Plan of Property for Randy Bowden, Pleasant Valley Road, Cumberland, Maine, 1984

https://digitalmaine.com/cumberland_plans/1097/thumbnail.jp

Standard Boundary Survey Plan of Land on Mill Road, Cumberland, Maine, 1986

Standard Boundary Survey Plan of Land on Mill Road, Cumberland, Maine was created by Daniel T. C. LaPoint in 1986.https://digitalmaine.com/cumberland_plans/1380/thumbnail.jp

Plan of Land of Peter Greenleaf, Mill Road, Cumberland, Maine, 1983

https://digitalmaine.com/cumberland_plans/1091/thumbnail.jp

Plan of Land on Mill Road, Cumberland, Maine, 1983

Plan of Land on Mill Road, Cumberland, Maine was created by Daniel T. C. LaPoint in 1983.https://digitalmaine.com/cumberland_plans/1365/thumbnail.jp

Genes Involved in the Evolution of Herbivory by a Leaf-Mining, Drosophilid Fly

Herbivorous insects are among the most successful radiations of life. However, we know little about the processes underpinning the evolution of herbivory. We examined the evolution of herbivory in the fly, Scaptomyza flava, whose larvae are leaf miners on species of Brassicaceae, including the widely studied reference plant, Arabidopsis thaliana (Arabidopsis). Scaptomyza flava is phylogenetically nested within the paraphyletic genus Drosophila, and the whole genome sequences available for 12 species of Drosophila facilitated phylogenetic analysis and assembly of a transcriptome for S. flava. A time-calibrated phylogeny indicated that leaf mining in Scaptomyza evolved between 6 and 16 million years ago. Feeding assays showed that biosynthesis of glucosinolates, the major class of antiherbivore chemical defense compounds in mustard leaves, was upregulated by S. flava larval feeding. The presence of glucosinolates in wild-type (WT) Arabidopsis plants reduced S. flava larval weight gain and increased egg–adult development time relative to flies reared in glucosinolate knockout (GKO) plants. An analysis of gene expression differences in 5-day-old larvae reared on WT versus GKO plants showed a total of 341 transcripts that were differentially regulated by glucosinolate uptake in larval S. flava. Of these, approximately a third corresponded to homologs of Drosophila melanogaster genes associated with starvation, dietary toxin-, heat-, oxidation-, and aging-related stress. The upregulated transcripts exhibited elevated rates of protein evolution compared with unregulated transcripts. The remaining differentially regulated transcripts also contained a higher proportion of novel genes than the unregulated transcripts. Thus, the transition to herbivory in Scaptomyza appears to be coupled with the evolution of novel genes and the co-option of conserved stress-related genes.Organismic and Evolutionary Biolog

Effects of body size on estimation of mammalian area requirements.

Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied blockcross validation to quantify bias in empirical home range estimates. Area requirements of mammals 1, meaning the scaling of the relationship changedsubstantially at the upper end of the mass spectrum

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August-24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication

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

Search for neutral charmless B decays at LEP

A search for rare charmless decays of \Bd and \Bs mesons has been performed in the exclusive channels \Bd_{(\mathrm s)}\ra\eta\eta, \Bd_{(\mathrm s)}\ra\eta\pio and \Bd_{(\mathrm s)}\ra\pio\pio. The data sample consisted of three million hadronic \Zo decays collected by the L3 experiment at LEP from 1991 through 1994. No candidate event has been observed and the following upper limits at 90\% confidence level on the branching ratios have been set \begin{displaymath} \mathrm{Br}(\Bd\ra\eta\eta)<4.1\times 10^{-4},\,\, \mathrm{Br}(\Bs\ra\eta\eta)<1.5\times 10^{-3},\,\, \end{displaymath} \begin{displaymath} \mathrm{Br}(\Bd\ra\eta\pio)<2.5\times 10^{-4},\,\, \mathrm{Br}(\Bs\ra\eta\pio)<1.0\times 10^{-3},\,\, \end{displaymath} \begin{displaymath} \mathrm{Br}(\Bd\ra\pio\pio)<6.0\times 10^{-5},\,\, \mathrm{Br}(\Bs\ra\pio\pio)<2.1\times 10^{-4}. \end{displaymath} These are the first experimental limits on \Bd\ra\eta\eta and on the \Bs neutral charmless modes

Measurement of the Average Lifetime of b-Hadrons in Z Decays

We present a measurement of the average b-hadron lifetime ${\rm \tau_b}$ at the $\mathrm{e^+e^-} \,$ collider LEP. Using hadronic Z decays collected in the period from 1991 to 1994, two independent analyses have been performed. In the first one, the b-decay position is reconstructed as a secondary vertex of hadronic b-decay particles. The second analysis is an updated measurement of ${\rm \tau_b}$ using the impact parameter of leptons with high momentum and high transverse momentum. The combined result is \begin{center} ${\rm \tau_b= [ 1549 \pm 9 \, (stat) \, \pm 15 \, (syst) ] \; fs \,}$ . \end{center} In addition, we measure the average charged b-decay multiplicity ${\rm \langle n_{\rm b}} \rangle$ and the normalized average b-energy ${\rm \langle x_E \rangle_{\rm b}}$ at LEP to be \begin{center} ${\rm \langle n_{\rm b} \rangle = 4.90 \pm 0.04 \ (stat) \pm 0.11 \, (syst)}$ , \end{center} \begin{center} ${\rm \langle x_E \rangle_{\rm b} = 0.709 \pm 0.004 \, (stat + syst).}$ \end{center