Examining macroecological patterns in mammals: space use, diet and energetics

Investigating large-scale patterns in ecology, biogeography and evolution is important to aid our knowledge of species diversity. With the current natural and anthropogenic environmental changes, it is necessary to gather information that can be used for developing models of global ecosystems to assist with conservation. To achieve this, we need to establish basic ecological theories and re-examine older theories to ensure that our current understanding—which is often based on small datasets consisting of a couple of individuals or species—is applicable when expanded across communities, populations and species. The aim of this thesis was to examine the driving influences behind macroecological patterns in mammals, including spatial behaviour and foraging ecology. The investigation of spatial behaviour and foraging ecology will provide useful information on the area required by species, trophic interactions and community structure. More specifically, I was interested in how behavioural changes that have occurred following the colonisation of the marine environment has influenced patterns in home range size, predator-prey relationships and trophic level position. Using published and empirical data and comparative methodologies, I examine the effect of body size, diet, energetics and environment upon home range size, predator-prey relationships and trophic position across mammals. I identify that body mass has been the key factor driving of home range size, prey size, energetics and trophic position in mammals, explaining between 46 and 85% of the variance. However, whether a species lives within the marine or terrestrial environment has also influenced macroecological patterns, with marine mammals having home ranges 1.2 times larger, sitting 1.3 trophic levels higher and have evolved two distinct feeding strategies compared to their terrestrial counterparts. I demonstrate the ability to utilise published data to re-examine ecological theories and highlights that when developing integrative models, we need to incorporate the possibility of phylogenetic effects, a range of ecological variables, and species representative of the diversity within a group. I identify the driving influences of macroecological patterns and show how living in different environments has impacted upon mammalian spatial behaviour, foraging, food web structure and energetics

Evolutionary predictors of mammalian home range size: body mass, diet and the environment

ABSTRACT Aim Mammalian home range patterns provide information on spatial behaviour and ecological patterns, such as resource use, that is often used by conservation managers in a variety of contexts. However, there has been little research on home range patterns outside of the terrestrial environment, potentially limiting the relevance of current home range models for marine mammals, a group of particular conservation concern. To address this gap, we investigated how variation in mammalian home range size among marine and terrestrial species was related to diet, environment and body mass. Location Global. Methods We compiled data on home range size, environment (marine and terrestrial), diet and body mass from the literature and empirical studies to obtain a dataset covering 462 mammalian species. We then used phylogenetic regression analyses (to address non-independence between species) to examine the relative contribution of these factors to variation of home range size among species. Results Body size explained the majority of the difference in home range size among species (53-85%), with larger species occupying larger home ranges. The type of food exploited by species was also an important predictor of home range size (an additional 15% of variation), as was the environment, but to a much lesser degree (1.7%). Main conclusions The factors contributing to the evolution of home ranges are more complex than has been assumed. We demonstrate that diet and body size both influence home range patterns but differ in their relative contribution, and show that colonization of the marine environment has resulted in the expansion of home range size. Broad-scale models are often used to inform conservation strategies. We propose that future integrative models should incorporate the possibility of phylogenetic effects and a range of ecological variables, and that they should include species representative of the diversity within a group

Scale-dependent effects of niche specialisation:the disconnect between individual and species ranges

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MadingleyR: An R package for mechanistic ecosystem modelling

Abstract: Aim: Mechanistic general ecosystem models are used to explore fundamental ecological dynamics and to assess possible consequences of anthropogenic and natural disturbances on ecosystems. The Madingley model is a mechanistic general ecosystem model (GEM) that simulates a coherent global ecosystem, consisting of photo‐autotrophic and heterotrophic life, based on fundamental ecological processes. The C++ implementation of the Madingley model delivers fast computational performance, but it (a) limits the userbase to researchers that are familiar with the intricacies of C++ programming, (b) has limited possibility to change model settings and provide model outputs required to address specific research questions, and (c) has limited reproducibility of simulation experiments. The aim of this paper is to present an R package of the Madingley model to aid with increasing the accessibility and flexibility of the model. Innovation: The MadingleyR R package streamlines the installation procedure and supports all major operating systems. MadingleyR enables users to combine multiple consecutive simulations, making case study specific modifications to MadingleyR objects along the way. Default input files are available from the package and study‐specific inputs can be easily loaded from the R environment. MadingleyR also provides functions to plot and summarize MadingleyR outputs. We provide a comprehensive description of the MadingleyR functions and workflow. We also demonstrate the applicability of the MadingleyR package using three case studies: (a) simulating the cascading effects of the loss of mega‐herbivores on food‐web structure, (b) simulating the impacts of increased land‐use intensity on the total biomass of different feeding guilds by restricting the total vegetation biomass available for feeding and (c) simulating the impacts of an intensive land‐use scenario on a continental scale. Main conclusions: The MadingleyR package provides direct accessibility to simulations with the mechanistic ecosystem model Madingley and is flexible in its application without a loss in performance

Who is adjusting to whom?: Differences in elephant diel activity in wildlife corridors across different human-modified landscapes

The global impact of increased human activities has consequences on the conservation of wildlife. Understanding how wildlife adapts to increased human pressures with urban expansion and agricultural areas is fundamental to future conservation plans of any species. However, there is a belief that large wild free-ranging carnivores and ungulates, cannot coexist with people, limited studies have looked at wildlife movements through differing human-dominated landscapes at finer spatial scales, in Africa. This information is vital as the human population is only going to increase and the wildlife protected areas decrease. We used remote-sensor camera traps to identify the movement patterns of African elephant (Loxodonta africana) through six wildlife corridors in Botswana. The wildlife corridors were located in two different human-dominated landscapes (agricultural/urban), with varying degrees of human impact. While we found that elephants use corridors in both landscapes, they use the urban corridors both diurnally and nocturnally in contrast to agricultural corridors which were only nocturnal. Our results provide evidence for temporal partitioning of corridor use by elephants. We identified that seasonality and landscape were important factors in determining the presence of elephants in the corridors. Our findings demonstrate that elephant diel patterns of use of the wildlife corridor differs based on the surrounding human land-uses on an hourly basis and daily basis, revealing potential adaptation and risk avoidance behaviour

COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife

Funding: Manuscript preparation was supported through: a Radcliffe Fellowship at the Radcliffe Institute for Advanced Study, Harvard University (to C.R.); the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 798091 (to M.-C.L.); and Autonomous Province of Trento ordinary funds to Fondazione Edmund Mach (to F.C.).Reduced human mobility during the pandemic will reveal critical aspects of our impact on animals, providing important guidance on how best to share space on this crowded planet.PostprintPeer reviewe

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

Behavioral responses of terrestrial mammals to COVID-19 lockdowns

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.acceptedVersio

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

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

Aim Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species. Location Worldwide. Time period 1998-2021. Major taxa studied Forty-nine terrestrial mammal species. Methods Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types. Results IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively. Main conclusions We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data