaikuhiʻike a kālaikuhikanaka ko ka loina naʻauao i hoʻāʻo e naʻi a hoʻohilimia i ka loina ʻŌiwi, a hoʻohāiki ʻia ke ʻano o ka manaʻo a kuanaʻike ʻŌiwi. ʻO ko mākou pahuhopu ke kālele ʻana i nā ʻōnaehana ʻike ʻŌiwi like ʻole a me ke ʻano o ka ʻenehana e hāpai i ka nīnau ʻo ka WʻIH. Ma muli o ia palena, a ma kahi o ka hoʻokuʻikuʻi ʻana he manaʻo lōkahi, he hōʻiliʻili kēia pepa kūlana o kēlā ʻano kēia ʻano o ka moʻokalaleo: ʻo nā manaʻo hoʻokele hakulau ʻoe,, ʻo ka ʻatikala akeakamai ʻoe, ʻo ka wehewehena o ka mana ʻenehana mua ʻoe , a ʻo ka poema ʻoe. I ko mākou manaʻo, he ʻolokeʻa kūpono maoli nā leo a kuanaʻike ʻokoʻa i ka ʻoiaʻiʻo he pae kinohi maoli nō kēia kamaʻilio ʻana, a he hōʻike i ka mea heluhelu no nā kuanaʻike i kupu mai i loko o nā hālāwai hoʻonaʻauao

Moving in the anthropocene: global reductions in terrestrial mammalian movements

Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission

Behavioral responses of terrestrial mammals to COVID-19 lockdowns

DATA AND MATERIALS AVAILABILITY : The full dataset used in the final analyses (33) and associated code (34) are available at Dryad. A subset of the spatial coordinate datasets is available at Zenodo (35). Certain datasets of spatial coordinates will be available only through requests made to the authors due to conservation and Indigenous sovereignty concerns (see table S1 for more information on data use restrictions and contact information for data requests). These sensitive data will be made available upon request to qualified researchers for research purposes, provided that the data use will not threaten the study populations, such as by distribution or publication of the coordinates or detailed maps. Some datasets, such as those overseen by government agencies, have additional legal restrictions on data sharing, and researchers may need to formally apply for data access. Collaborations with data holders are generally encouraged, and in cases where data are held by Indigenous groups or institutions from regions that are under-represented in the global science community, collaboration may be required to ensure inclusion.COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals’ 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.The Radboud Excellence Initiative, the German Federal Ministry of Education and Research, the National Science Foundation, Serbian Ministry of Education, Science and Technological Development, Dutch Research Council NWO program “Advanced Instrumentation for Wildlife Protection”, Fondation Segré, RZSS, IPE, Greensboro Science Center, Houston Zoo, Jacksonville Zoo and Gardens, Nashville Zoo, Naples Zoo, Reid Park Zoo, Miller Park, WWF, ZCOG, Zoo Miami, Zoo Miami Foundation, Beauval Nature, Greenville Zoo, Riverbanks zoo and garden, SAC Zoo, La Passarelle Conservation, Parc Animalier d’Auvergne, Disney Conservation Fund, Fresno Chaffee zoo, Play for nature, North Florida Wildlife Center, Abilene Zoo, a Liber Ero Fellowship, the Fish and Wildlife Compensation Program, Habitat Conservation Trust Foundation, Teck Coal, and the Grand Teton Association. The collection of Norwegian moose data was funded by the Norwegian Environment Agency, the German Ministry of Education and Research via the SPACES II project ORYCS, the Wyoming Game and Fish Department, Wyoming Game and Fish Commission, Bureau of Land Management, Muley Fanatic Foundation (including Southwest, Kemmerer, Upper Green, and Blue Ridge Chapters), Boone and Crockett Club, Wyoming Wildlife and Natural Resources Trust, Knobloch Family Foundation, Wyoming Animal Damage Management Board, Wyoming Governor’s Big Game License Coalition, Bowhunters of Wyoming, Wyoming Outfitters and Guides Association, Pope and Young Club, US Forest Service, US Fish and Wildlife Service, the Rocky Mountain Elk Foundation, Wyoming Wild Sheep Foundation, Wild Sheep Foundation, Wyoming Wildlife/Livestock Disease Research Partnership, the US National Science Foundation [IOS-1656642 and IOS-1656527, the Spanish Ministry of Economy, Industry and Competitiveness, and by a GRUPIN research grant from the Regional Government of Asturias, Sigrid Rausing Trust, Batubay Özkan, Barbara Watkins, NSERC Discovery Grant, the Federal Aid in Wildlife Restoration act under Pittman-Robertson project, the State University of New York, College of Environmental Science and Forestry, the Ministry of Education, Youth and Sport of the Czech Republic, the Ministry of Agriculture of the Czech Republic, Rufford Foundation, an American Society of Mammalogists African Graduate Student Research Fund, the German Science Foundation, the Israeli Science Foundation, the BSF-NSF, the Ministry of Agriculture, Forestry and Food and Slovenian Research Agency (CRP V1-1626), the Aage V. Jensen Naturfond (project: Kronvildt - viden, værdier og værktøjer), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy, National Centre for Research and Development in Poland, the Slovenian Research Agency, the David Shepherd Wildlife Foundation, Disney Conservation Fund, Whitley Fund for Nature, Acton Family Giving, Zoo Basel, Columbus, Bioparc de Doué-la-Fontaine, Zoo Dresden, Zoo Idaho, Kolmården Zoo, Korkeasaari Zoo, La Passarelle, Zoo New England, Tierpark Berlin, Tulsa Zoo, the Ministry of Environment and Tourism, Government of Mongolia, the Mongolian Academy of Sciences, the Federal Aid in Wildlife Restoration act and the Illinois Department of Natural Resources, the National Science Foundation, Parks Canada, Natural Sciences and Engineering Research Council, Alberta Environment and Parks, Rocky Mountain Elk Foundation, Safari Club International and Alberta Conservation Association, the Consejo Nacional de Ciencias y Tecnología (CONACYT) of Paraguay, the Norwegian Environment Agency and the Swedish Environmental Protection Agency, EU funded Interreg SI-HR 410 Carnivora Dinarica project, Paklenica and Plitvice Lakes National Parks, UK Wolf Conservation Trust, EURONATUR and Bernd Thies Foundation, the Messerli Foundation in Switzerland and WWF Germany, the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions, NASA Ecological Forecasting Program, the Ecotone Telemetry company, the French National Research Agency, LANDTHIRST, grant REPOS awarded by the i-Site MUSE thanks to the “Investissements d’avenir” program, the ANR Mov-It project, the USDA Hatch Act Formula Funding, the Fondation Segre and North American and European Zoos listed at http://www.giantanteater.org/, the Utah Division of Wildlife Resources, the Yellowstone Forever and the National Park Service, Missouri Department of Conservation, Federal Aid in Wildlife Restoration Grant, and State University of New York, various donors to the Botswana Predator Conservation Program, data from collared caribou in the Northwest Territories were made available through funds from the Department of Environment and Natural Resources, Government of the Northwest Territories. The European Research Council Horizon2020, the British Ecological Society, the Paul Jones Family Trust, and the Lord Kelvin Adam Smith fund, the Tanzania Wildlife Research Institute and Tanzania National Parks. The Eastern Shoshone and Northern Arapahoe Fish and Game Department and the Wyoming State Veterinary Laboratory, the Alaska Department of Fish and Game, Kodiak Brown Bear Trust, Rocky Mountain Elk Foundation, Koniag Native Corporation, Old Harbor Native Corporation, Afognak Native Corporation, Ouzinkie Native Corporation, Natives of Kodiak Native Corporation and the State University of New York, College of Environmental Science and Forestry, and the Slovenia Hunters Association and Slovenia Forest Service. F.C. was partly supported by the Resident Visiting Researcher Fellowship, IMéRA/Aix-Marseille Université, Marseille. This work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society.https://www.science.org/journal/sciencehj2023Mammal Research InstituteZoology and Entomolog

Large birds travel farther in homogeneous environments

Aim: Animal movement is an important determinant of individual survival, population dynamics and ecosystem structure and function. Nonetheless, it is still unclear how local movements are related to resource availability and the spatial arrangement of resources. Using resident bird species and migratory bird species outside the migratory period, we examined how the distribution of resources affects the movement patterns of both large terrestrial birds (e.g., raptors, bustards and hornbills) and waterbirds (e.g., cranes, storks, ducks, geese and flamingos). Location: Global. Time period: 2003–2015. Major taxa studied: Birds. Methods: We compiled GPS tracking data for 386 individuals across 36 bird species. We calculated the straight‐line distance between GPS locations of each individual at the 1‐hr and 10‐day time‐scales. For each individual and time‐scale, we calculated the median and 0.95 quantile of displacement. We used linear mixed‐effects models to examine the effect of the spatial arrangement of resources, measured as enhanced vegetation index homogeneity, on avian movements, while accounting for mean resource availability, body mass, diet, flight type, migratory status and taxonomy and spatial autocorrelation. Results: We found a significant effect of resource spatial arrangement at the 1‐hr and 10‐day time‐scales. On average, individual movements were seven times longer in environments with homogeneously distributed resources compared with areas of low resource homogeneity. Contrary to previous work, we found no significant effect of resource availability, diet, flight type, migratory status or body mass on the non‐migratory movements of birds. Main conclusions: We suggest that longer movements in homogeneous environments might reflect the need for different habitat types associated with foraging and reproduction. This highlights the importance of landscape complementarity, where habitat patches within a landscape include a range of different, yet complementary resources. As habitat homogenization increases, it might force birds to travel increasingly longer distances to meet their diverse needs.National Trust for Scotland; Penguin Foundation; The U.S. Department of Energy, Grant/Award Number: DE-EE0005362; Australian Research Council; NASA's Arctic Boreal Vulnerability Experiment (ABoVE), Grant/Award Number: NNX15AV92A; Netherlands Organization for Scientific Research, Grant/Award Number: VIDI 864.10.006; BCC; NSF Award, Grant/Award Number: ABI-1458748; U.K. Department for Energy and Climate Change; ‘Juan de la Cierva ‐ Incorporación’ postdoctoral grant; Irish Research Council, Grant/Award Number: GOIPD/2015/81 ; DECC; Goethe International Postdoctoral Programme, People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007‐2013/ under REA grant agreement no [291776]; German Aerospace Center Award, Grant/Award Number: 50JR1601; Scottish Natural Heritage; Solway Coast AONB Sustainable Development Fund; COWRIE Ltd.; Heritage Lottery Fund; Robert Bosch Stiftung; NSF Division of Biological Infrastructure Award, Grant/Award Number: 1564380; Spanish Ministry of Economy and Competitiveness, Grant/Award Number: IJCI-2014-19190; Energinet.dk; NASA Award, Grant/Award Number: NNX15AV92A; MAVA Foundation; Fundação para a Ciência e Tecnologia, Grant/Award Number: SFRH/BPD/118635/2016; National Key R&D Program of China, Grant/Award Number: 2016YFC0500406; Green Fund of the Greek Ministry of Environmen

Empirical GPS tracking data

Anonymised, empirical tracking data used to estimate home range areas based on various home range estimators

Data from: A comprehensive analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or 50.1\%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing $\hat{N}_\mathrm{area}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small $\hat{N}_\mathrm{area}$. While 72\% of the 369 empirical datasets had \textgreater1000 total observations, only 4\% had an $\hat{N}_\mathrm{area}$ \textgreater1000, where 30\% had an $\hat{N}_\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}_\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data