The Activity of Long-tailed Macaque (Macaca fascicularis) at Plantation Forest in Ogan Komering Ilir Regency, South Sumatera

Long-tailed macaque (Macaca fascicularis) is a quite adaptive animal so it could be found in a diverse range of habitat types, one of the habitats is Plantation Forest on peatlands. This study aims to obtain the information about the activity of long-tailed macaque related to habitat utilization at Plantation The operational activities in the Plantation Forest can affect the activities of long-tailed macaque, changing of food sources, and they have a potential to attack the Acacia plants, so it’s necessary to do research about the activity of long-tailed macaque related to the habitat utilization at Plantation Forest and to acknowledge the types of plants used by long-tailed macaque for food and shelter. This research is a descriptive study and the data are obtained through a focal animal sampling method in April and May 2019 at the Plantation Forest in Sungai Penyabungan District, Ogan Komering Ilir Regency. The focal animal sampling method used at 7.00-17.00 WIB with using interval of 10 minutes. Based on the study, the long-tailed macaque used the active time to feed (24.60%), rest (25.58%), move (27.92%), and social activities (21.89%) included vocalization, agonistic, and grooming. The vegetation parts consumed by the long-tailed macaque are leaves (42.30%), fruits (34.62%), and flowers (23.08%). The vegetation used as food is Acacia crassicarpa, Melaleuca cajuputi, and Melastoma malabathricum. The plant that’s most often used as food and shelter is Acacia crassicarpa

Effect of study area extent on the potential distribution of Species: A case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae).

Ecological niche models are used to quantify the relationships between known occurrence records of a given species and environmental variables at these locations. Maxent is among the most widely used algorithms for modeling species distribution and has demonstrated better performance compared to other methods. However, the extent of the study area is a critical issue in the development of presence-only species distribution models because it encompasses the region used to extract the background points employed to characterize the envi- ronments accessible to the species. Thus, this study evaluated the effect of the extension of the study area on the species distribution modeling with the Maxent algorithm and occurrence data from the invasive species Raoiella indica Hirst (Acari: Tenuipalpidae). The increase in the study area extent inflated most of the threshold- dependent and -independent metrics used to assess model performance. The selection of the study area also affected the predicted suitable areas for the species (its potential distribution). The analysis shows that models developed with smaller study areas resulted in model overfitting and an increase in false-negative predictions. The extent of the area used during model training has a strong influence on the model outputs, with significant consequences for predicting the potential distribution of invasive species and thus for the areas under risk of invasion

Human Pressures and Impacts on Shallow Seafloor Environments of the Northern Baltic Sea

ABSTRACT Unsustainable use of coastal resources and space has resulted in global degradation of marine environments. Stopping adverse development requires improved understanding of how different human activities affect nature. International treaties and national legislation have been established to stop widespread environmental deterioration, but targeted local actions are still needed. Comprehensive planning processes such as marine spatial planning (MSP) and integrated coastal zone management (ICZM), promote sustainable development of coastal regions and additionally require evaluation of human influence on the marine environment. In this thesis, I examine human pressures and impacts on the shallow seafloor environments of the northern Baltic Sea. The general aim of this work is to improve the quality of location-based human pressure and impact evaluations on marine environments. The work contributes to developing environmentally conscious coastal planning by improving knowledge and introducing new methodological solutions for pressure and impact evaluations. A great variety of spatial data has been used in this work, ranging from LiDAR point clouds to species-specific monitoring data. The analysis processes in the research utilizes and combines methodologies of scenario assessments, spatial modeling and statistical examination with a geographical approach. The results of this study display the possibilities and uncertainties of detailed remote sensing data, categorized biotope data and different modeling approaches when evaluating human pressures and impacts on shallow seafloor environments. This thesis also discusses the possibilities for utilizing open source data on benthic environments and human activities to support sustainable planning decisions. The work also reveals large-scale degradation of benthic keystone species Fucus spp. in the Finnish coastal areas using modeling and species monitoring data. The main findings of this thesis provide new geographical insights on human pressure and impact evaluations that can promote sustainable planning decisions in coastal regions. KEYWORDS: human pressure, human impact, benthic communities, Baltic Sea, maarine spatial planning, coastal planning, spatial modelingTIIVISTELMÄ Rannikkoalueiden ja rannikon resurssien kestämätön käyttö on heikentänyt meriympäristöjen tilaa maailmanlaajuisesti. Kehityksen suunnan kääntäminen edellyttää luotettavaa tietoa ihmisen toiminnasta sekä sen vaikutuksista rannikoiden luontoon. Vaikka kansainväliset sopimukset ja kansalliset lait pyrkivät osaltaan estämään luonnon tilan heikkenemistä, niiden lisäksi tarvitaan paikallisia toimia. Esimerkiksi laaja-alaiset rannikoilla tehtävät suunnitteluprosessit, kuten merialuesuunnittelu (MSP) ja rannikkoalueiden yhdennetty käyttö ja hoito (ICZM), tavoittelevat kestävää kehitystä, mutta niiden tulee pohjautua luotettavaan tutkimustietoon. Tutkin väitöskirjassani ihmistoiminnan aiheuttamia paineita ja niiden vaikutuksia pohjoisen Itämeren mataliin merenpohjaympäristöihin. Tavoitteenani on parantaa näitä ympäristöjä kuvaavien alueellisten ihmistoimintaan kytkeytyvien paine- ja vaikutusarviointien laatua ja siten tukea matalien merenpohjaympäristöjen erityispiirteet huomioivaa rannikkosuunnittelua. Käytän tutkimuksissani monipuolisia paikkatietoaineistoja LiDAR –pistepilvistä yksittäisten lajien seurantaaineistoihin. Teen monen tyyppisiä maantieteellisiä analyyseja hyödyntäen ja yhdistellen erilaisia skenaariomenetelmiä, alueellisia paikkatietomalleja ja tilastollisia menetelmiä. Tutkimukseni tulokset osoittavat yhtäältä yksityiskohtaisten kaukokartoitusaineistojen, luokiteltujen biotooppiaineistojen ja erilaisten mallinnusmenetelmien arvon keskeisinä merenpohjien tilaa käsittelevän tiedon lähteinä, mutta tuovat esille myös niiden käyttöön liittyviä epävarmuuksia. Työssä tarkastelen erityisesti, miten avoimia tietolähteitä voidaan hyödyntää rannikon kestävän käytön suunnittelussa. Käytän myös mallinnusmenetelmiä ja lajitasoista seurantatietoa osoittaakseni rakkohaurujen (Fucus spp.) taantuneen laaja-alaisesti Suomen rannikkoalueilla. Väitöskirjani keskeisenä tuloksena on, että maantieteellinen lähestymistapa ja alueellinen työskentelymenetelmä vahvistavat rannikkoalueiden kestävää käyttöä ja suunnittelua tukevaa tietopohjaa. ASIASANAT: ihmistoiminnan paine, ihmistoiminnan vaikutus, merenpohjan yhteisöt, Itämeri, merialuesuunnittelu, rannikkosuunnittelu, levinneisyysmallinnu

Correcting the effect of sampling bias in species distribution modeling – A new method in the case of a low number of presence data

International audienceSpecies distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. Methods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size. A new method developed to better take into account environmental biases in a context of data scarcity is proposed here. It is compared to other sampling bias correction methods primarily used in the literature by analyzing their absolute and relative impacts on model performances. Results showed that the number of presence sites is critical for selecting the applicable method. The method proposed was regularly placed in the first or second rank and tends to be more proficient than other methods in the context of presence site scarcity (〈100). It tends to improve results regarding environment-based performance indexes. Eventually, its parametrization, requiring background knowledge on species bio-ecology, appears to be more robust and convenient to perform than those based on geographical criteria