A Double machine learning trend model for citizen science data

Funding: This work was funded by The Leon Levy Foundation, The Wolf Creek Foundation and the National Science Foundation (ABI sustaining: DBI-1939187). This work used Bridges2 at Pittsburgh Supercomputing Center and Anvil at Rosen Center for Advanced Computing at Purdue University through allocation DEB200010 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603 and #2138296. Our research was also funded through the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND program, with financial support from the Academy of Finland (AKA, Univ. Turku: 326327, Univ. Helsinki: 326338), the Swedish Research Council (Formas, SLU: 2018-02440, Lund Univ.: 2018-02441), the Research Council of Norway (Forskningsrådet, NINA: 295767) and the U.S. National Science Foundation (NSF, Cornell Univ.: ICER-1927646).1. Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process. 2. Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores. 3. To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change. 4. The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data.Peer reviewe

Selection of forest habitats by Capercaillie (Tetrao urogallus) in Polish part of the Western Carpathians

The objective of this paper is to analyse the forest habitat selection by Capercaillie in Polish part of the Western Carpathians. The study was carried out in the Ujsoły Forest District located in the Beskid Żywiecki Mountains in years 2002−2004. Habitats were investigated with regard to the forest types, age classes of the main tree species, canopy closure and altitude. We established 28 linear transects of the total length of 221 km. Data regarding Capercaillie occurrence (n=141) i.e., bird observations, tracks, feathers and droppings were collected twice during spring, summer, autumn and winter. Average index of Capercaillie density based only on birds seen amounted to 0.055/km of the transect and was the highest during spring (0,090/km). Availability and usage by Capercaillie, as well as preference index were calculated for distinguished habitat groups. As shown by Bailey's test, mountain mixed coniferous forest and mountain coniferous forest sites were preferred by Caperacillie, while mountain deciduous forest was avoided. The birds preferred spruce and beech stands of age ranging from 80 to 120 years. Moreover, stands with open and broken canopy and habitats located 800−1200 m a.s.l. turned to be highly preferred by Capercaillie. The obtained results were analyzed in relation to potential food resources, predator pressure and human disturbance. The following forest management measures in mountain refuges of Capercaillie were suggested: (1) maintaining open or broken canopy closure of stands, (2) policyclic timber harvesting system with a long period of regeneration, (3) patchy distribution of understory vegetation with cover below 50% of area, (4) maintaining in the ground flora at least 30% cover of bilberry, and (5) leaving seed trees and old−growth forest patches in clear−cuts as well as promotion of natural regeneration. This activities together with control of predation and reduction of human pressure allows to protect Capercaillie population in the Beskid Żywiecki Mountains

Egg weight effects on hatchability of african ostrich

Ocenę wylęgowości przeprowadzono na 133 jajach strusich, które w zależności od masy podzielono na 3 grupy: I – jaja o masie od 1300 do 1450 g, II – jaja o masie od 1451 do 1600 g, III – jaja o masie od 1601 do 1750 g. Największy odsetek zarodków zamarłych (30%) stwierdzono w grupie I, natomiast w pozostałych dwóch wskaźnik ten był mniejszy, odpowiednio o 22,6 i 12,4%. Najwyższe wskaźniki wylęgowości zarówno z jaj nałożonych, jak i zapłodnionych stwierdzono w grupie II, odpowiednio 66,6 i 88,8%. Optimum masy strusich jaj wylęgowych winno mieścić się w przedziale 1451–1600 g.Hatchability evaluation was performed on 133 ostrich eggs which, according to their weight, were divided into 3 groups: group I – with eggs weighing 1300 to 1450 g, group II – with eggs weighing 1451 to 1600 g, and group III – with eggs weighing 1601 to 1750 g. The largest percentage of dead embryos (30%) was found in group I, whereas this rate in two other ones was smaller by 22.6 and 12.4%, respectively. Largest hatchability rates both from set and ferilised eggs were found in group II, i.e. 66.6 and 88.8%, respectively. The optimum of ostrich egg weight should be within the range of 1451–1600 g

A fourth-order cartesian grid embedded boundary method for poisson's equation

In this paper, we present a fourth-order algorithm to solve Poisson's equation in two and three dimensions. We use a Cartesian grid, embedded boundary method to resolve complex boundaries. We use a weighted least squares algorithm to solve for our stencils. We use convergence tests to demonstrate accuracy and we show the eigenvalues of the operator to demonstrate stability. We compare accuracy and performance with an established second-order algorithm. We also discuss in depth strategies for retaining higher-order accuracy in the presence of nonsmooth geometries