The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Light Pollution Observations in Indonesia

Light pollution is a growing concern in the world. It affects many walk of lives, including human health, the degradation of nocturnal animal habitat, and the inability of the astronomers to observe dimmer objects. We in Space Science Center of LAPAN (Indonesian National Institute of Aeronautics and Space) try to mitigate this through a coordinated observation of light pollution using Sky Quality Meter equipments which are located in several LAPAN’s stations [Agam (West Sumatra), Pontianak (West Kalimantan), Sumedang (West Java), Garut (West Java), Pasuruan (East Java), Kupang (East Nusa Tenggara), and Biak (Papua)]. The observations has been conducted since 2018 in stationary and moving modes, and the results are then sent to a central database which is located in Space Science Center in Bandung (West Java). The results showed that there are some variations of light pollution across Indonesia. In this respect most of the stations have moderate pollution as can be seen from the values of Biak, Agam, Sumedang, and Pontianak (20.0, 19.5, 19.6, and 17.7 mpsas respectively). On the other hand, the stations which are located near or in cities have high light pollution (Bandung and Pasuruan with 17.1 and 18.0 mpsas, respectively). A particular station (Garut) has low light pollution (20.6 mpsas). The data of these observations are presented in a website to be accessed by interested parties.

Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

TAMAN SATWA KOTA PONTIANAK

Animal Park is a form of ex-situ conservation institution that functions as a forum for preserving fauna outside of their natural habitat which is public. Besides having a conservation function, the Animal Park also has recreational, educational and research functions. Animal Park Planning in Pontianak City is also a form of designation and management of protected areas that are able to pay attention to the sustainability of supporting environmental functions in Pontianak City. This article aims to explain the process of designing a Wildlife Park as a recreation and tourism center regarding the introduction of native animals of West Kalimantan. The theme or design concept that is planned is the consideration of the animal's life behavior without reducing the comfort from the visitor's side. This design provides a boundary between the end zone and the animal zone, as well as the placement of a management service zone that is separate from the visitor zone but can still be connected to each animal enclosure. In the design carried out, the enclosure for the animal habitat uses a membrane structure for primate species and a wide-span structure to form an aviary enclosure for aves species.

Prevalence of antimicrobial resistance and integrons in Escherichia coli isolated from feces of dairy goats in Nong Chok, Bangkok, Thailand

Antimicrobial resistance is recognized as a growing public health problem. Antimicrobial use and misuse in animal farms have boosted antimicrobial resistance among bacteria in the animal habitat and may be transferred to humans. Therefore, this study was to determine the prevalence of antimicrobial resistance, integrons and their association in Escherichia coli isolated from dairy goats in Nong Chok, Bangkok. Ninety-four fecal samples from dairy goats were collected by rectal swab between April 2019 and May 2019. Of 180 E. coli isolates, 141 were resistant to at least one antimicrobial agent by disc diffusion method. The most frequent E. coli resistance was to streptomycin 65.6% (118/180), followed by tetracycline 30.0% (54/180), kanamycin 21.7% (39/180), and sulfamethoxazole/trimethoprim 21.7% (39/180). Furthermore, the percentage of multidrug resistant (MDR) E. coli was 23.9% (43/180). Thirty-nine antimicrobial resistance profiles were found in this study and the most common resistance profiles were STR 23.3% (42/180), STR-TET-SXT 10.0% (18/180) and KAN-STR 6.7% (12/180). All of the 180 E. coli isolates were detected class 1 and 2 integrons by multiplex PCR. The results revealed 22.2% (40/180) were positive for integrons including resistant isolates 92.5% (37/40) and susceptible 7.5% (3/40). Moreover, E. coli isolates resistant to streptomycin, tetracycline, enrofloxacin and sulfamethoxazole/trimethoprim were significantly associated with the presence of integrons (P < 0.05). The data of this study indicated that dairy goats in farms could be a reservoir and possible spread of resistant isolates to farmers and consumers via animals and their products.

Road visibility influences habitat selection by grizzly bears (Ursus arctos horribilis)

Anthropogenic disturbances, including roads, are known to influence animal habitat selection and mortality. In this study, we consider the role of sensory perception in understanding why and how animals respond to disturbances. Our goal was to investigate the effect of visual perception (visibility) around roads on grizzly bear (Ursus arctos horribilis Ord, 1815) habitat selection and mortality in Alberta, Canada. We used detailed topographic and vegetation data from airborne Light Detection and Ranging (lidar) to estimate visibility around roads. We modelled habitat selection as a function of road visibility and environmental variables using GPS telemetry data from 39 grizzly bears and integrated step selection analysis (iSSA). Finally, we assessed mortality risk in visible areas by comparing habitat selection between grizzly bears that died and those that survived. We found that grizzly bears were less likely to select visible areas when moving slowly or resting, but more likely to select visible areas when traveling. We found that grizzly bears that survived selected for areas farther from roads than grizzly bears that died. However, no difference in selection for visible areas was observed. An exploratory analysis showed that grizzly bear mortalities commonly occurred in visible areas. Our findings highlight the importance of sensory perception in understanding animal behaviour.

Context Dependence: A Conceptual Approach for Understanding the Habitat Relationships of Coastal Marine Fauna

Coastal habitats, such as seagrasses, mangroves, rocky and coral reefs, salt marshes, and kelp forests, sustain many key fish and invertebrate populations around the globe. Our understanding of how animals use these broadly defined habitat types is typically derived from a few well-studied regions and is often extrapolated to similar habitats elsewhere. As a result, a working understanding of their habitat importance is often based on information derived from other regions and environmental contexts. Contexts such as tidal range, rainfall, and local geomorphology may fundamentally alter animal–habitat relationships, and there is growing evidence that broadly defined habitat types such as “mangroves” or “salt marsh” may show predictable spatial and temporal variation in habitat function in relation to these environmental drivers. In the present article, we develop a framework for systematically examining contextual predictability to define the geographic transferability of animal–habitat relationships, to guide ongoing research, conservation, and management actions in these systems.

Determinants of the nursery role of seagrass meadows in the sub-tropical Gulf of Mexico: inshore-offshore connectivity for snapper and grouper

Quantifying the nursery role of habitats or locations in supporting fisheries is central to understanding population-scale animal-habitat relationships, and in guiding ecosystem-based management. We assessed the nursery role of northern Gulf of Mexico seagrass meadows for gray snapper, lane snapper, and gag recruiting to Alabama’s extensive offshore reef complex. We accomplished this using broadscale juvenile trawl surveys and geochemical tags—indicative of past habitat use—stored in the otoliths of >2200 fishes. These natural tags revealed that 47-61% of snapper and gag recruits to Alabama reefs originated in Florida panhandle seagrass nurseries. Seagrass meadows in Alabama and Mississippi were also important nurseries for snappers and gag, contributing 26-46% of recruits. Despite high juvenile snapper and gag catches along the extensive Chandeleur Islands, Louisiana, relatively few of those fishes recruited to Alabama’s reefs (<13% of total recruits, across species), although they may have recruited to populations outside our sampling domain. Beyond the applied value of these data for resource management (i.e. interstate connectivity), our findings highlight broadscale drivers of the nursery role of juvenile habitats for coastal marine populations. These factors include: (1) juvenile habitat extent (i.e. extensive Florida panhandle meadows sourced the most recruits for Alabama fisheries); (2) proximity between juvenile and adult habitats (i.e. highest unit-area contribution from Alabama-Mississippi meadows); and (3) unidirectional, alongshore migration of egressing juveniles (i.e. primarily east-to-west movement, enhancing connectivity with Florida panhandle nurseries, and dampening connectivity with Chandeleur nurseries).